TY - JOUR AB - Half a century after Lewis Wolpert's seminal conceptual advance on how cellular fates distribute in space, we provide a brief historical perspective on how the concept of positional information emerged and influenced the field of developmental biology and beyond. We focus on a modern interpretation of this concept in terms of information theory, largely centered on its application to cell specification in the early Drosophila embryo. We argue that a true physical variable (position) is encoded in local concentrations of patterning molecules, that this mapping is stochastic, and that the processes by which positions and corresponding cell fates are determined based on these concentrations need to take such stochasticity into account. With this approach, we shift the focus from biological mechanisms, molecules, genes and pathways to quantitative systems-level questions: where does positional information reside, how it is transformed and accessed during development, and what fundamental limits it is subject to? AU - Tkačik, Gašper AU - Gregor, Thomas ID - 9226 IS - 2 JF - Development TI - The many bits of positional information VL - 148 ER - TY - JOUR AB - The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks. AU - Mlynarski, Wiktor F AU - Hermundstad, Ann M. ID - 9439 JF - Nature Neuroscience SN - 1097-6256 TI - Efficient and adaptive sensory codes VL - 24 ER - TY - JOUR AB - Attachment of adhesive molecules on cell culture surfaces to restrict cell adhesion to defined areas and shapes has been vital for the progress of in vitro research. In currently existing patterning methods, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome, and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable patterning step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes and that our innovative approach has great potential for further applications in cell science. AU - Zisis, Themistoklis AU - Schwarz, Jan AU - Balles, Miriam AU - Kretschmer, Maibritt AU - Nemethova, Maria AU - Chait, Remy P AU - Hauschild, Robert AU - Lange, Janina AU - Guet, Calin C AU - Sixt, Michael K AU - Zahler, Stefan ID - 9822 IS - 30 JF - ACS Applied Materials and Interfaces SN - 19448244 TI - Sequential and switchable patterning for studying cellular processes under spatiotemporal control VL - 13 ER - TY - JOUR AB - Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using natural speech and synthetic signals. AU - Gabrielaitis, Mantas ID - 9828 JF - IEEE Transactions on Signal Processing SN - 1053-587X TI - Fast and accurate amplitude demodulation of wideband signals VL - 69 ER - TY - JOUR AB - A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics. AU - Chalk, Matthew J AU - Tkačik, Gašper AU - Marre, Olivier ID - 9362 IS - 4 JF - PLoS ONE TI - Inferring the function performed by a recurrent neural network VL - 16 ER - TY - JOUR AB - Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems. AU - Kavcic, Bor AU - Tkačik, Gašper AU - Bollenbach, Tobias ID - 8997 JF - PLOS Computational Biology KW - Modelling and Simulation KW - Genetics KW - Molecular Biology KW - Antibiotics KW - Drug interactions SN - 1553-7358 TI - Minimal biophysical model of combined antibiotic action VL - 17 ER - TY - JOUR AB - Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks (GRNs) remains a major challenge. Here, we use a well-defined synthetic GRN to study in Escherichia coli how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one GRN with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Transcriptional read-through is the main molecular mechanism that places one transcriptional unit (TU) within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual TUs, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of GRNs. AU - Nagy-Staron, Anna A AU - Tomasek, Kathrin AU - Caruso Carter, Caroline AU - Sonnleitner, Elisabeth AU - Kavcic, Bor AU - Paixão, Tiago AU - Guet, Calin C ID - 9283 JF - eLife KW - Genetics and Molecular Biology SN - 2050-084X TI - Local genetic context shapes the function of a gene regulatory network VL - 10 ER - TY - JOUR AB - Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems. AU - Mlynarski, Wiktor F AU - Hledik, Michal AU - Sokolowski, Thomas R AU - Tkačik, Gašper ID - 7553 IS - 7 JF - Neuron TI - Statistical analysis and optimality of neural systems VL - 109 ER - TY - GEN AB - Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain. AU - Nardin, Michele AU - Csicsvari, Jozsef L AU - Tkačik, Gašper AU - Savin, Cristina ID - 10077 T2 - bioRxiv TI - The structure of hippocampal CA1 interactions optimizes spatial coding across experience ER - TY - JOUR AB - Physical and biological systems often exhibit intermittent dynamics with bursts or avalanches (active states) characterized by power-law size and duration distributions. These emergent features are typical of systems at the critical point of continuous phase transitions, and have led to the hypothesis that such systems may self-organize at criticality, i.e. without any fine tuning of parameters. Since the introduction of the Bak-Tang-Wiesenfeld (BTW) model, the paradigm of self-organized criticality (SOC) has been very fruitful for the analysis of emergent collective behaviors in a number of systems, including the brain. Although considerable effort has been devoted in identifying and modeling scaling features of burst and avalanche statistics, dynamical aspects related to the temporal organization of bursts remain often poorly understood or controversial. Of crucial importance to understand the mechanisms responsible for emergent behaviors is the relationship between active and quiet periods, and the nature of the correlations. Here we investigate the dynamics of active (θ-bursts) and quiet states (δ-bursts) in brain activity during the sleep-wake cycle. We show the duality of power-law (θ, active phase) and exponential-like (δ, quiescent phase) duration distributions, typical of SOC, jointly emerge with power-law temporal correlations and anti-correlated coupling between active and quiet states. Importantly, we demonstrate that such temporal organization shares important similarities with earthquake dynamics, and propose that specific power-law correlations and coupling between active and quiet states are distinctive characteristics of a class of systems with self-organization at criticality. AU - Lombardi, Fabrizio AU - Wang, Jilin W.J.L. AU - Zhang, Xiyun AU - Ivanov, Plamen Ch ID - 8105 JF - EPJ Web of Conferences SN - 2100-014X TI - Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality VL - 230 ER - TY - JOUR AB - In plants, clathrin mediated endocytosis (CME) represents the major route for cargo internalisation from the cell surface. It has been assumed to operate in an evolutionary conserved manner as in yeast and animals. Here we report characterisation of ultrastructure, dynamics and mechanisms of plant CME as allowed by our advancement in electron microscopy and quantitative live imaging techniques. Arabidopsis CME appears to follow the constant curvature model and the bona fide CME population generates vesicles of a predominantly hexagonal-basket type; larger and with faster kinetics than in other models. Contrary to the existing paradigm, actin is dispensable for CME events at the plasma membrane but plays a unique role in collecting endocytic vesicles, sorting of internalised cargos and directional endosome movement that itself actively promote CME events. Internalized vesicles display a strongly delayed and sequential uncoating. These unique features highlight the independent evolution of the plant CME mechanism during the autonomous rise of multicellularity in eukaryotes. AU - Narasimhan, Madhumitha AU - Johnson, Alexander J AU - Prizak, Roshan AU - Kaufmann, Walter AU - Tan, Shutang AU - Casillas Perez, Barbara E AU - Friml, Jiří ID - 7490 JF - eLife TI - Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants VL - 9 ER - TY - GEN AU - Grah, Rok AU - Friedlander, Tamar ID - 9779 TI - Distribution of crosstalk values ER - TY - GEN AU - Grah, Rok AU - Friedlander, Tamar ID - 9776 TI - Supporting information ER - TY - JOUR AB - We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement. AU - Berry, Michael J. AU - Tkačik, Gašper ID - 7656 JF - Frontiers in Computational Neuroscience TI - Clustering of neural activity: A design principle for population codes VL - 14 ER - TY - JOUR AB - The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation. AU - Maoz, Ori AU - Tkačik, Gašper AU - Esteki, Mohamad Saleh AU - Kiani, Roozbeh AU - Schneidman, Elad ID - 8698 IS - 40 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 00278424 TI - Learning probabilistic neural representations with randomly connected circuits VL - 117 ER - TY - JOUR AB - Skeletal muscle activity is continuously modulated across physiologic states to provide coordination, flexibility and responsiveness to body tasks and external inputs. Despite the central role the muscular system plays in facilitating vital body functions, the network of brain-muscle interactions required to control hundreds of muscles and synchronize their activation in relation to distinct physiologic states has not been investigated. Recent approaches have focused on general associations between individual brain rhythms and muscle activation during movement tasks. However, the specific forms of coupling, the functional network of cortico-muscular coordination, and how network structure and dynamics are modulated by autonomic regulation across physiologic states remains unknown. To identify and quantify the cortico-muscular interaction network and uncover basic features of neuro-autonomic control of muscle function, we investigate the coupling between synchronous bursts in cortical rhythms and peripheral muscle activation during sleep and wake. Utilizing the concept of time delay stability and a novel network physiology approach, we find that the brain-muscle network exhibits complex dynamic patterns of communication involving multiple brain rhythms across cortical locations and different electromyographic frequency bands. Moreover, our results show that during each physiologic state the cortico-muscular network is characterized by a specific profile of network links strength, where particular brain rhythms play role of main mediators of interaction and control. Further, we discover a hierarchical reorganization in network structure across physiologic states, with high connectivity and network link strength during wake, intermediate during REM and light sleep, and low during deep sleep, a sleep-stage stratification that demonstrates a unique association between physiologic states and cortico-muscular network structure. The reported empirical observations are consistent across individual subjects, indicating universal behavior in network structure and dynamics, and high sensitivity of cortico-muscular control to changes in autonomic regulation, even at low levels of physical activity and muscle tone during sleep. Our findings demonstrate previously unrecognized basic principles of brain-muscle network communication and control, and provide new perspectives on the regulatory mechanisms of brain dynamics and locomotor activation, with potential clinical implications for neurodegenerative, movement and sleep disorders, and for developing efficient treatment strategies. AU - Rizzo, Rossella AU - Zhang, Xiyun AU - Wang, Jilin W.J.L. AU - Lombardi, Fabrizio AU - Ivanov, Plamen Ch ID - 8955 JF - Frontiers in Physiology TI - Network physiology of cortico–muscular interactions VL - 11 ER - TY - JOUR AB - In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future. AU - Grah, Rok AU - Zoller, Benjamin AU - Tkačik, Gašper ID - 9000 IS - 50 JF - PNAS SN - 00278424 TI - Nonequilibrium models of optimal enhancer function VL - 117 ER - TY - JOUR AB - Origin and functions of intermittent transitions among sleep stages, including brief awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing sleep on scales of seconds and minutes results from intrinsic non-equilibrium critical