@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},
}
@article{1290,
abstract = {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.},
author = {Stone, Laura and Baym, Michael and Lieberman, Tami and Chait, Remy P and Clardy, Jon and Kishony, Roy},
journal = {Nature Chemical Biology},
number = {11},
pages = {902 -- 904},
publisher = {Nature Publishing Group},
title = {{Compounds that select against the tetracycline-resistance efflux pump}},
doi = {10.1038/nchembio.2176},
volume = {12},
year = {2016},
}
@inproceedings{1320,
abstract = {In recent years, several biomolecular systems have been shown to be scale-invariant (SI), i.e. to show the same output dynamics when exposed to geometrically scaled input signals (u → pu, p > 0) after pre-adaptation to accordingly scaled constant inputs. In this article, we show that SI systems-as well as systems invariant with respect to other input transformations-can realize nonlinear differential operators: when excited by inputs obeying functional forms characteristic for a given class of invariant systems, the systems' outputs converge to constant values directly quantifying the speed of the input.},
author = {Lang, Moritz and Sontag, Eduardo},
location = {Boston, MA, USA},
publisher = {IEEE},
title = {{Scale-invariant systems realize nonlinear differential operators}},
doi = {10.1109/ACC.2016.7526722},
volume = {2016-July},
year = {2016},
}
@article{1332,
abstract = {Antibiotic-sensitive and -resistant bacteria coexist in natural environments with low, if detectable, antibiotic concentrations. Except possibly around localized antibiotic sources, where resistance can provide a strong advantage, bacterial fitness is dominated by stresses unaffected by resistance to the antibiotic. How do such mixed and heterogeneous conditions influence the selective advantage or disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels of tetracyclines potentiate selection for or against tetracycline resistance around localized sources of almost any toxin or stress. Furthermore, certain stresses generate alternating rings of selection for and against resistance around a localized source of the antibiotic. In these conditions, localized antibiotic sources, even at high strengths, can actually produce a net selection against resistance to the antibiotic. Our results show that interactions between the effects of an antibiotic and other stresses in inhomogeneous environments can generate pervasive, complex patterns of selection both for and against antibiotic resistance.},
author = {Chait, Remy P and Palmer, Adam and Yelin, Idan and Kishony, Roy},
journal = {Nature Communications},
publisher = {Nature Publishing Group},
title = {{Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments}},
doi = {10.1038/ncomms10333},
volume = {7},
year = {2016},
}
@article{1342,
abstract = {A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front.While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front,we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate provides a versatile platformfor studying microbial adaption and directly visualizing evolutionary dynamics.},
author = {Baym, Michael and Lieberman, Tami and Kelsic, Eric and Chait, Remy P and Gross, Rotem and Yelin, Idan and Kishony, Roy},
journal = {Science},
number = {6304},
pages = {1147 -- 1151},
publisher = {American Association for the Advancement of Science},
title = {{Spatiotemporal microbial evolution on antibiotic landscapes}},
doi = {10.1126/science.aag0822},
volume = {353},
year = {2016},
}
@article{1358,
abstract = {Gene regulation relies on the specificity of transcription factor (TF)–DNA interactions. Limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF–DNA interactions or remains erroneously inactive. As each TF can have numerous interactions with noncognate cis-regulatory elements, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyse the effects of global crosstalk on gene regulation. We find that crosstalk presents a significant challenge for organisms with low-specificity TFs, such as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting at equilibrium, including variants of cooperativity and combinatorial regulation. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements.},
author = {Friedlander, Tamar and Prizak, Roshan and Guet, Calin C and Barton, Nicholas H and Tkacik, Gasper},
journal = {Nature Communications},
publisher = {Nature Publishing Group},
title = {{Intrinsic limits to gene regulation by global crosstalk}},
doi = {10.1038/ncomms12307},
volume = {7},
year = {2016},
}
@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},
}
@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},
}
@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},
}
@phdthesis{1128,
abstract = {The process of gene expression is central to the modern understanding of how cellular systems
function. In this process, a special kind of regulatory proteins, called transcription factors,
are important to determine how much protein is produced from a given gene. As biological
information is transmitted from transcription factor concentration to mRNA levels to amounts of
protein, various sources of noise arise and pose limits to the fidelity of intracellular signaling.
