@article{548, abstract = {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.}, author = {De Martino, Daniele}, issn = {2470-0045}, journal = {Physical Review E}, number = {6}, publisher = {American Physical Society}, title = {{Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes}}, doi = {10.1103/PhysRevE.96.060401}, volume = {96}, year = {2017}, } @misc{5560, abstract = {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.}, author = {Bergmiller, Tobias and Andersson, Anna M and Tomasek, Kathrin and Balleza, Enrique and Kiviet, Daniel and Hauschild, Robert and Tkacik, Gasper and Guet, Calin C}, keywords = {single cell microscopy, mother machine microfluidic device, AcrAB-TolC pump, multi-drug efflux, Escherichia coli}, publisher = {Institute of Science and Technology Austria}, title = {{Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity}}, doi = {10.15479/AT:ISTA:53}, year = {2017}, } @misc{5562, abstract = {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.}, author = {Marre, Olivier and Tkacik, Gasper and Amodei, Dario and Schneidman, Elad and Bialek, William and Berry, Michael}, keywords = {multi-electrode recording, retinal ganglion cells}, publisher = {Institute of Science and Technology Austria}, title = {{Multi-electrode array recording from salamander retinal ganglion cells}}, doi = {10.15479/AT:ISTA:61}, year = {2017}, } @article{613, abstract = {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.}, author = {Chait, Remy P and Ruess, Jakob and Bergmiller, Tobias and Tkacik, Gasper and Guet, Calin C}, issn = {2041-1723}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, title = {{Shaping bacterial population behavior through computer interfaced control of individual cells}}, doi = {10.1038/s41467-017-01683-1}, volume = {8}, year = {2017}, } @inproceedings{652, abstract = {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.}, author = {Der, Ralf and Martius, Georg S}, isbn = {978-150905069-7}, location = {Cergy-Pontoise, France}, publisher = {IEEE}, title = {{Dynamical self consistency leads to behavioral development and emergent social interactions in robots}}, doi = {10.1109/DEVLRN.2016.7846789}, year = {2017}, } @article{658, abstract = {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.}, author = {Der, Ralf and Martius, Georg S}, issn = {1662-5218}, journal = {Frontiers in Neurorobotics}, number = {MAR}, publisher = {Frontiers Research Foundation}, title = {{Self organized behavior generation for musculoskeletal robots}}, doi = {10.3389/fnbot.2017.00008}, volume = {11}, year = {2017}, } @article{665, abstract = {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.}, author = {Bergmiller, Tobias and Andersson, Anna M and Tomasek, Kathrin and Balleza, Enrique and Kiviet, Daniel and Hauschild, Robert and Tkacik, Gasper and Guet, Calin C}, issn = {0036-8075}, journal = {Science}, number = {6335}, pages = {311 -- 315}, publisher = {American Association for the Advancement of Science}, title = {{Biased partitioning of the multidrug efflux pump AcrAB TolC underlies long lived phenotypic heterogeneity}}, doi = {10.1126/science.aaf4762}, volume = {356}, year = {2017}, } @article{680, abstract = {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.}, author = {Chalk, Matthew J and Masset, Paul and Gutkin, Boris and Denève, Sophie}, issn = {1553-734X}, journal = {PLoS Computational Biology}, number = {6}, publisher = {Public Library of Science}, title = {{Sensory noise predicts divisive reshaping of receptive fields}}, doi = {10.1371/journal.pcbi.1005582}, volume = {13}, year = {2017}, } @article{720, abstract = {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.}, author = {Humplik, Jan and Tkacik, Gasper}, issn = {1553-734X}, journal = {PLoS Computational Biology}, number = {9}, publisher = {Public Library of Science}, title = {{Probabilistic models for neural populations that naturally capture global coupling and criticality}}, doi = {10.1371/journal.pcbi.1005763}, volume = {13}, year = {2017}, } @article{725, abstract = {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.