dynamics. We investigate θ- and δ-wave dynamics in control rats and in rats where the sleep-promoting ventrolateral preoptic nucleus (VLPO) is lesioned (male Sprague-Dawley rats). We demonstrate that bursts in θ and δ cortical rhythms exhibit complex temporal organization, with long-range correlations and robust duality of power-law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, features typical of non-equilibrium systems self-organizing at criticality. We show that such non-equilibrium behavior relates to anti-correlated coupling between θ- and δ-bursts, persists across a range of time scales, and is independent of the dominant physiologic state; indications of a basic principle in sleep regulation. Further, we find that VLPO lesions lead to a modulation of cortical dynamics resulting in altered dynamical parameters of θ- and δ-bursts and significant reduction in θ–δ coupling. Our empirical findings and model simulations demonstrate that θ–δ coupling is essential for the emerging non-equilibrium critical dynamics observed across the sleep–wake cycle, and indicate that VLPO neurons may have dual role for both sleep and arousal/brief wake activation. The uncovered critical behavior in sleep- and wake-related cortical rhythms indicates a mechanism essential for the micro-architecture of spontaneous sleep-stage and arousal transitions within a novel, non-homeostatic paradigm of sleep regulation. AU - Lombardi, Fabrizio AU - Gómez-Extremera, Manuel AU - Bernaola-Galván, Pedro AU - Vetrivelan, Ramalingam AU - Saper, Clifford B. AU - Scammell, Thomas E. AU - Ivanov, Plamen Ch. ID - 8084 IS - 1 JF - Journal of Neuroscience SN - 0270-6474 TI - Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake VL - 40 ER - TY - THES AB - In the thesis we focus on the interplay of the biophysics and evolution of gene regulation. We start by addressing how the type of prokaryotic gene regulation – activation and repression – affects spurious binding to DNA, also known as transcriptional crosstalk. We propose that regulatory interference caused by excess regulatory proteins in the dense cellular medium – global crosstalk – could be a factor in determining which type of gene regulatory network is evolutionarily preferred. Next,we use a normative approach in eukaryotic gene regulation to describe minimal non-equilibrium enhancer models that optimize so-called regulatory phenotypes. We find a class of models that differ from standard thermodynamic equilibrium models by a single parameter that notably increases the regulatory performance. Next chapter addresses the question of genotype-phenotype-fitness maps of higher dimensional phenotypes. We show that our biophysically realistic approach allows us to understand how the mechanisms of promoter function constrain genotypephenotype maps, and how they affect the evolutionary trajectories of promoters. In the last chapter we ask whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. Using mathematical modeling, we show that amplifications can tune gene expression in many environments, including those where transcription factor-based schemes are hard to evolve or maintain. AU - Grah, Rok ID - 8155 SN - 2663-337X TI - Gene regulation across scales – how biophysical constraints shape evolution ER - TY - GEN AB - In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene expression levels that is compatible with in vivo and in vitro bio-physical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In non-equilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal non-equilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in non-equilibrium models is in a tradeoff with gene expression noise, predicting bursty dynamics — an experimentally-observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space to a much smaller subspace that optimally realizes biological function prior to inference from data, our normative approach holds promise for mathematical models in systems biology. AU - Grah, Rok AU - Zoller, Benjamin AU - Tkačik, Gašper ID - 7675 T2 - bioRxiv TI - Normative models of enhancer function ER - TY - JOUR AB - Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models. AU - Grah, Rok AU - Friedlander, Tamar ID - 7569 IS - 2 JF - PLOS Computational Biology SN - 1553-7358 TI - The relation between crosstalk and gene regulation form revisited VL - 16 ER - TY - GEN AU - Grah, Rok AU - Friedlander, Tamar ID - 9777 TI - Maximizing crosstalk ER - TY - DATA AB - Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by "translation bottlenecks": points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of "continuous epistasis" in bacterial physiology. AU - Kavcic, Bor ID - 8097 KW - Escherichia coli KW - antibiotic combinations KW - translation KW - growth laws KW - drug interactions KW - bacterial physiology KW - translation inhibitors TI - Analysis scripts and research data for the paper "Mechanisms of drug interactions between translation-inhibiting antibiotics" ER - TY - DATA AB - Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems. AU - Kavcic, Bor ID - 8930 KW - Escherichia coli KW - antibiotic combinations KW - translation KW - growth laws KW - drug interactions KW - bacterial physiology KW - translation inhibitors TI - Analysis scripts and research data for the paper "Minimal biophysical model of combined antibiotic action" ER - TY - DATA AB - Organisms cope with change by employing transcriptional regulators. However, when faced with rare environments, the evolution of transcriptional regulators and their promoters may be too slow. We ask whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. By real-time monitoring of gene copy number mutations in E. coli, we show that gene duplications and amplifications enable adaptation to fluctuating environments by rapidly generating copy number, and hence expression level, polymorphism. This ‘amplification-mediated gene expression tuning’ occurs on timescales similar to canonical gene regulation and can deal with rapid environmental changes. Mathematical modeling shows that amplifications also tune gene expression in stochastic environments where transcription factor-based schemes are hard to evolve or maintain. The fleeting nature of gene amplifications gives rise to a generic population-level mechanism that relies on genetic heterogeneity to rapidly tune expression of any gene, without leaving any genomic signature. AU - Grah, Rok ID - 7383 KW - Matlab scripts KW - analysis of microfluidics KW - mathematical model TI - Matlab scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression regulation ER - TY - THES AB - Synthesis of proteins – translation – is a fundamental process of life. Quantitative studies anchor translation into the context of bacterial physiology and reveal several mathematical relationships, called “growth laws,” which capture physiological feedbacks between protein synthesis and cell growth. Growth laws describe the dependency of the ribosome abundance as a function of growth rate, which can change depending on the growth conditions. Perturbations of translation reveal that bacteria employ a compensatory strategy in which the reduced translation capability results in increased expression of the translation machinery. Perturbations of translation are achieved in various ways; clinically interesting is the application of translation-targeting antibiotics – translation inhibitors. The antibiotic effects on bacterial physiology are often poorly understood. Bacterial responses to two or more simultaneously applied antibiotics are even more puzzling. The combined antibiotic effect determines the type of drug interaction, which ranges from synergy (the effect is stronger than expected) to antagonism (the effect is weaker) and suppression (one of the drugs loses its potency). In the first part of this work, we systematically measure the pairwise interaction network for translation inhibitors that interfere with different steps in translation. We find that the interactions are surprisingly diverse and tend to be more antagonistic. To explore the underlying mechanisms, we begin with a minimal biophysical model of combined antibiotic action. We base this model on the kinetics of antibiotic uptake and binding together with the physiological response described by the growth laws. The biophysical model explains some drug interactions, but not all; it specifically fails to predict suppression. In the second part of this work, we hypothesize that elusive suppressive drug interactions result from the interplay between ribosomes halted in different stages of translation. To elucidate this putative mechanism of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using in- ducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks partially causes these interactions. We extend this approach by varying two translation bottlenecks simultaneously. This approach reveals the suppression of translocation inhibition by inhibited translation. We rationalize this effect by modeling dense traffic of ribosomes that move on transcripts in a translation factor-mediated manner. This model predicts a dissolution of traffic jams caused by inhibited translocation when the density of ribosome traffic is reduced by lowered initiation. We base this model on the growth laws and quantitative relationships between different translation and growth parameters. In the final part of this work, we describe a set of tools aimed at quantification of physiological and translation parameters. We further develop a simple model that directly connects the abundance of a translation factor with the growth rate, which allows us to extract physiological parameters describing initiation. We demonstrate the development of tools for measuring translation rate. This thesis showcases how a combination of high-throughput growth rate mea- surements, genetics, and modeling can reveal mechanisms of drug interactions. Furthermore, by a gradual transition from combinations of antibiotics to precise genetic interventions, we demonstrated the equivalency between genetic and chemi- cal perturbations of translation. These findings tile the path for quantitative studies of antibiotic combinations and illustrate future approaches towards the quantitative description of translation. AU - Kavcic, Bor ID - 8657 SN - 2663-337X TI - Perturbations of protein synthesis: from antibiotics to genetics and physiology ER - TY - JOUR AB - Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by “translation bottlenecks”: points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of “continuous epistasis” in bacterial physiology. AU - Kavcic, Bor AU - Tkačik, Gašper AU - Bollenbach, Tobias ID - 8250 JF - Nature Communications SN - 2041-1723 TI - Mechanisms of drug interactions between translation-inhibiting antibiotics VL - 11 ER - TY - GEN AB - Combining drugs can improve the efficacy of treatments. However, predicting the effect of drug combinations is still challenging. The combined potency of drugs determines the drug interaction, which is classified as synergistic, additive, antagonistic, or suppressive. While probabilistic, non-mechanistic models exist, there is currently no biophysical model that can predict antibiotic interactions. Here, we present a physiologically relevant model of the combined action of antibiotics that inhibit protein synthesis by targeting the ribosome. This model captures the kinetics of antibiotic binding and transport, and uses bacterial growth laws to predict growth in the presence of antibiotic combinations. We find that this biophysical model can produce all drug interaction types except suppression. We show analytically that antibiotics which cannot bind to the ribosome simultaneously generally act as substitutes for one another, leading to additive drug interactions. Previously proposed null expectations for higher-order drug interactions follow as a limiting case of our model. We further extend the model to include the effects of direct physical or allosteric interactions between individual drugs on the ribosome. Notably, such