This thesis concerns itself with several aspects of stochastic gene expression: (i) the mathematical
description of complex promoters responsible for the stochastic production of biomolecules,
(ii) fundamental limits to information processing the cell faces due to the interference from multiple
fluctuating signals, (iii) how the presence of gene expression noise influences the evolution
of regulatory sequences, (iv) and tools for the experimental study of origins and consequences
of cell-cell heterogeneity, including an application to bacterial stress response systems.},
author = {Rieckh, Georg},
pages = {114},
publisher = {IST Austria},
title = {{Studying the complexities of transcriptional regulation}},
year = {2016},
}
@article{1564,
author = {Gilson, Matthieu and Savin, Cristina and Zenke, Friedemann},
journal = {Frontiers in Computational Neuroscience},
number = {11},
publisher = {Frontiers Research Foundation},
title = {{Editorial: Emergent neural computation from the interaction of different forms of plasticity}},
doi = {10.3389/fncom.2015.00145},
volume = {9},
year = {2015},
}
@article{1570,
abstract = {Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no systemspecific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking,which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.},
author = {Der, Ralf and Martius, Georg S},
journal = {PNAS},
number = {45},
pages = {E6224 -- E6232},
publisher = {National Academy of Sciences},
title = {{Novel plasticity rule can explain the development of sensorimotor intelligence}},
doi = {10.1073/pnas.1508400112},
volume = {112},
year = {2015},
}
@article{1576,
abstract = {Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory crosstalk: the possibility that noncognate TFs could initiate transcription, with potentially disastrous effects for the cell. Here, we estimate the importance of crosstalk, suggest that its avoidance strongly constrains equilibrium models of TF binding, and propose an alternative nonequilibrium scheme that implements kinetic proofreading to suppress erroneous initiation. This proposal is consistent with the observed covalent modifications of the transcriptional apparatus and predicts increased noise in gene expression as a trade-off for improved specificity. Using information theory, we quantify this trade-off to find when optimal proofreading architectures are favored over their equilibrium counterparts. Such architectures exhibit significant super-Poisson noise at low expression in steady state.},
author = {Cepeda Humerez, Sarah A and Rieckh, Georg and Tkacik, Gasper},
journal = {Physical Review Letters},
number = {24},
publisher = {American Physical Society},
title = {{Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation}},
doi = {10.1103/PhysRevLett.115.248101},
volume = {115},
year = {2015},
}
@article{1655,
abstract = {Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration.},
author = {Martius, Georg S and Olbrich, Eckehard},
journal = {Entropy},
number = {10},
pages = {7266 -- 7297},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Quantifying emergent behavior of autonomous robots}},
doi = {10.3390/e17107266},
volume = {17},
year = {2015},
}
@inproceedings{1658,
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.},
author = {Bogomolov, Sergiy and Henzinger, Thomas A and Podelski, Andreas and Ruess, Jakob and Schilling, Christian},
location = {Nantes, France},
pages = {77 -- 89},
publisher = {Springer},
title = {{Adaptive moment closure for parameter inference of biochemical reaction networks}},
doi = {10.1007/978-3-319-23401-4_8},
volume = {9308},
year = {2015},
}
@article{1666,
abstract = {Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite populations under both point and insertion/deletion mutations. Our results show that these rates are typically slow for a single TFBS in an isolated DNA region, unless the selection is extremely strong. These rates decrease drastically with increasing TFBS length or increasingly specific protein-DNA interactions, making the evolution of sites longer than ∼ 10 bp unlikely on typical eukaryotic speciation timescales. Similarly, evolution converges to the stationary distribution of binding sequences very slowly, making the equilibrium assumption questionable. The availability of longer regulatory sequences in which multiple binding sites can evolve simultaneously, the presence of “pre-sites” or partially decayed old sites in the initial sequence, and biophysical cooperativity between transcription factors, can all facilitate gain of TFBS and reconcile theoretical calculations with timescales inferred from comparative genomics.},
author = {Tugrul, Murat and Paixao, Tiago and Barton, Nicholas H and Tkacik, Gasper},
journal = {PLoS Genetics},
number = {11},
publisher = {Public Library of Science},
title = {{Dynamics of transcription factor binding site evolution}},
doi = {10.1371/journal.pgen.1005639},
volume = {11},
year = {2015},
}
@article{1697,
abstract = {Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.},
author = {Marre, Olivier and Botella Soler, Vicente and Simmons, Kristina and Mora, Thierry and Tkacik, Gasper and Berry, Michael},
journal = {PLoS Computational Biology},
number = {7},
publisher = {Public Library of Science},
