@article{1394, abstract = {The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coliʼs metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity.}, author = {De Martino, Daniele and Capuani, Fabrizio and De Martino, Andrea}, journal = {Physical Biology}, number = {3}, publisher = {IOP Publishing Ltd.}, title = {{Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli}}, doi = {10.1088/1478-3975/13/3/036005}, volume = {13}, year = {2016}, } @article{1420, abstract = {Selection, mutation, and random drift affect the dynamics of allele frequencies and consequently of quantitative traits. While the macroscopic dynamics of quantitative traits can be measured, the underlying allele frequencies are typically unobserved. Can we understand how the macroscopic observables evolve without following these microscopic processes? This problem has been studied previously by analogy with statistical mechanics: the allele frequency distribution at each time point is approximated by the stationary form, which maximizes entropy. We explore the limitations of this method when mutation is small (4Nμ < 1) so that populations are typically close to fixation, and we extend the theory in this regime to account for changes in mutation strength. We consider a single diallelic locus either under directional selection or with overdominance and then generalize to multiple unlinked biallelic loci with unequal effects. We find that the maximum-entropy approximation is remarkably accurate, even when mutation and selection change rapidly. }, author = {Bod'ová, Katarína and Tkacik, Gasper and Barton, Nicholas H}, journal = {Genetics}, number = {4}, pages = {1523 -- 1548}, publisher = {Genetics Society of America}, title = {{A general approximation for the dynamics of quantitative traits}}, doi = {10.1534/genetics.115.184127}, volume = {202}, year = {2016}, } @article{1485, abstract = {In this article the notion of metabolic turnover is revisited in the light of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo methods we perform an exact sampling of the enzymatic fluxes in a genome scale metabolic network of E. Coli in stationary growth conditions from which we infer the metabolites turnover times. However the latter are inferred from net fluxes, and we argue that this approximation is not valid for enzymes working nearby thermodynamic equilibrium. We recalculate turnover times from total fluxes by performing an energy balance analysis of the network and recurring to the fluctuation theorem. We find in many cases values one of order of magnitude lower, implying a faster picture of intermediate metabolism.}, author = {De Martino, Daniele}, journal = {Physical Biology}, number = {1}, publisher = {IOP Publishing Ltd.}, title = {{Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis}}, doi = {10.1088/1478-3975/13/1/016003}, volume = {13}, year = {2016}, } @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}, } @inproceedings{8094, abstract = {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. The idea is that the controller controls the world---the body plus its environment---as reliably as possible. This paper focuses on new lines of self-organization for developmental robotics. We apply the recently developed 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 discovers how to rotate it. In this way, the robot discovers affordances of objects its body is interacting with.}, author = {Martius, Georg S and Hostettler, Rafael and Knoll, Alois and Der, Ralf}, booktitle = {Proceedings of the Artificial Life Conference 2016}, isbn = {9780262339360}, location = {Cancun, Mexico}, pages = {142--143}, publisher = {MIT Press}, title = {{Self-organized control of an tendon driven arm by differential extrinsic plasticity}}, doi = {10.7551/978-0-262-33936-0-ch029}, volume = {28}, year = {2016}, } @article{1197, 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 Tkacik, Gasper and Berry, Michael}, journal = {PLoS Computational Biology}, number = {11}, publisher = {Public Library of Science}, title = {{Error-robust modes of the retinal population code}}, doi = {10.1371/journal.pcbi.1005148}, volume = {12}, year = {2016}, } @inproceedings{948, abstract = {Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations.}, author = {Monk, Travis and Savin, Cristina and Lücke, Jörg}, location = {Barcelona, Spaine}, pages = {4285 -- 4293}, publisher = {Neural Information Processing Systems}, title = {{Neurons equipped with intrinsic plasticity learn stimulus intensity statistics}}, volume = {29}, year = {2016}, } @article{1270, abstract = {A crucial step in the early development of multicellular organisms involves the establishment of spatial patterns of gene expression which later direct proliferating cells to take on different cell fates. These patterns enable the cells to infer their global position within a tissue or an organism by reading out local gene expression levels. The patterning system is thus said to encode positional information, a concept that was formalized recently in the framework of information theory. Here we introduce a toy model of patterning in one spatial dimension, which can be seen as an extension of Wolpert's paradigmatic "French Flag" model, to patterning by several interacting, spatially coupled genes subject to intrinsic and extrinsic noise. Our model, a variant of an Ising spin system, allows us to systematically explore expression patterns that optimally encode positional information. We find that optimal patterning systems use positional cues, as in the French Flag model, together with gene-gene interactions to generate combinatorial codes for position which we call "Counter" patterns. Counter patterns can also be stabilized against noise and variations in system size or morphogen dosage by longer-range spatial interactions of the type invoked in the Turing model. The simple setup proposed here qualitatively captures many of the experimentally observed properties of biological patterning systems and allows them to be studied in a single, theoretically consistent framework.}, author = {Hillenbrand, Patrick and Gerland, Ulrich and Tkacik, Gasper}, journal = {PLoS One}, number = {9}, publisher = {Public Library of Science}, title = {{Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information}}, doi = {10.1371/journal.pone.0163628}, volume = {11}, year = {2016}, } @misc{9870, abstract = {The effect of noise in the input field on an Ising model is approximated. Furthermore, methods to compute positional information in an Ising model by transfer matrices and Monte Carlo sampling are outlined.}, author = {Hillenbrand, Patrick and Gerland, Ulrich and Tkačik, Gašper}, publisher = {Public Library of Science}, title = {{Computation of positional information in an Ising model}}, doi = {10.1371/journal.pone.0163628.s002}, year = {2016}, } @misc{9869, abstract = {A lower bound on the error of a positional estimator with limited positional information is derived.}, author = {Hillenbrand, Patrick and Gerland, Ulrich and Tkačik, Gašper}, publisher = {Public Library of Science}, title = {{Error bound on an estimator of position}}, doi = {10.1371/journal.pone.0163628.s001}, year = {2016}, }