direct interactions profoundly change the combined drug effect, depending on the kinetic parameters of the drugs used. The model makes additional predictions for the effects of resistance genes on drug interactions and for interactions between ribosome-targeting antibiotics and antibiotics with other targets. These findings enhance our understanding of the interplay between drug action and cell physiology and are a key step toward a general framework for predicting drug interactions. AU - Kavcic, Bor AU - Tkačik, Gašper AU - Bollenbach, Tobias ID - 7673 T2 - bioRxiv TI - A minimal biophysical model of combined antibiotic action ER - TY - JOUR AB - Organisms cope with change by taking advantage of transcriptional regulators. However, when faced with rare environments, the evolution of transcriptional regulators and their promoters may be too slow. Here, we investigate whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. Using real-time monitoring of gene-copy-number mutations in Escherichia coli, we show that gene duplications and amplifications enable adaptation to fluctuating environments by rapidly generating copy-number and, therefore, expression-level polymorphisms. This amplification-mediated gene expression tuning (AMGET) occurs on timescales that are similar to canonical gene regulation and can respond to rapid environmental changes. Mathematical modelling shows that amplifications also tune gene expression in stochastic environments in which transcription-factor-based schemes are hard to evolve or maintain. The fleeting nature of gene amplifications gives rise to a generic population-level mechanism that relies on genetic heterogeneity to rapidly tune the expression of any gene, without leaving any genomic signature. AU - Tomanek, Isabella AU - Grah, Rok AU - Lagator, M. AU - Andersson, A. M. C. AU - Bollback, Jonathan P AU - Tkačik, Gašper AU - Guet, Calin C ID - 7652 IS - 4 JF - Nature Ecology & Evolution SN - 2397-334X TI - Gene amplification as a form of population-level gene expression regulation VL - 4 ER - TY - GEN AB - There is increasing evidence that protein binding to specific sites along DNA can activate the reading out of genetic information without coming into direct physical contact with the gene. There also is evidence that these distant but interacting sites are embedded in a liquid droplet of proteins which condenses out of the surrounding solution. We argue that droplet-mediated interactions can account for crucial features of gene regulation only if the droplet is poised at a non-generic point in its phase diagram. We explore a minimal model that embodies this idea, show that this model has a natural mechanism for self-tuning, and suggest direct experimental tests. AU - Bialek, William AU - Gregor, Thomas AU - Tkačik, Gašper ID - 7552 T2 - arXiv:1912.08579 TI - Action at a distance in transcriptional regulation ER - TY - JOUR AB - In developing organisms, spatially prescribed cell identities are thought to be determined by the expression levels of multiple genes. Quantitative tests of this idea, however, require a theoretical framework capable of exposing the rules and precision of cell specification over developmental time. We use the gap gene network in the early fly embryo as an example to show how expression levels of the four gap genes can be jointly decoded into an optimal specification of position with 1% accuracy. The decoder correctly predicts, with no free parameters, the dynamics of pair-rule expression patterns at different developmental time points and in various mutant backgrounds. Precise cellular identities are thus available at the earliest stages of development, contrasting the prevailing view of positional information being slowly refined across successive layers of the patterning network. Our results suggest that developmental enhancers closely approximate a mathematically optimal decoding strategy. AU - Petkova, Mariela D. AU - Tkacik, Gasper AU - Bialek, William AU - Wieschaus, Eric F. AU - Gregor, Thomas ID - 5945 IS - 4 JF - Cell TI - Optimal decoding of cellular identities in a genetic network VL - 176 ER - TY - JOUR AB - In this article it is shown that large systems with many interacting units endowing multiple phases display self-oscillations in the presence of linear feedback between the control and order parameters, where an Andronov–Hopf bifurcation takes over the phase transition. This is simply illustrated through the mean field Landau theory whose feedback dynamics turn out to be described by the Van der Pol equation and it is then validated for the fully connected Ising model following heat bath dynamics. Despite its simplicity, this theory accounts potentially for a rich range of phenomena: here it is applied to describe in a stylized way (i) excess demand-price cycles due to strong herding in a simple agent-based market model; (ii) congestion waves in queuing networks triggered by user feedback to delays in overloaded conditions; and (iii) metabolic network oscillations resulting from cell growth control in a bistable phenotypic landscape. AU - De Martino, Daniele ID - 6049 IS - 4 JF - Journal of Physics A: Mathematical and Theoretical TI - Feedback-induced self-oscillations in large interacting systems subjected to phase transitions VL - 52 ER - TY - JOUR AB - Sudden stress often triggers diverse, temporally structured gene expression responses in microbes, but it is largely unknown how variable in time such responses are and if genes respond in the same temporal order in every single cell. Here, we quantified timing variability of individual promoters responding to sublethal antibiotic stress using fluorescent reporters, microfluidics, and time‐lapse microscopy. We identified lower and upper bounds that put definite constraints on timing variability, which varies strongly among promoters and conditions. Timing variability can be interpreted using results from statistical kinetics, which enable us to estimate the number of rate‐limiting molecular steps underlying different responses. We found that just a few critical steps control some responses while others rely on dozens of steps. To probe connections between different stress responses, we then tracked the temporal order and response time correlations of promoter pairs in individual cells. Our results support that, when bacteria are exposed to the antibiotic nitrofurantoin, the ensuing oxidative stress and SOS responses are part of the same causal chain of molecular events. In contrast, under trimethoprim, the acid stress response and the SOS response are part of different chains of events running in parallel. Our approach reveals fundamental constraints on gene expression timing and provides new insights into the molecular events that underlie the timing of stress responses. AU - Mitosch, Karin AU - Rieckh, Georg AU - Bollenbach, Mark Tobias ID - 6046 IS - 2 JF - Molecular systems biology TI - Temporal order and precision of complex stress responses in individual bacteria VL - 15 ER - TY - JOUR AB - Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems. AU - Ruess, Jakob AU - Pleska, Maros AU - Guet, Calin C AU - Tkačik, Gašper ID - 6784 IS - 7 JF - PLoS Computational Biology TI - Molecular noise of innate immunity shapes bacteria-phage ecologies VL - 15 ER - TY - GEN AU - Ruess, Jakob AU - Pleska, Maros AU - Guet, Calin C AU - Tkačik, Gašper ID - 9786 TI - Supporting text and results ER - TY - JOUR AB - Biochemical reactions often occur at low copy numbers but at once in crowded and diverse environments. Space and stochasticity therefore play an essential role in biochemical networks. Spatial-stochastic simulations have become a prominent tool for understanding how stochasticity at the microscopic level influences the macroscopic behavior of such systems. While particle-based models guarantee the level of detail necessary to accurately describe the microscopic dynamics at very low copy numbers, the algorithms used to simulate them typically imply trade-offs between computational efficiency and biochemical accuracy. eGFRD (enhanced Green’s Function Reaction Dynamics) is an exact algorithm that evades such trade-offs by partitioning the N-particle system into M ≤ N analytically tractable one- and two-particle systems; the analytical solutions (Green’s functions) then are used to implement an event-driven particle-based scheme that allows particles to make large jumps in time and space while retaining access to their state variables at arbitrary simulation times. Here we present “eGFRD2,” a new eGFRD version that implements the principle of eGFRD in all dimensions, thus enabling efficient particle-based simulation of biochemical reaction-diffusion processes in the 3D cytoplasm, on 2D planes representing membranes, and on 1D elongated cylinders representative of, e.g., cytoskeletal tracks or DNA; in 1D, it also incorporates convective motion used to model active transport. We find that, for low particle densities, eGFRD2 is up to 6 orders of magnitude faster than conventional Brownian dynamics. We exemplify the capabilities of eGFRD2 by simulating an idealized model of Pom1 gradient formation, which involves 3D diffusion, active transport on microtubules, and autophosphorylation on the membrane, confirming recent experimental and theoretical results on this system to hold under genuinely stochastic conditions. AU - Sokolowski, Thomas R AU - Paijmans, Joris AU - Bossen, Laurens AU - Miedema, Thomas AU - Wehrens, Martijn AU - Becker, Nils B. AU - Kaizu, Kazunari AU - Takahashi, Koichi AU - Dogterom, Marileen AU - ten Wolde, Pieter Rein ID - 7422 IS - 5 JF - The Journal of Chemical Physics SN - 0021-9606 TI - eGFRD in all dimensions VL - 150 ER - TY - JOUR AB - Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks. AU - Cepeda Humerez, Sarah A AU - Ruess, Jakob AU - Tkačik, Gašper ID - 6900 IS - 9 JF - PLoS computational biology TI - Estimating information in time-varying signals VL - 15 ER - TY - JOUR AB - The abelian sandpile serves as a model to study self-organized criticality, a phenomenon occurring in biological, physical and social processes. The identity of the abelian group is a fractal composed of self-similar patches, and its limit is subject of extensive collaborative research. Here, we analyze the evolution of the sandpile identity under harmonic fields of different orders. We show that this evolution corresponds to periodic cycles through the abelian group characterized by the smooth transformation and apparent conservation of the patches constituting the identity. The dynamics induced by second and third order harmonics resemble smooth stretchings, respectively translations, of the identity, while the ones induced by fourth order harmonics resemble magnifications and rotations. Starting with order three, the dynamics pass through extended regions of seemingly random configurations which spontaneously reassemble into accentuated patterns. We show that the space of harmonic functions projects to the extended analogue of the sandpile group, thus providing a set of universal coordinates identifying configurations between different domains. Since the original sandpile group is a subgroup of the extended one, this directly implies that it admits a natural renormalization. Furthermore, we show that the harmonic fields can be induced by simple Markov processes, and that the corresponding stochastic dynamics show remarkable robustness over hundreds of periods. Finally, we encode information into seemingly random configurations, and decode this information with an algorithm requiring minimal prior knowledge. Our results suggest that harmonic fields might split the sandpile group into sub-sets showing different critical coefficients, and that it might be possible to extend the fractal structure of the identity beyond the boundaries of its domain. AU - Lang, Moritz AU - Shkolnikov, Mikhail ID - 196 IS - 8 JF - Proceedings of the National Academy of Sciences TI - Harmonic dynamics of the Abelian sandpile VL - 116 ER - TY - JOUR AB - We theoretically