title = {{High accuracy decoding of dynamical motion from a large retinal population}},
doi = {10.1371/journal.pcbi.1004304},
volume = {11},
year = {2015},
}
@article{1701,
abstract = {The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. },
author = {Tkacik, Gasper and Mora, Thierry and Marre, Olivier and Amodei, Dario and Palmer, Stephanie and Berry Ii, Michael and Bialek, William},
journal = {PNAS},
number = {37},
pages = {11508 -- 11513},
publisher = {National Academy of Sciences},
title = {{Thermodynamics and signatures of criticality in a network of neurons}},
doi = {10.1073/pnas.1514188112},
volume = {112},
year = {2015},
}
@article{1827,
abstract = {Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved.},
author = {Friedlander, Tamar and Mayo, Avraham and Tlusty, Tsvi and Alon, Uri},
journal = {PLoS Computational Biology},
number = {3},
publisher = {Public Library of Science},
title = {{Evolution of bow-tie architectures in biology}},
doi = {10.1371/journal.pcbi.1004055},
volume = {11},
year = {2015},
}
@article{1861,
abstract = {Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.},
author = {Ruess, Jakob and Lygeros, John},
journal = {ACM Transactions on Modeling and Computer Simulation},
number = {2},
publisher = {ACM},
title = {{Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks}},
doi = {10.1145/2688906},
volume = {25},
year = {2015},
}
@article{1885,
abstract = {The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. },
author = {Tkacik, Gasper and Dubuis, Julien and Petkova, Mariela and Gregor, Thomas},
journal = {Genetics},
number = {1},
pages = {39 -- 59},
publisher = {Genetics Society of America},
title = {{Positional information, positional error, and readout precision in morphogenesis: A mathematical framework}},
doi = {10.1534/genetics.114.171850},
volume = {199},
year = {2015},
}
@article{1940,
abstract = {We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the "input") by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively new regulatory strategy emerges where individual cells respond to the input in a nearly step-like fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.},
author = {Sokolowski, Thomas R and Tkacik, Gasper},
journal = {Physical Review E Statistical Nonlinear and Soft Matter Physics},
number = {6},
publisher = {American Institute of Physics},
title = {{Optimizing information flow in small genetic networks. IV. Spatial coupling}},
doi = {10.1103/PhysRevE.91.062710},
volume = {91},
year = {2015},
}
@article{1538,
abstract = {Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.},
author = {Ruess, Jakob and Parise, Francesca and Milias Argeitis, Andreas and Khammash, Mustafa and Lygeros, John},
journal = {PNAS},
number = {26},
pages = {8148 -- 8153},
publisher = {National Academy of Sciences},
title = {{Iterative experiment design guides the characterization of a light-inducible gene expression circuit}},
doi = {10.1073/pnas.1423947112},
volume = {112},
year = {2015},
}
@article{1539,
abstract = {Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space. },
author = {Ruess, Jakob},
journal = {Journal of Chemical Physics},
number = {24},
publisher = {American Institute of Physics},
title = {{Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space}},
doi = {10.1063/1.4937937},
volume = {143},
year = {2015},
}
@article{2231,
abstract = {Based on the measurements of noise in gene expression performed during the past decade, it has become customary to think of gene regulation in terms of a two-state model, where the promoter of a gene can stochastically switch between an ON and an OFF state. As experiments are becoming increasingly precise and the deviations from the two-state model start to be observable, we ask about the experimental signatures of complex multistate promoters, as well as the functional consequences of this additional complexity. In detail, we i), extend the calculations for noise in gene expression to promoters described by state transition diagrams with multiple states, ii), systematically compute the experimentally accessible noise characteristics for these complex promoters, and iii), use information theory to evaluate the channel capacities of complex promoter architectures and compare them with the baseline provided by the two-state model. We find that adding internal states to the promoter generically decreases channel capacity, except in certain cases, three of which (cooperativity, dual-role regulation, promoter cycling) we analyze in detail.},
author = {Rieckh, Georg and Tkacik, Gasper},
issn = {00063495},
journal = {Biophysical Journal},
number = {5},
pages = {1194 -- 1204},
publisher = {Biophysical Society},
title = {{Noise and information transmission in promoters with multiple internal states}},
doi = {10.1016/j.bpj.2014.01.014},
volume = {106},
year = {2014},
}
@article{2257,
abstract = {Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.},
author = {Tkacik, Gasper and Marre, Olivier and Amodei, Dario and Schneidman, Elad and Bialek, William and Berry, Michael},
issn = {1553734X},
journal = {PLoS Computational Biology},
number = {1},
publisher = {Public Library of Science},
title = {{Searching for collective behavior in a large network of sensory neurons}},
