@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 = {24700045}, 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{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 = {00368075}, 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{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 = {17425468}, journal = { Journal of Statistical Mechanics: Theory and Experiment}, number = {9}, publisher = {IOPscience}, title = {{Phase transitions in integer linear problems}}, doi = {10.1088/1742-5468/aa85c3}, volume = {2017}, 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 = {09594388}, 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{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}, } @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{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 = {00368075}, 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{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 = {15345807}, 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}, } @inproceedings{1082, abstract = {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.}, author = {Chalk, Matthew J and Marre, Olivier and Tkacik, Gasper}, location = {Barcelona, Spain}, pages = {1965--1973}, publisher = {Neural Information Processing Systems}, title = {{Relevant sparse codes with variational information bottleneck}}, volume = {29}, year = {2016}, } @inproceedings{1105, abstract = {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.}, author = {Savin, Cristina and Tkacik, Gasper}, location = {Barcelona; Spain}, pages = {3610--3618}, publisher = {Neural Information Processing Systems}, title = {{Estimating nonlinear neural response functions using GP priors and Kronecker methods}}, volume = {29}, year = {2016}, }