}, author = {Harpaz, Roy and Tkacik, Gasper and Schneidman, Elad}, issn = {0027-8424}, journal = {PNAS}, number = {38}, pages = {10149 -- 10154}, publisher = {National Academy of Sciences}, title = {{Discrete modes of social information processing predict individual behavior of fish in a group}}, doi = {10.1073/pnas.1703817114}, volume = {114}, year = {2017}, } @misc{9855, abstract = {Includes derivation of optimal estimation algorithm, generalisation to non-poisson noise statistics, correlated input noise, and implementation of in a multi-layer neural network.}, author = {Chalk, Matthew J and Masset, Paul and Gutkin, Boris and Denève, Sophie}, publisher = {Public Library of Science}, title = {{Supplementary appendix}}, doi = {10.1371/journal.pcbi.1005582.s001}, year = {2017}, } @article{993, abstract = {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.}, author = {Levina (Martius), Anna and Priesemann, Viola}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Nature Publishing Group}, title = {{Subsampling scaling}}, doi = {10.1038/ncomms15140}, volume = {8}, year = {2017}, } @article{955, abstract = {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.}, author = {Friedlander, Tamar and Prizak, Roshan and Barton, Nicholas H and Tkacik, Gasper}, issn = {2041-1723}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, title = {{Evolution of new regulatory functions on biophysically realistic fitness landscapes}}, doi = {10.1038/s41467-017-00238-8}, volume = {8}, year = {2017}, } @article{2016, abstract = {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.}, author = {Martin Del Campo Sanchez, Abraham and Cepeda Humerez, Sarah A and Uhler, Caroline}, issn = {03036898}, journal = {Scandinavian Journal of Statistics}, number = {2}, pages = {285 -- 306}, publisher = {Wiley-Blackwell}, title = {{Exact goodness-of-fit testing for the Ising model}}, doi = {10.1111/sjos.12251}, volume = {44}, year = {2017}, } @article{666, abstract = {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.}, author = {Mitosch, Karin and Rieckh, Georg and Bollenbach, Tobias}, issn = {2405-4712}, journal = {Cell Systems}, number = {4}, pages = {393 -- 403}, publisher = {Cell Press}, title = {{Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment}}, doi = {10.1016/j.cels.2017.03.001}, volume = {4}, year = {2017}, } @article{730, abstract = {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.}, author = {Savin, Cristina and Tkacik, Gasper}, issn = {0959-4388}, journal = {Current Opinion in Neurobiology}, pages = {120 -- 126}, publisher = {Elsevier}, title = {{Maximum entropy models as a tool for building precise neural controls}}, doi = {10.1016/j.conb.2017.08.001}, volume = {46}, year = {2017}, } @article{735, abstract = {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.}, author = {Barone, Vanessa and Lang, Moritz and Krens, Gabriel and Pradhan, Saurabh and Shamipour, Shayan and Sako, Keisuke and Sikora, Mateusz K and Guet, Calin C and Heisenberg, Carl-Philipp J}, issn = {1534-5807}, journal = {Developmental Cell}, number = {2}, pages = {198 -- 211}, publisher = {Cell Press}, title = {{An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate}}, doi = {10.1016/j.devcel.2017.09.014}, volume = {43}, year = {2017}, } @article{1148, abstract = {Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space. © 2016 Elsevier Ireland Ltd}, author = {Schilling, Christian and Bogomolov, Sergiy and Henzinger, Thomas A and Podelski, Andreas and Ruess, Jakob}, journal = {Biosystems}, pages = {15 -- 25}, publisher = {Elsevier}, title = {{Adaptive moment closure for parameter inference of biochemical reaction networks}}, doi = {10.1016/j.biosystems.2016.07.005}, volume = {149}, year = {2016}, } @article{1170, abstract = {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.}, author = {Lang, Moritz and Stelling, Jörg}, journal = {SIAM Journal on Scientific Computing}, number = {6}, pages = {B988 -- B1008}, publisher = {Society for Industrial and Applied Mathematics }, title = {{Modular parameter identification of biomolecular networks}}, doi = {10.1137/15M103306X}, volume = {38}, year = {2016}, } @article{1171, author = {Tkacik, Gasper}, journal = {Physics of Life Reviews}, pages = {166 -- 167}, publisher = {Elsevier}, title = {{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.}}, doi = {10.1016/j.plrev.2016.06.005}, volume = {17}, year = {2016}, }