doi = {10.1371/journal.pcbi.1003408},
volume = {10},
year = {2014},
}
@article{537,
abstract = {Transgenerational effects are broader than only parental relationships. Despite mounting evidence that multigenerational effects alter phenotypic and life-history traits, our understanding of how they combine to determine fitness is not well developed because of the added complexity necessary to study them. Here, we derive a quantitative genetic model of adaptation to an extraordinary new environment by an additive genetic component, phenotypic plasticity, maternal and grandmaternal effects. We show how, at equilibrium, negative maternal and negative grandmaternal effects maximize expected population mean fitness. We define negative transgenerational effects as those that have a negative effect on trait expression in the subsequent generation, that is, they slow, or potentially reverse, the expected evolutionary dynamic. When maternal effects are positive, negative grandmaternal effects are preferred. As expected under Mendelian inheritance, the grandmaternal effects have a lower impact on fitness than the maternal effects, but this dual inheritance model predicts a more complex relationship between maternal and grandmaternal effects to constrain phenotypic variance and so maximize expected population mean fitness in the offspring.},
author = {Prizak, Roshan and Ezard, Thomas and Hoyle, Rebecca},
journal = {Ecology and Evolution},
number = {15},
pages = {3139 -- 3145},
publisher = {Wiley-Blackwell},
title = {{Fitness consequences of maternal and grandmaternal effects}},
doi = {10.1002/ece3.1150},
volume = {4},
year = {2014},
}
@inproceedings{1708,
abstract = {It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations.},
author = {Savin, Cristina and Denève, Sophie},
location = {Montreal, Canada},
number = {January},
pages = {2024 -- 2032},
publisher = {Neural Information Processing Systems},
title = {{Spatio-temporal representations of uncertainty in spiking neural networks}},
volume = {3},
year = {2014},
}
@article{1886,
abstract = {Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.},
author = {Hermundstad, Ann and Briguglio, John and Conte, Mary and Victor, Jonathan and Balasubramanian, Vijay and Tkacik, Gasper},
journal = {eLife},
number = {November},
publisher = {eLife Sciences Publications},
title = {{Variance predicts salience in central sensory processing}},
doi = {10.7554/eLife.03722},
year = {2014},
}
@article{1896,
abstract = {Biopolymer length regulation is a complex process that involves a large number of biological, chemical, and physical subprocesses acting simultaneously across multiple spatial and temporal scales. An illustrative example important for genomic stability is the length regulation of telomeres - nucleoprotein structures at the ends of linear chromosomes consisting of tandemly repeated DNA sequences and a specialized set of proteins. Maintenance of telomeres is often facilitated by the enzyme telomerase but, particularly in telomerase-free systems, the maintenance of chromosomal termini depends on alternative lengthening of telomeres (ALT) mechanisms mediated by recombination. Various linear and circular DNA structures were identified to participate in ALT, however, dynamics of the whole process is still poorly understood. We propose a chemical kinetics model of ALT with kinetic rates systematically derived from the biophysics of DNA diffusion and looping. The reaction system is reduced to a coagulation-fragmentation system by quasi-steady-state approximation. The detailed treatment of kinetic rates yields explicit formulas for expected size distributions of telomeres that demonstrate the key role played by the J factor, a quantitative measure of bending of polymers. The results are in agreement with experimental data and point out interesting phenomena: an appearance of very long telomeric circles if the total telomere density exceeds a critical value (excess mass) and a nonlinear response of the telomere size distributions to the amount of telomeric DNA in the system. The results can be of general importance for understanding dynamics of telomeres in telomerase-independent systems as this mode of telomere maintenance is similar to the situation in tumor cells lacking telomerase activity. Furthermore, due to its universality, the model may also serve as a prototype of an interaction between linear and circular DNA structures in various settings.},
author = {Kollár, Richard and Bod'ová, Katarína and Nosek, Jozef and Tomáška, Ľubomír},
journal = {Physical Review E Statistical Nonlinear and Soft Matter Physics},
number = {3},
publisher = {American Institute of Physics},
title = {{Mathematical model of alternative mechanism of telomere length maintenance}},
doi = {10.1103/PhysRevE.89.032701},
volume = {89},
year = {2014},
}
@article{1909,