study the shapes of lipid vesicles confined to a spherical cavity, elaborating a framework based on the so-called limiting shapes constructed from geometrically simple structural elements such as double-membrane walls and edges. Partly inspired by numerical results, the proposed non-compartmentalized and compartmentalized limiting shapes are arranged in the bilayer-couple phase diagram which is then compared to its free-vesicle counterpart. We also compute the area-difference-elasticity phase diagram of the limiting shapes and we use it to interpret shape transitions experimentally observed in vesicles confined within another vesicle. The limiting-shape framework may be generalized to theoretically investigate the structure of certain cell organelles such as the mitochondrion. AU - Kavcic, Bor AU - Sakashita, A. AU - Noguchi, H. AU - Ziherl, P. ID - 5817 IS - 4 JF - Soft Matter SN - 1744-683X TI - Limiting shapes of confined lipid vesicles VL - 15 ER - TY - THES AB - Single cells are constantly interacting with their environment and each other, more importantly, the accurate perception of environmental cues is crucial for growth, survival, and reproduction. This communication between cells and their environment can be formalized in mathematical terms and be quantified as the information flow between them, as prescribed by information theory. The recent availability of real–time dynamical patterns of signaling molecules in single cells has allowed us to identify encoding about the identity of the environment in the time–series. However, efficient estimation of the information transmitted by these signals has been a data–analysis challenge due to the high dimensionality of the trajectories and the limited number of samples. In the first part of this thesis, we develop and evaluate decoding–based estimation methods to lower bound the mutual information and derive model–based precise information estimates for biological reaction networks governed by the chemical master equation. This is followed by applying the decoding-based methods to study the intracellular representation of extracellular changes in budding yeast, by observing the transient dynamics of nuclear translocation of 10 transcription factors in response to 3 stress conditions. Additionally, we apply these estimators to previously published data on ERK and Ca2+ signaling and yeast stress response. We argue that this single cell decoding-based measure of information provides an unbiased, quantitative and interpretable measure for the fidelity of biological signaling processes. Finally, in the last section, we deal with gene regulation which is primarily controlled by transcription factors (TFs) that bind to the DNA to activate gene expression. The possibility that non-cognate TFs activate transcription diminishes the accuracy of regulation with potentially disastrous effects for the cell. This ’crosstalk’ acts as a previously unexplored source of noise in biochemical networks and puts a strong constraint on their performance. To mitigate erroneous initiation we propose an out of equilibrium scheme that implements kinetic proofreading. We show that such architectures are favored over their equilibrium counterparts for complex organisms despite introducing noise in gene expression. AU - Cepeda Humerez, Sarah A ID - 6473 KW - Information estimation KW - Time-series KW - data analysis SN - 2663-337X TI - Estimating information flow in single cells ER - TY - THES AB - Transcription factors, by binding to specific sequences on the DNA, control the precise spatio-temporal expression of genes inside a cell. However, this specificity is limited, leading to frequent incorrect binding of transcription factors that might have deleterious consequences on the cell. By constructing a biophysical model of TF-DNA binding in the context of gene regulation, I will first explore how regulatory constraints can strongly shape the distribution of a population in sequence space. Then, by directly linking this to a picture of multiple types of transcription factors performing their functions simultaneously inside the cell, I will explore the extent of regulatory crosstalk -- incorrect binding interactions between transcription factors and binding sites that lead to erroneous regulatory states -- and understand the constraints this places on the design of regulatory systems. I will then develop a generic theoretical framework to investigate the coevolution of multiple transcription factors and multiple binding sites, in the context of a gene regulatory network that performs a certain function. As a particular tractable version of this problem, I will consider the evolution of two transcription factors when they transmit upstream signals to downstream target genes. Specifically, I will describe the evolutionary steady states and the evolutionary pathways involved, along with their timescales, of a system that initially undergoes a transcription factor duplication event. To connect this important theoretical model to the prominent biological event of transcription factor duplication giving rise to paralogous families, I will then describe a bioinformatics analysis of C2H2 Zn-finger transcription factors, a major family in humans, and focus on the patterns of evolution that paralogs have undergone in their various protein domains in the recent past. AU - Prizak, Roshan ID - 6071 SN - 2663-337X TI - Coevolution of transcription factors and their binding sites in sequence space ER - TY - JOUR AB - Origin and functions of intermittent transitions among sleep stages, including short awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing the sleep-wake cycle results from an underlying non-equilibrium critical dynamics, bridging collective behaviors across spatio-temporal scales. We investigate θ and δ wave dynamics in control rats and in rats with lesions of sleep-promoting neurons in the parafacial zone. We demonstrate that intermittent bursts in θ and δ rhythms exhibit a complex temporal organization, with long-range power-law correlations and a robust duality of power law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, typical features of non-equilibrium systems self-organizing at criticality. Crucially, such temporal organization relates to anti-correlated coupling between θ- and δ-bursts, and is independent of the dominant physiologic state and lesions, a solid indication of a basic principle in sleep dynamics. AU - Wang, Jilin W. J. L. AU - Lombardi, Fabrizio AU - Zhang, Xiyun AU - Anaclet, Christelle AU - Ivanov, Plamen Ch. ID - 7103 IS - 11 JF - PLoS Computational Biology SN - 1553-7358 TI - Non-equilibrium critical dynamics of bursts in θ and δ rhythms as fundamental characteristic of sleep and wake micro-architecture VL - 15 ER - TY - JOUR AB - Cells need to reliably sense external ligand concentrations to achieve various biological functions such as chemotaxis or signaling. The molecular recognition of ligands by surface receptors is degenerate in many systems, leading to crosstalk between ligand-receptor pairs. Crosstalk is often thought of as a deviation from optimal specific recognition, as the binding of noncognate ligands can interfere with the detection of the receptor's cognate ligand, possibly leading to a false triggering of a downstream signaling pathway. Here we quantify the optimal precision of sensing the concentrations of multiple ligands by a collection of promiscuous receptors. We demonstrate that crosstalk can improve precision in concentration sensing and discrimination tasks. To achieve superior precision, the additional information about ligand concentrations contained in short binding events of the noncognate ligand should be exploited. We present a proofreading scheme to realize an approximate estimation of multiple ligand concentrations that reaches a precision close to the derived optimal bounds. Our results help rationalize the observed ubiquity of receptor crosstalk in molecular sensing. AU - Carballo-Pacheco, Martín AU - Desponds, Jonathan AU - Gavrilchenko, Tatyana AU - Mayer, Andreas AU - Prizak, Roshan AU - Reddy, Gautam AU - Nemenman, Ilya AU - Mora, Thierry ID - 6090 IS - 2 JF - Physical Review E TI - Receptor crosstalk improves concentration sensing of multiple ligands VL - 99 ER - TY - CONF AB - We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and maximum a posteriori decodes (most probable signals given channel output). As part of our derivation, we describe the key properties of the distribution of signals, channel outputs and decodes, that minimizes equivocation and maximizes mutual information. This work addresses a problem in data analysis, where mutual information between signals and decodes is sometimes used to lower bound the mutual information between signals and channel outputs. Our result provides a corresponding upper bound. AU - Hledik, Michal AU - Sokolowski, Thomas R AU - Tkačik, Gašper ID - 7606 SN - 9781538669006 T2 - IEEE Information Theory Workshop, ITW 2019 TI - A tight upper bound on mutual information ER - TY - JOUR AB - A cornerstone of statistical inference, the maximum entropy framework is being increasingly applied to construct descriptive and predictive models of biological systems, especially complex biological networks, from large experimental data sets. Both its broad applicability and the success it obtained in different contexts hinge upon its conceptual simplicity and mathematical soundness. Here we try to concisely review the basic elements of the maximum entropy principle, starting from the notion of ‘entropy’, and describe its usefulness for the analysis of biological systems. As examples, we focus specifically on the problem of reconstructing gene interaction networks from expression data and on recent work attempting to expand our system-level understanding of bacterial metabolism. Finally, we highlight some extensions and potential limitations of the maximum entropy approach, and point to more recent developments that are likely to play a key role in the upcoming challenges of extracting structures and information from increasingly rich, high-throughput biological data. AU - De Martino, Andrea AU - De Martino, Daniele ID - 306 IS - 4 JF - Heliyon TI - An introduction to the maximum entropy approach and its application to inference problems in biology VL - 4 ER - TY - JOUR AB - The hanging-drop network (HDN) is a technology platform based on a completely open microfluidic network at the bottom of an inverted, surface-patterned substrate. The platform is predominantly used for the formation, culturing, and interaction of self-assembled spherical microtissues (spheroids) under precisely controlled flow conditions. Here, we describe design, fabrication, and operation of microfluidic hanging-drop networks. AU - Misun, Patrick AU - Birchler, Axel AU - Lang, Moritz AU - Hierlemann, Andreas AU - Frey, Olivier ID - 305 JF - Methods in Molecular Biology TI - Fabrication and operation of microfluidic hanging drop networks VL - 1771 ER - TY - JOUR AB - Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making. AU - Granados, Alejandro AU - Pietsch, Julian AU - Cepeda Humerez, Sarah A AU - Farquhar, Isebail AU - Tkacik, Gasper AU - Swain, Peter ID - 281 IS - 23 JF - PNAS TI - Distributed and dynamic intracellular organization of extracellular information VL - 115 ER - TY - JOUR AB - Self-incompatibility (SI) is a genetically based recognition system that functions to prevent self-fertilization and mating among related plants. An enduring puzzle in SI is how the high diversity observed in nature arises and is maintained. Based on the underlying recognition mechanism, SI can be classified into two main groups: self- and non-self recognition. Most work has focused on diversification within self-recognition systems despite expected differences between the two groups in the evolutionary pathways and outcomes of diversification. Here, we use a deterministic population genetic model and stochastic simulations to investigate how novel S-haplotypes evolve in a gametophytic