@article{19, abstract = {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.}, author = {Palmer, Adam and Chait, Remy P and Kishony, Roy}, issn = {0737-4038}, journal = {Molecular Biology and Evolution}, number = {11}, pages = {2669 -- 2684}, publisher = {Oxford University Press}, title = {{Nonoptimal gene expression creates latent potential for antibiotic resistance}}, doi = {10.1093/molbev/msy163}, volume = {35}, year = {2018}, } @article{161, abstract = {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.}, author = {De Martino, Daniele and Mc, Andersson Anna and Bergmiller, Tobias and Guet, Calin C and Tkacik, Gasper}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, title = {{Statistical mechanics for metabolic networks during steady state growth}}, doi = {10.1038/s41467-018-05417-9}, volume = {9}, year = {2018}, } @misc{9813, abstract = {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.}, author = {Bod'ová, Katarína and Priklopil, Tadeas and Field, David and Barton, Nicholas H and Pickup, Melinda}, publisher = {Genetics Society of America}, title = {{Supplemental material for Bodova et al., 2018}}, doi = {10.25386/genetics.6148304.v1}, year = {2018}, } @article{543, abstract = {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.}, author = {Chalk, Matthew J and Marre, Olivier and Tkacik, Gasper}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {1}, pages = {186 -- 191}, publisher = {National Academy of Sciences}, title = {{Toward a unified theory of efficient, predictive, and sparse coding}}, doi = {10.1073/pnas.1711114115}, volume = {115}, year = {2018}, } @misc{5584, abstract = {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. }, author = {Deny, Stephane and Marre, Olivier and Botella-Soler, Vicente and Martius, Georg S and Tkacik, Gasper}, keywords = {retina, decoding, regression, neural networks, complex stimulus}, publisher = {Institute of Science and Technology Austria}, title = {{Nonlinear decoding of a complex movie from the mammalian retina}}, doi = {10.15479/AT:ISTA:98}, year = {2018}, } @misc{5587, abstract = {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).}, author = {De Martino, Daniele and Tkacik, Gasper}, keywords = {metabolic networks, e.coli core, maximum entropy, monte carlo markov chain sampling, ellipsoidal rounding}, publisher = {Institute of Science and Technology Austria}, title = {{Supporting materials "STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH"}}, doi = {10.15479/AT:ISTA:62}, year = {2018}, } @article{607, abstract = {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.}, author = {Bodova, Katarina and Haskovec, Jan and Markowich, Peter}, journal = {Physica D: Nonlinear Phenomena}, pages = {108--120}, publisher = {Elsevier}, title = {{Well posedness and maximum entropy approximation for the dynamics of quantitative traits}}, doi = {10.1016/j.physd.2017.10.015}, volume = {376-377}, year = {2018}, } @misc{9831, abstract = {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.}, author = {Bod’Ová, Katarína and Mitchell, Gabriel and Harpaz, Roy and Schneidman, Elad and Tkačik, Gašper}, publisher = {Public Library of Science}, title = {{Implementation of the inference method in Matlab}}, doi = {10.1371/journal.pone.0193049.s001}, year = {2018}, } @article{406, abstract = {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. }, author = {Bod’Ová, Katarína and Mitchell, Gabriel and Harpaz, Roy and Schneidman, Elad and Tkacik, Gasper}, journal = {PLoS One}, number = {3}, publisher = {Public Library of Science}, title = {{Probabilistic models of individual and collective animal behavior}}, doi = {10.1371/journal.pone.0193049}, volume = {13}, year = {2018}, } @article{281, abstract = {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.}, author = {Granados, Alejandro and Pietsch, Julian and Cepeda Humerez, Sarah A and Farquhar, Isebail and Tkacik, Gasper and Swain, Peter}, journal = {PNAS}, number = {23}, pages = {6088 -- 6093}, publisher = {National Academy of Sciences}, title = {{Distributed and dynamic intracellular organization of extracellular information}}, doi = {10.1073/pnas.1716659115}, volume = {115}, year = {2018}, } @article{457, abstract = {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}, author = {Pleska, Maros and Lang, Moritz and Refardt, Dominik and Levin, Bruce and Guet, Calin C}, journal = {Nature Ecology and Evolution}, number = {2}, pages = {359 -- 366}, publisher = {Springer Nature}, title = {{Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity}}, doi = {10.1038/s41559-017-0424-z}, volume = {2}, year = {2018}, } @article{67, abstract = {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.