non-self recognition (SRNase/S Locus F-box (SLF)) SI system. For this model the pathways for diversification involve either the maintenance or breakdown of SI and can vary in the order of mutations of the female (SRNase) and male (SLF) components. We show analytically that diversification can occur with high inbreeding depression and self-pollination, but this varies with evolutionary pathway and level of completeness (which determines the number of potential mating partners in the population), and in general is more likely for lower haplotype number. The conditions for diversification are broader in stochastic simulations of finite population size. However, the number of haplotypes observed under high inbreeding and moderate to high self-pollination is less than that commonly observed in nature. Diversification was observed through pathways that maintain SI as well as through self-compatible intermediates. Yet the lifespan of diversified haplotypes was sensitive to their level of completeness. By examining diversification in a non-self recognition SI system, this model extends our understanding of the evolution and maintenance of haplotype diversity observed in a self recognition system common in flowering plants. AU - Bodova, Katarina AU - Priklopil, Tadeas AU - Field, David AU - Barton, Nicholas H AU - Pickup, Melinda ID - 316 IS - 3 JF - Genetics TI - Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system VL - 209 ER - TY - GEN AB - File S1 contains figures that clarify the following features: (i) effect of population size on the average number/frequency of SI classes, (ii) changes in the minimal completeness deficit in time for a single class, and (iii) diversification diagrams for all studied pathways, including the summary figure for k = 8. File S2 contains the code required for a stochastic simulation of the SLF system with an example. This file also includes the output in the form of figures and tables. AU - Bod'ová, Katarína AU - Priklopil, Tadeas AU - Field, David AU - Barton, Nicholas H AU - Pickup, Melinda ID - 9813 TI - Supplemental material for Bodova et al., 2018 ER - TY - JOUR AB - Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie’s Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data. AU - Bod’Ová, Katarína AU - Mitchell, Gabriel AU - Harpaz, Roy AU - Schneidman, Elad AU - Tkacik, Gasper ID - 406 IS - 3 JF - PLoS One TI - Probabilistic models of individual and collective animal behavior VL - 13 ER - TY - JOUR AB - Temperate bacteriophages integrate in bacterial genomes as prophages and represent an important source of genetic variation for bacterial evolution, frequently transmitting fitness-augmenting genes such as toxins responsible for virulence of major pathogens. However, only a fraction of bacteriophage infections are lysogenic and lead to prophage acquisition, whereas the majority are lytic and kill the infected bacteria. Unless able to discriminate lytic from lysogenic infections, mechanisms of immunity to bacteriophages are expected to act as a double-edged sword and increase the odds of survival at the cost of depriving bacteria of potentially beneficial prophages. We show that although restriction-modification systems as mechanisms of innate immunity prevent both lytic and lysogenic infections indiscriminately in individual bacteria, they increase the number of prophage-acquiring individuals at the population level. We find that this counterintuitive result is a consequence of phage-host population dynamics, in which restriction-modification systems delay infection onset until bacteria reach densities at which the probability of lysogeny increases. These results underscore the importance of population-level dynamics as a key factor modulating costs and benefits of immunity to temperate bacteriophages AU - Pleska, Maros AU - Lang, Moritz AU - Refardt, Dominik AU - Levin, Bruce AU - Guet, Calin C ID - 457 IS - 2 JF - Nature Ecology and Evolution TI - Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity VL - 2 ER - TY - GEN AB - Implementation of the inference method in Matlab, including three applications of the method: The first one for the model of ant motion, the second one for bacterial chemotaxis, and the third one for the motion of fish. AU - Bod’Ová, Katarína AU - Mitchell, Gabriel AU - Harpaz, Roy AU - Schneidman, Elad AU - Tkačik, Gašper ID - 9831 TI - Implementation of the inference method in Matlab ER - TY - JOUR AB - Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network and, thus, depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes in the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach in retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus. AU - Ferrari, Ulisse AU - Deny, Stephane AU - Chalk, Matthew J AU - Tkacik, Gasper AU - Marre, Olivier AU - Mora, Thierry ID - 31 IS - 4 JF - Physical Review E SN - 24700045 TI - Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons VL - 98 ER - TY - JOUR AB - A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, “efficient coding” posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits. AU - Chalk, Matthew J AU - Marre, Olivier AU - Tkacik, Gasper ID - 543 IS - 1 JF - PNAS TI - Toward a unified theory of efficient, predictive, and sparse coding VL - 115 ER - TY - JOUR AB - We study the Fokker-Planck equation derived in the large system limit of the Markovian process describing the dynamics of quantitative traits. The Fokker-Planck equation is posed on a bounded domain and its transport and diffusion coefficients vanish on the domain's boundary. We first argue that, despite this degeneracy, the standard no-flux boundary condition is valid. We derive the weak formulation of the problem and prove the existence and uniqueness of its solutions by constructing the corresponding contraction semigroup on a suitable function space. Then, we prove that for the parameter regime with high enough mutation rate the problem exhibits a positive spectral gap, which implies exponential convergence to equilibrium.Next, we provide a simple derivation of the so-called Dynamic Maximum Entropy (DynMaxEnt) method for approximation of observables (moments) of the Fokker-Planck solution, which can be interpreted as a nonlinear Galerkin approximation. The limited applicability of the DynMaxEnt method inspires us to introduce its modified version that is valid for the whole range of admissible parameters. Finally, we present several numerical experiments to demonstrate the performance of both the original and modified DynMaxEnt methods. We observe that in the parameter regimes where both methods are valid, the modified one exhibits slightly better approximation properties compared to the original one. AU - Bodova, Katarina AU - Haskovec, Jan AU - Markowich, Peter ID - 607 JF - Physica D: Nonlinear Phenomena TI - Well posedness and maximum entropy approximation for the dynamics of quantitative traits VL - 376-377 ER - TY - JOUR AB - Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses. AU - Palmer, Adam AU - Chait, Remy P AU - Kishony, Roy ID - 19 IS - 11 JF - Molecular Biology and Evolution SN - 0737-4038 TI - Nonoptimal gene expression creates latent potential for antibiotic resistance VL - 35 ER - TY - JOUR AB - Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains. AU - Botella Soler, Vicent AU - Deny, Stephane AU - Martius, Georg S AU - Marre, Olivier AU - Tkacik, Gasper ID - 292 IS - 5 JF - PLoS Computational Biology TI - Nonlinear decoding of a complex movie from the mammalian retina VL - 14 ER - TY - DATA AB - This package contains data for the publication "Nonlinear decoding of a complex movie from the mammalian retina" by Deny S. et al, PLOS Comput Biol (2018). The data consists of (i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin. (ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments are exact repeats or unique ball trajectories. (iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions ("sites") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. AU - Deny, Stephane AU - Marre, Olivier AU - Botella-Soler, Vicente AU - Martius, Georg S AU - Tkacik, Gasper ID - 5584 KW - retina KW - decoding KW - regression KW - neural networks KW - complex stimulus TI - Nonlinear decoding of a complex movie from the mammalian retina ER - TY - JOUR AB - Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells. AU - De Martino, Daniele AU - Mc, Andersson Anna AU - Bergmiller, Tobias AU - Guet, Calin C AU - Tkacik, Gasper ID - 161 IS - 1 JF - Nature Communications TI - Statistical mechanics for metabolic networks during steady state growth VL - 9 ER - TY - DATA AB - Supporting material to the article STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH boundscoli.dat Flux Bounds of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium. polcoli.dat Matrix enconding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium, obtained from the soichiometric matrix by standard linear algebra (reduced row echelon form). ellis.dat Approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium obtained with the Lovasz method. point0.dat Center of the approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium obtained with the Lovasz method. lovasz.cpp This c++ code file receives in input the polytope of the feasible steady states of a metabolic network, (matrix and bounds), and it gives in output an approximate Lowner-John ellipsoid rounding the polytope with the Lovasz method NB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. For further details we refer to PLoS ONE 10.4 e0122670 (2015). sampleHRnew.cpp This c++ code file receives in input the polytope of the feasible steady states of a metabolic network, (matrix and bounds), the ellipsoid rounding the polytope, a point inside and it gives in output a max entropy sampling at fixed average growth rate of the steady states by performing an Hit-and-Run Monte Carlo Markov chain. NB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. For further details we refer to PLoS ONE 10.4 e0122670 (2015). AU - De Martino, Daniele AU - Tkacik, Gasper ID - 5587 KW - metabolic networks KW - e.coli core KW - maximum entropy KW - monte carlo markov chain sampling KW - ellipsoidal rounding TI - Supporting materials "STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH" ER - TY - JOUR AB - Gene regulatory networks evolve through rewiring of individual components—that is, through changes in regulatory connections. However, the mechanistic basis of regulatory rewiring is poorly understood. Using a canonical gene regulatory system, we quantify the properties of transcription factors that determine the evolutionary potential for rewiring of regulatory connections: robustness, tunability and evolvability. In vivo repression measurements of two repressors at mutated operator sites reveal their contrasting evolutionary potential: while robustness and evolvability were positively correlated, both were in trade-off with tunability. Epistatic interactions between adjacent operators alleviated this trade-off. A thermodynamic model explains how the differences in robustness, tunability and evolvability arise from biophysical characteristics of repressor–DNA binding. The model also uncovers that the energy matrix, which describes how mutations affect repressor–DNA binding, encodes crucial information about the evolutionary potential of a repressor. The biophysical determinants of evolutionary potential for regulatory rewiring constitute a mechanistic framework for understanding network evolution. AU - Igler, Claudia AU - Lagator, Mato AU - Tkacik, Gasper AU - Bollback, Jonathan P AU - Guet, Calin C ID - 67 IS - 10 JF - Nature Ecology and Evolution TI - Evolutionary potential of transcription factors for gene regulatory rewiring VL - 2 ER - TY - DATA AB - Mean repression values and standard error of the mean are given for all operator mutant libraries. AU - Igler, Claudia AU - Lagator, Mato AU - Tkacik, Gasper AU - Bollback, Jonathan P AU - Guet, Calin C ID - 5585 TI - Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring ER - TY - JOUR AB - Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior. AU - Chait, Remy P AU - Ruess, Jakob AU - Bergmiller, Tobias AU - Tkacik, Gasper AU - Guet, Calin C ID - 613 IS - 1 JF - Nature Communications SN - 20411723 TI - Shaping bacterial population behavior through computer interfaced control of individual cells VL - 8 ER - TY - CONF AB - We present an approach that enables robots to self-organize their sensorimotor behavior from scratch without providing specific information about neither the robot nor its environment. This is achieved by a simple neural control law that increases the consistency between external sensor dynamics and internal neural dynamics of the utterly simple controller. In this way, the embodiment and the agent-environment coupling are the only source of individual development. We show how an anthropomorphic tendon driven arm-shoulder system develops different behaviors depending on that coupling. For instance: Given a bottle half-filled with water, the arm starts to shake it, driven by the physical response of the water. When attaching a brush, the arm can be manipulated into wiping a table, and when connected to a revolvable wheel it finds out how to rotate it. Thus, the robot may be said to discover the affordances of the world. When allowing two (simulated) humanoid robots to interact physically, they engage into a joint behavior development leading to, for instance, spontaneous cooperation. More social effects are observed if the robots can visually perceive each other. Although, as an observer, it is tempting to attribute an apparent intentionality, there is nothing of the kind put in. As a conclusion, we argue that emergent behavior may be much less rooted in explicit intentions, internal motivations, or specific reward systems than is commonly believed. AU - Der, Ralf AU - Martius, Georg S ID - 652 SN - 978-150905069-7 TI - Dynamical self consistency leads to behavioral development and emergent social interactions in robots ER - TY - JOUR AB - With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object's identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors "waiting" to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics. AU - Der, Ralf AU - Martius, Georg S ID - 658 IS - MAR JF - Frontiers in Neurorobotics SN - 16625218 TI - Self organized behavior generation for musculoskeletal robots VL - 11 ER - TY - JOUR AB - Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality. AU - Humplik, Jan AU - Tkacik, Gasper ID - 720 IS - 9 JF - PLoS Computational Biology SN - 1553734X TI - Probabilistic models for neural populations that naturally capture global coupling and criticality VL - 13 ER - TY - JOUR AB - Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here, we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an “active” mode, in which they are sensitive to the swimming patterns of conspecifics, and a “passive” mode, where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors’ behavior. At the group level, switching between active and passive modes is uncorrelated among fish, but correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates as well as to other species. AU - Harpaz, Roy AU - Tkacik, Gasper AU - Schneidman, Elad ID - 725 IS - 38 JF - PNAS SN - 00278424 TI - Discrete modes of social information processing predict individual behavior of fish in a group VL - 114 ER - TY - GEN AB - Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina. AU - Prentice, Jason AU - Marre, Olivier AU - Ioffe, Mark AU - Loback, Adrianna AU - Tkačik, Gašper AU - Berry, Michael ID - 9709 TI - Data from: Error-robust modes of the retinal population code ER - TY - JOUR AB - In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics. AU - Chalk, Matthew J AU - Masset, Paul AU - Gutkin, Boris AU - Denève, Sophie ID - 680 IS - 6 JF - PLoS Computational Biology SN - 1553734X TI - Sensory noise predicts divisive reshaping of receptive fields VL - 13 ER - TY - GEN AB - Includes derivation of optimal estimation algorithm, generalisation to non-poisson noise statistics, correlated input noise, and implementation of in a multi-layer neural network. AU - Chalk, Matthew J AU - Masset, Paul AU - Gutkin, Boris AU - Denève, Sophie ID - 9855 TI - Supplementary appendix ER - TY - JOUR AB - Antibiotics elicit drastic changes in microbial gene expression, including the induction of stress response genes. While certain stress responses are known to “cross-protect” bacteria from other stressors, it is unclear whether cellular responses to antibiotics have a similar protective role. By measuring the genome-wide transcriptional response dynamics of Escherichia coli to four antibiotics, we found that trimethoprim induces a rapid acid stress response that protects bacteria from subsequent exposure to acid. Combining microfluidics with time-lapse imaging to monitor survival and acid stress response in single cells revealed that the noisy expression of the acid resistance operon gadBC correlates with single-cell survival. Cells with higher gadBC expression following trimethoprim maintain higher intracellular pH and survive the acid stress longer. The seemingly random single-cell survival under acid stress can therefore be predicted from gadBC expression and rationalized in terms of GadB/C molecular function. Overall, we provide a roadmap for identifying the molecular mechanisms of single-cell cross-protection between antibiotics and other stressors. AU - Mitosch, Karin AU - Rieckh, Georg AU - Bollenbach, Tobias ID - 666 IS - 4 JF - Cell Systems SN - 24054712 TI - Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment VL - 4 ER - TY - JOUR AB - The Ising model is one of the simplest and most famous models of interacting systems. It was originally proposed to model ferromagnetic interactions in statistical physics and is now widely used to model spatial processes in many areas such as ecology, sociology, and genetics, usually without testing its goodness-of-fit. Here, we propose an exact goodness-of-fit test for the finite-lattice Ising model. The theory of Markov bases has been developed in algebraic statistics for exact goodness-of-fit testing using a Monte Carlo approach. However, this beautiful theory has fallen short of its promise for applications, because finding a Markov basis is usually computationally intractable. We develop a Monte Carlo method for exact goodness-of-fit testing for the Ising model which avoids computing a Markov basis and also leads to a better connectivity of the Markov chain and hence to a faster convergence. We show how this method can be applied to analyze the spatial organization of receptors on the cell membrane. AU - Martin Del Campo Sanchez, Abraham AU - Cepeda Humerez, Sarah A AU - Uhler, Caroline ID - 2016 IS - 2 JF - Scandinavian Journal of Statistics SN - 03036898 TI - Exact goodness-of-fit testing for the Ising model VL - 44 ER - TY - JOUR AB - In the early visual system, cells of the same type perform the same computation in different places of the visual field. How these cells code together a complex visual scene is unclear. A common assumption is that cells of a single-type extract a single-stimulus feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the rat retina, we show that a homogeneous population of fast OFF ganglion cells simultaneously encodes two radically different features of a visual scene. Cells close to a moving object code quasilinearly for its position, while distant cells remain largely invariant to the object's position and, instead, respond nonlinearly to changes in the object's speed. We develop a quantitative model that accounts for this effect and identify a disinhibitory circuit that mediates it. Ganglion cells of a single type thus do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems. AU - Deny, Stephane AU - Ferrari, Ulisse AU - Mace, Emilie AU - Yger, Pierre AU - Caplette, Romain AU - Picaud, Serge AU - Tkacik, Gasper AU - Marre, Olivier ID - 1104 IS - 1 JF - Nature Communications SN - 20411723 TI - Multiplexed computations in retinal ganglion cells of a single type VL - 8 ER - TY - JOUR AB - In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system’s aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development. AU - Levina (Martius), Anna AU - Priesemann, Viola ID - 993 JF - Nature Communications SN - 20411723 TI - Subsampling scaling VL - 8 ER - TY - JOUR AB - Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components. AU - Friedlander, Tamar AU - Prizak, Roshan AU - Barton, Nicholas H AU - Tkacik, Gasper ID - 955 IS - 1 JF - Nature Communications SN - 20411723 TI - Evolution of new regulatory functions on biophysically realistic fitness landscapes VL - 8 ER - TY - JOUR AB - In this work it is shown that scale-free tails in metabolic flux distributions inferred in stationary models are an artifact due to reactions involved in thermodynamically unfeasible cycles, unbounded by physical constraints and in principle able to perform work without expenditure of free energy. After implementing thermodynamic constraints by removing such loops, metabolic flux distributions scale meaningfully with the physical limiting factors, acquiring in turn a richer multimodal structure potentially leading to symmetry breaking while optimizing for objective functions. AU - De Martino, Daniele ID - 959 IS - 6 JF - Physical Review E Statistical Nonlinear and Soft Matter Physics SN - 24700045 TI - Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics VL - 95 ER - TY - JOUR AB - Viewing the ways a living cell can organize its metabolism as the phase space of a physical system, regulation can be seen as the ability to reduce the entropy of that space by selecting specific cellular configurations that are, in some sense, optimal. Here we quantify the amount of regulation required to control a cell's growth rate by a maximum-entropy approach to the space of underlying metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern as described by genome-scale models. We link the mean growth rate achieved by a population of cells to the minimal amount of metabolic regulation needed to achieve it through a phase diagram that highlights how growth suppression can be as costly (in regulatory terms) as growth enhancement. Moreover, we provide an interpretation of the inverse temperature β controlling maximum-entropy distributions based on the underlying growth dynamics. Specifically, we show that the asymptotic value of β for a cell population can be expected to depend on (i) the carrying capacity of the environment, (ii) the initial size of the colony, and (iii) the probability distribution from which the inoculum was sampled. Results obtained for E. coli and human cells are found to be remarkably consistent with empirical evidence. AU - De Martino, Daniele AU - Capuani, Fabrizio AU - De Martino, Andrea ID - 947 IS - 1 JF - Physical Review E Statistical Nonlinear and Soft Matter Physics SN - 24700045 TI - Quantifying the entropic cost of cellular growth control VL - 96 ER - TY - JOUR AB - Like many developing tissues, the vertebrate neural tube is patterned by antiparallel morphogen gradients. To understand how these inputs are interpreted, we measured morphogen signaling and target gene expression in mouse embryos and chick ex vivo assays. From these data, we derived and validated a characteristic decoding map that relates morphogen input to the positional identity of neural progenitors. Analysis of the observed responses indicates that the underlying interpretation strategy minimizes patterning errors in response to the joint input of noisy opposing gradients. We reverse-engineered a transcriptional network that provides a mechanistic basis for the observed cell fate decisions and accounts for the precision and dynamics of pattern formation. Together, our data link opposing gradient dynamics in a growing tissue to precise pattern formation. AU - Zagórski, Marcin P AU - Tabata, Yoji AU - Brandenberg, Nathalie AU - Lutolf, Matthias AU - Tkacik, Gasper AU - Bollenbach, Tobias AU - Briscoe, James