}, author = {Igler, Claudia and Lagator, Mato and Tkacik, Gasper and Bollback, Jonathan P and Guet, Calin C}, journal = {Nature Ecology and Evolution}, number = {10}, pages = {1633 -- 1643}, publisher = {Nature Publishing Group}, title = {{Evolutionary potential of transcription factors for gene regulatory rewiring}}, doi = {10.1038/s41559-018-0651-y}, volume = {2}, year = {2018}, } @misc{5585, abstract = {Mean repression values and standard error of the mean are given for all operator mutant libraries.}, author = {Igler, Claudia and Lagator, Mato and Tkacik, Gasper and Bollback, Jonathan P and Guet, Calin C}, publisher = {Institute of Science and Technology Austria}, title = {{Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring}}, doi = {10.15479/AT:ISTA:108}, year = {2018}, } @article{1104, abstract = {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.}, author = {Deny, Stephane and Ferrari, Ulisse and Mace, Emilie and Yger, Pierre and Caplette, Romain and Picaud, Serge and Tkacik, Gasper and Marre, Olivier}, issn = {2041-1723}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, title = {{Multiplexed computations in retinal ganglion cells of a single type}}, doi = {10.1038/s41467-017-02159-y}, volume = {8}, year = {2017}, } @article{1007, abstract = {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.}, author = {Lang, Moritz and Sontag, Eduardo}, issn = {0005-1098}, journal = {Automatica}, pages = {46 -- 55}, publisher = {International Federation of Automatic Control}, title = {{Zeros of nonlinear systems with input invariances}}, doi = {10.1016/j.automatica.2017.03.030}, volume = {81C}, year = {2017}, } @article{823, abstract = {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.}, author = {Colabrese, Simona and De Martino, Daniele and Leuzzi, Luca and Marinari, Enzo}, issn = {1742-5468}, journal = { Journal of Statistical Mechanics: Theory and Experiment}, number = {9}, publisher = {IOP Publishing}, title = {{Phase transitions in integer linear problems}}, doi = {10.1088/1742-5468/aa85c3}, volume = {2017}, year = {2017}, } @article{943, abstract = {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.}, author = {Zagórski, Marcin P and Tabata, Yoji and Brandenberg, Nathalie and Lutolf, Matthias and Tkacik, Gasper and Bollenbach, Tobias and Briscoe, James and Kicheva, Anna}, issn = {0036-8075}, journal = {Science}, number = {6345}, pages = {1379 -- 1383}, publisher = {American Association for the Advancement of Science}, title = {{Decoding of position in the developing neural tube from antiparallel morphogen gradients}}, doi = {10.1126/science.aam5887}, volume = {356}, year = {2017}, } @article{947, abstract = {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.}, author = {De Martino, Daniele and Capuani, Fabrizio and De Martino, Andrea}, issn = {2470-0045}, journal = { Physical Review E Statistical Nonlinear and Soft Matter Physics }, number = {1}, publisher = {American Institute of Physics}, title = {{Quantifying the entropic cost of cellular growth control}}, doi = {10.1103/PhysRevE.96.010401}, volume = {96}, year = {2017}, } @article{959, abstract = {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.}, author = {De Martino, Daniele}, issn = {2470-0045}, journal = { Physical Review E Statistical Nonlinear and Soft Matter Physics }, number = {6}, pages = {062419}, publisher = {American Institute of Physics}, title = {{Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics}}, doi = {10.1103/PhysRevE.95.062419}, volume = {95}, year = {2017}, } @misc{9709, abstract = {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.}, author = {Prentice, Jason and Marre, Olivier and Ioffe, Mark and Loback, Adrianna and Tkačik, Gašper and Berry, Michael}, publisher = {Dryad}, title = {{Data from: Error-robust modes of the retinal population code}}, doi = {10.5061/dryad.1f1rc}, year = {2017}, }