AU - Kicheva, Anna ID - 943 IS - 6345 JF - Science SN - 00368075 TI - Decoding of position in the developing neural tube from antiparallel morphogen gradients VL - 356 ER - TY - JOUR AB - The resolution of a linear system with positive integer variables is a basic yet difficult computational problem with many applications. We consider sparse uncorrelated random systems parametrised by the density c and the ratio α=N/M between number of variables N and number of constraints M. By means of ensemble calculations we show that the space of feasible solutions endows a Van-Der-Waals phase diagram in the plane (c, α). We give numerical evidence that the associated computational problems become more difficult across the critical point and in particular in the coexistence region. AU - Colabrese, Simona AU - De Martino, Daniele AU - Leuzzi, Luca AU - Marinari, Enzo ID - 823 IS - 9 JF - Journal of Statistical Mechanics: Theory and Experiment SN - 17425468 TI - Phase transitions in integer linear problems VL - 2017 ER - TY - JOUR AB - Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible. AU - Savin, Cristina AU - Tkacik, Gasper ID - 730 JF - Current Opinion in Neurobiology SN - 09594388 TI - Maximum entropy models as a tool for building precise neural controls VL - 46 ER - TY - JOUR AB - In this work maximum entropy distributions in the space of steady states of metabolic networks are considered upon constraining the first and second moments of the growth rate. Coexistence of fast and slow phenotypes, with bimodal flux distributions, emerges upon considering control on the average growth (optimization) and its fluctuations (heterogeneity). This is applied to the carbon catabolic core of Escherichia coli where it quantifies the metabolic activity of slow growing phenotypes and it provides a quantitative map with metabolic fluxes, opening the possibility to detect coexistence from flux data. A preliminary analysis on data for E. coli cultures in standard conditions shows degeneracy for the inferred parameters that extend in the coexistence region. AU - De Martino, Daniele ID - 548 IS - 6 JF - Physical Review E SN - 2470-0045 TI - Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes VL - 96 ER - TY - JOUR AB - A nonlinear system possesses an invariance with respect to a set of transformations if its output dynamics remain invariant when transforming the input, and adjusting the initial condition accordingly. Most research has focused on invariances with respect to time-independent pointwise transformations like translational-invariance (u(t) -> u(t) + p, p in R) or scale-invariance (u(t) -> pu(t), p in R>0). In this article, we introduce the concept of s0-invariances with respect to continuous input transformations exponentially growing/decaying over time. We show that s0-invariant systems not only encompass linear time-invariant (LTI) systems with transfer functions having an irreducible zero at s0 in R, but also that the input/output relationship of nonlinear s0-invariant systems possesses properties well known from their linear counterparts. Furthermore, we extend the concept of s0-invariances to second- and higher-order s0-invariances, corresponding to invariances with respect to transformations of the time-derivatives of the input, and encompassing LTI systems with zeros of multiplicity two or higher. Finally, we show that nth-order 0-invariant systems realize – under mild conditions – nth-order nonlinear differential operators: when excited by an input of a characteristic functional form, the system’s output converges to a constant value only depending on the nth (nonlinear) derivative of the input. AU - Lang, Moritz AU - Sontag, Eduardo ID - 1007 JF - Automatica SN - 0005-1098 TI - Zeros of nonlinear systems with input invariances VL - 81C ER - TY - DATA AB - This data was collected as part of the study [1]. It consists of preprocessed multi-electrode array recording from 160 salamander retinal ganglion cells responding to 297 repeats of a 19 s natural movie. The data is available in two formats: (1) a .mat file containing an array with dimensions “number of repeats” x “number of neurons” x “time in a repeat”; (2) a zipped .txt file containing the same data represented as an array with dimensions “number of neurons” x “number of samples”, where the number of samples is equal to the product of the number of repeats and timebins within a repeat. The time dimension is divided into 20 ms time windows, and the array is binary indicating whether a given cell elicited at least one spike in a given time window during a particular repeat. See the reference below for details regarding collection and preprocessing: [1] Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry MJ II. Searching for Collective Behavior in a Large Network of Sensory Neurons. PLoS Comput Biol. 2014;10(1):e1003408. AU - Marre, Olivier AU - Tkacik, Gasper AU - Amodei, Dario AU - Schneidman, Elad AU - Bialek, William AU - Berry, Michael ID - 5562 KW - multi-electrode recording KW - retinal ganglion cells TI - Multi-electrode array recording from salamander retinal ganglion cells ER - TY - DATA AB - This repository contains the data collected for the manuscript "Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity". The data is compressed into a single archive. Within the archive, different folders correspond to figures of the main text and the SI of the related publication. Data is saved as plain text, with each folder containing a separate readme file describing the format. Typically, the data is from fluorescence microscopy measurements of single cells growing in a microfluidic "mother machine" device, and consists of relevant values (primarily arbitrary unit or normalized fluorescence measurements, and division times / growth rates) after raw microscopy images have been processed, segmented, and their features extracted, as described in the methods section of the related publication. AU - Bergmiller, Tobias AU - Andersson, Anna M AU - Tomasek, Kathrin AU - Balleza, Enrique AU - Kiviet, Daniel AU - Hauschild, Robert AU - Tkacik, Gasper AU - Guet, Calin C ID - 5560 KW - single cell microscopy KW - mother machine microfluidic device KW - AcrAB-TolC pump KW - multi-drug efflux KW - Escherichia coli TI - Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity ER - TY - JOUR AB - The molecular mechanisms underlying phenotypic variation in isogenic bacterial populations remain poorly understood.We report that AcrAB-TolC, the main multidrug efflux pump of Escherichia coli, exhibits a strong partitioning bias for old cell poles by a segregation mechanism that is mediated by ternary AcrAB-TolC complex formation. Mother cells inheriting old poles are phenotypically distinct and display increased drug efflux activity relative to daughters. Consequently, we find systematic and long-lived growth differences between mother and daughter cells in the presence of subinhibitory drug concentrations. A simple model for biased partitioning predicts a population structure of long-lived and highly heterogeneous phenotypes. This straightforward mechanism of generating sustained growth rate differences at subinhibitory antibiotic concentrations has implications for understanding the emergence of multidrug resistance in bacteria. AU - Bergmiller, Tobias AU - Andersson, Anna M AU - Tomasek, Kathrin AU - Balleza, Enrique AU - Kiviet, Daniel AU - Hauschild, Robert AU - Tkacik, Gasper AU - Guet, Calin C ID - 665 IS - 6335 JF - Science SN - 00368075 TI - Biased partitioning of the multidrug efflux pump AcrAB TolC underlies long lived phenotypic heterogeneity VL - 356 ER - TY - JOUR AB - Cell-cell contact formation constitutes an essential step in evolution, leading to the differentiation of specialized cell types. However, remarkably little is known about whether and how the interplay between contact formation and fate specification affects development. Here, we identify a positive feedback loop between cell-cell contact duration, morphogen signaling, and mesendoderm cell-fate specification during zebrafish gastrulation. We show that long-lasting cell-cell contacts enhance the competence of prechordal plate (ppl) progenitor cells to respond to Nodal signaling, required for ppl cell-fate specification. We further show that Nodal signaling promotes ppl cell-cell contact duration, generating a positive feedback loop between ppl cell-cell contact duration and cell-fate specification. Finally, by combining mathematical modeling and experimentation, we show that this feedback determines whether anterior axial mesendoderm cells become ppl or, instead, turn into endoderm. Thus, the interdependent activities of cell-cell signaling and contact formation control fate diversification within the developing embryo. AU - Barone, Vanessa AU - Lang, Moritz AU - Krens, Gabriel AU - Pradhan, Saurabh AU - Shamipour, Shayan AU - Sako, Keisuke AU - Sikora, Mateusz K AU - Guet, Calin C AU - Heisenberg, Carl-Philipp J ID - 735 IS - 2 JF - Developmental Cell SN - 15345807 TI - An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate VL - 43 ER - TY - CONF AB - In many applications, it is desirable to extract only the relevant aspects of data. A principled way to do this is the information bottleneck (IB) method, where one seeks a code that maximises information about a relevance variable, Y, while constraining the information encoded about the original data, X. Unfortunately however, the IB method is computationally demanding when data are high-dimensional and/or non-gaussian. Here we propose an approximate variational scheme for maximising a lower bound on the IB objective, analogous to variational EM. Using this method, we derive an IB algorithm to recover features that are both relevant and sparse. Finally, we demonstrate how kernelised versions of the algorithm can be used to address a broad range of problems with non-linear relation between X and Y. AU - Chalk, Matthew J AU - Marre, Olivier AU - Tkacik, Gasper ID - 1082 TI - Relevant sparse codes with variational information bottleneck VL - 29 ER - TY - CONF AB - Jointly characterizing neural responses in terms of several external variables promises novel insights into circuit function, but remains computationally prohibitive in practice. Here we use gaussian process (GP) priors and exploit recent advances in fast GP inference and learning based on Kronecker methods, to efficiently estimate multidimensional nonlinear tuning functions. Our estimator require considerably less data than traditional methods and further provides principled uncertainty estimates. We apply these tools to hippocampal recordings during open field exploration and use them to characterize the joint dependence of CA1 responses on the position of the animal and several other variables, including the animal\'s speed, direction of motion, and network oscillations.Our results provide an unprecedentedly detailed quantification of the tuning of hippocampal neurons. The model\'s generality suggests that our approach can be used to estimate neural response properties in other brain regions. AU - Savin, Cristina AU - Tkacik, Gasper ID - 1105 TI - Estimating nonlinear neural response functions using GP priors and Kronecker methods VL - 29 ER - TY - JOUR AB - The increasing complexity of dynamic models in systems and synthetic biology poses computational challenges especially for the identification of model parameters. While modularization of the corresponding optimization problems could help reduce the “curse of dimensionality,” abundant feedback and crosstalk mechanisms prohibit a simple decomposition of most biomolecular networks into subnetworks, or modules. Drawing on ideas from network modularization and multiple-shooting optimization, we present here a modular parameter identification approach that explicitly allows for such interdependencies. Interfaces between our modules are given by the experimentally measured molecular species. This definition allows deriving good (initial) estimates for the inter-module communication directly from the experimental data. Given these estimates, the states and parameter sensitivities of different modules can be integrated independently. To achieve consistency between modules, we iteratively adjust the estimates for inter-module communication while optimizing the parameters. After convergence to an optimal parameter set---but not during earlier iterations---the intermodule communication as well as the individual modules\' state dynamics agree with the dynamics of the nonmodularized network. Our modular parameter identification approach allows for easy parallelization; it can reduce the computational complexity for larger networks and decrease the probability to converge to suboptimal local minima. We demonstrate the algorithm\'s performance in parameter estimation for two biomolecular networks, a synthetic genetic oscillator and a mammalian signaling pathway. AU - Lang, Moritz AU - Stelling, Jörg ID - 1170 IS - 6 JF - SIAM Journal on Scientific Computing TI - Modular parameter identification of biomolecular networks VL - 38 ER - TY - JOUR AU - Tkacik, Gasper ID - 1171 JF - Physics of Life Reviews TI - Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. VL - 17 ER - TY - JOUR AB - We consider a population dynamics model coupling cell growth to a diffusion in the space of metabolic phenotypes as it can be obtained from realistic constraints-based modelling. In the asymptotic regime of slow diffusion, that coincides with the relevant experimental range, the resulting non-linear Fokker–Planck equation is solved for the steady state in the WKB approximation that maps it into the ground state of a quantum particle in an Airy potential plus a centrifugal term. We retrieve scaling laws for growth rate fluctuations and time response with respect to the distance from the maximum growth rate suggesting that suboptimal populations can have a faster response to perturbations. AU - De Martino, Daniele AU - Masoero, Davide ID - 1188 IS - 12 JF - Journal of Statistical Mechanics: Theory and Experiment TI - Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth VL - 2016 ER - TY - JOUR AB - Haemophilus haemolyticus has been recently discovered to have the potential to cause invasive disease. It is closely related to nontypeable Haemophilus influenzae (NT H. influenzae). NT H. influenzae and H. haemolyticus are often misidentified because none of the existing tests targeting the known phenotypes of H. haemolyticus are able to specifically identify H. haemolyticus. Through comparative genomic analysis of H. haemolyticus and NT H. influenzae, we identified genes unique to H. haemolyticus that can be used as targets for the identification of H. haemolyticus. A real-time PCR targeting purT (encoding phosphoribosylglycinamide formyltransferase 2 in the purine synthesis pathway) was developed and evaluated. The lower limit of detection was 40 genomes/PCR; the sensitivity and specificity in detecting H. haemolyticus were 98.9% and 97%, respectively. To improve the discrimination of H. haemolyticus and NT H. influenzae, a testing scheme combining two targets (H. haemolyticus purT and H. influenzae hpd, encoding protein D lipoprotein) was also evaluated and showed 96.7% sensitivity and 98.2% specificity for the identification of H. haemolyticus and 92.8% sensitivity and 100% specificity for the identification of H. influenzae, respectively. The dual-target testing scheme can be used for the diagnosis and surveillance of infection and disease caused by H. haemolyticus and NT H. influenzae. AU - Hu, Fang AU - Rishishwar, Lavanya AU - Sivadas, Ambily AU - Mitchell, Gabriel AU - King, Jordan AU - Murphy, Timothy AU - Gilsdorf, Janet AU - Mayer, Leonard AU - Wang, Xin ID - 1203 IS - 12 JF - Journal of Clinical Microbiology TI - Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination VL - 54 ER - TY - CONF AB - With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. While very successful with classical robots, these methods run into severe difficulties when applied to soft robots, a new field of robotics with large interest for human-robot interaction. We claim that a novel controller paradigm opens new perspective for this field. This paper applies a recently developed neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently develops to rotate it. In this way, the robot discovers affordances of objects its body is interacting with. AU - Martius, Georg S AU - Hostettler, Raphael AU - Knoll, Alois AU - Der, Ralf ID - 1214 TI - Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm VL - 2016-November ER - TY - CONF AB - Theoretical and numerical aspects of aerodynamic efficiency of propulsion systems coupled to the boundary layer of a fuselage are studied. We discuss the effects of local flow fields, which are affected both by conservative flow acceleration as well as total pressure losses, on the efficiency of boundary layer immersed propulsion devices. We introduce the concept of a boundary layer retardation turbine that helps reduce skin friction over the fuselage. We numerically investigate efficiency gains offered by boundary layer and wake interacting devices. We discuss the results in terms of a total energy consumption framework and show that efficiency gains of any device depend on all the other elements of the propulsion system. AU - Mikić, Gregor AU - Stoll, Alex AU - Bevirt, Joe AU - Grah, Rok AU - Moore, Mark ID - 1220 TI - Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency ER - TY - JOUR AB - A crucial step in the regulation of gene expression is binding of transcription factor (TF) proteins to regulatory sites along the DNA. But transcription factors act at nanomolar concentrations, and noise due to random arrival of these molecules at their binding sites can severely limit the precision of regulation. Recent work on the optimization of information flow through regulatory networks indicates that the lower end of the dynamic range of concentrations is simply inaccessible, overwhelmed by the impact of this noise. Motivated by the behavior of homeodomain proteins, such as the maternal morphogen Bicoid in the fruit fly embryo, we suggest a scheme in which transcription factors also act as indirect translational regulators, binding to the mRNA of other regulatory proteins. Intuitively, each mRNA molecule acts as an independent sensor of the input concentration, and averaging over these multiple sensors reduces the noise. We analyze information flow through this scheme and identify conditions under which it outperforms direct transcriptional regulation. Our results suggest that the dual role of homeodomain proteins is not just a historical accident, but a solution to a crucial physics problem in the regulation of gene expression. AU - Sokolowski, Thomas R AU - Walczak, Aleksandra AU - Bialek, William AU - Tkacik, Gasper ID - 1242 IS - 2 JF - Physical Review E Statistical Nonlinear and Soft Matter Physics TI - Extending the dynamic range of transcription factor action by translational regulation VL - 93 ER - TY - JOUR AB - Cell polarity refers to a functional spatial organization of proteins that is crucial for the control of essential cellular processes such as growth and division. To establish polarity, cells rely on elaborate regulation networks that control the distribution of proteins at the cell membrane. In fission yeast cells, a microtubule-dependent network has been identified that polarizes the distribution of signaling proteins that restricts growth to cell ends and targets the cytokinetic machinery to the middle of the cell. Although many molecular components have been shown to play a role in this network, it remains unknown which molecular functionalities are minimally required to establish a polarized protein distribution in this system. Here we show that a membrane-binding protein fragment, which distributes homogeneously in wild-type fission yeast cells, can be made to concentrate at cell ends by attaching it to a cytoplasmic microtubule end-binding protein. This concentration results in a polarized pattern of chimera proteins with a spatial extension that is very reminiscent of natural polarity patterns in fission yeast. However, chimera levels fluctuate in response to microtubule dynamics, and disruption of microtubules leads to disappearance of the pattern. Numerical simulations confirm that the combined functionality of membrane anchoring and microtubule tip affinity is in principle sufficient to create polarized patterns. Our chimera protein may thus represent a simple molecular functionality that is able to polarize the membrane, onto which additional layers of molecular complexity may be built to provide the temporal robustness that is typical of natural polarity patterns. AU - Recouvreux, Pierre AU - Sokolowski, Thomas R AU - Grammoustianou, Aristea AU - Tenwolde, Pieter AU - Dogterom, Marileen ID - 1244 IS - 7 JF - PNAS TI - Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells VL - 113 ER - TY - JOUR AB - Life depends as much on the flow of information as on the flow of energy. Here we review the many efforts to make this intuition precise. Starting with the building blocks of information theory, we explore examples where it has been possible to measure, directly, the flow of information in biological networks, or more generally where information-theoretic ideas have been used to guide the analysis of experiments. Systems of interest range from single molecules (the sequence diversity in families of proteins) to groups of organisms (the distribution of velocities in flocks of birds), and all scales in between. Many of these analyses are motivated by the idea that biological systems may have evolved to optimize the gathering and representation of information, and we review the experimental evidence for this optimization, again across a wide range of scales. AU - Tkacik, Gasper AU - Bialek, William ID - 1248 JF - Annual Review of Condensed Matter Physics TI - Information processing in living systems VL - 7 ER - TY - JOUR AB - In this work, the Gardner problem of inferring interactions and fields for an Ising neural network from given patterns under a local stability hypothesis is addressed under a dual perspective. By means of duality arguments, an integer linear system is defined whose solution space is the dual of the Gardner space and whose solutions represent mutually unstable patterns. We propose and discuss Monte Carlo methods in order to find and remove unstable patterns and uniformly sample the space of interactions thereafter. We illustrate the problem on a set of real data and perform ensemble calculation that shows how the emergence of phase dominated by unstable patterns can be triggered in a nonlinear discontinuous way. AU - De Martino, Daniele ID - 1260 IS - 6 JF - International Journal of Modern Physics C TI - The dual of the space of interactions in neural network models VL - 27 ER - TY - JOUR AB - Cortical networks exhibit ‘global oscillations’, in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a ‘prediction error’ while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code. AU - Chalk, Matthew J AU - Gutkin, Boris AU - Denève, Sophie ID - 1266 IS - 2016JULY JF - eLife TI - Neural oscillations as a signature of efficient coding in the presence of synaptic delays VL - 5 ER - TY - JOUR AB - We developed a competition-based screening strategy to identify compounds that invert the selective advantage of antibiotic resistance. Using our assay, we screened over 19,000 compounds for the ability to select against the TetA tetracycline-resistance efflux pump in Escherichia coli and identified two hits, β-thujaplicin and disulfiram. Treating a tetracycline-resistant population with β-thujaplicin selects for loss of the resistance gene, enabling an effective second-phase treatment with doxycycline. AU - Stone, Laura AU - Baym, Michael AU - Lieberman, Tami AU - Chait, Remy P AU - Clardy, Jon AU - Kishony, Roy ID - 1290 IS - 11 JF - Nature Chemical Biology TI - Compounds that select against the tetracycline-resistance efflux pump VL - 12 ER -