abstract = {Summary: Phenotypes are often environmentally dependent, which requires organisms to track environmental change. The challenge for organisms is to construct phenotypes using the most accurate environmental cue. Here, we use a quantitative genetic model of adaptation by additive genetic variance, within- and transgenerational plasticity via linear reaction norms and indirect genetic effects respectively. We show how the relative influence on the eventual phenotype of these components depends on the predictability of environmental change (fast or slow, sinusoidal or stochastic) and the developmental lag τ between when the environment is perceived and when selection acts. We then decompose expected mean fitness into three components (variance load, adaptation and fluctuation load) to study the fitness costs of within- and transgenerational plasticity. A strongly negative maternal effect coefficient m minimizes the variance load, but a strongly positive m minimises the fluctuation load. The adaptation term is maximized closer to zero, with positive or negative m preferred under different environmental scenarios. Phenotypic plasticity is higher when τ is shorter and when the environment changes frequently between seasonal extremes. Expected mean population fitness is highest away from highest observed levels of phenotypic plasticity. Within- and transgenerational plasticity act in concert to deliver well-adapted phenotypes, which emphasizes the need to study both simultaneously when investigating phenotypic evolution.},
author = {Ezard, Thomas and Prizak, Roshan and Hoyle, Rebecca},
journal = {Functional Ecology},
number = {3},
pages = {693 -- 701},
publisher = {Wiley-Blackwell},
title = {{The fitness costs of adaptation via phenotypic plasticity and maternal effects}},
doi = {10.1111/1365-2435.12207},
volume = {28},
year = {2014},
}
@article{1928,
abstract = {In infectious disease epidemiology the basic reproductive ratio, R0, is defined as the average number of new infections caused by a single infected individual in a fully susceptible population. Many models describing competition for hosts between non-interacting pathogen strains in an infinite population lead to the conclusion that selection favors invasion of new strains if and only if they have higher R0 values than the resident. Here we demonstrate that this picture fails in finite populations. Using a simple stochastic SIS model, we show that in general there is no analogous optimization principle. We find that successive invasions may in some cases lead to strains that infect a smaller fraction of the host population, and that mutually invasible pathogen strains exist. In the limit of weak selection we demonstrate that an optimization principle does exist, although it differs from R0 maximization. For strains with very large R0, we derive an expression for this local fitness function and use it to establish a lower bound for the error caused by neglecting stochastic effects. Furthermore, we apply this weak selection limit to investigate the selection dynamics in the presence of a trade-off between the virulence and the transmission rate of a pathogen.},
author = {Humplik, Jan and Hill, Alison and Nowak, Martin},
journal = {Journal of Theoretical Biology},
pages = {149 -- 162},
publisher = {Elsevier},
title = {{Evolutionary dynamics of infectious diseases in finite populations}},
doi = {10.1016/j.jtbi.2014.06.039},
volume = {360},
year = {2014},
}
@article{1931,
abstract = {A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP) and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.},
author = {Savin, Cristina and Triesch, Jochen},
journal = {Frontiers in Computational Neuroscience},
number = {MAY},
publisher = {Frontiers Research Foundation},
title = {{Emergence of task-dependent representations in working memory circuits}},
doi = {10.3389/fncom.2014.00057},
volume = {8},
year = {2014},
}
@article{3263,
abstract = {Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution.},
author = {Tkacik, Gasper and Ghosh, Anandamohan and Schneidman, Elad and Segev, Ronen},
journal = {PLoS One},
number = {1},
publisher = {Public Library of Science},
title = {{Adaptation to changes in higher-order stimulus statistics in the salamander retina}},
doi = {10.1371/journal.pone.0085841},
volume = {9},
year = {2014},
}
@article{2277,
abstract = {Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.},
author = {Simmons, Kristina and Prentice, Jason and Tkacik, Gasper and Homann, Jan and Yee, Heather and Palmer, Stephanie and Nelson, Philip and Balasubramanian, Vijay},
journal = {PLoS Computational Biology},
number = {12},
publisher = {Public Library of Science},
title = {{Transformation of stimulus correlations by the retina}},
doi = {10.1371/journal.pcbi.1003344},
volume = {9},
year = {2013},
}
@inbook{2413,
abstract = {Progress in understanding the global brain dynamics has remained slow to date in large part because of the highly multiscale nature of brain activity. Indeed, normal brain dynamics is characterized by complex interactions between multiple levels: from the microscopic scale of single neurons to the mesoscopic level of local groups of neurons, and finally to the macroscopic level of the whole brain. Among the most difficult tasks are those of identifying which scales are significant for a given particular function and describing how the scales affect each other. It is important to realize that the scales of time and space are linked together, or even intertwined, and that causal inference is far more ambiguous between than within levels. We approach this problem from the perspective of our recent work on simultaneous recording from micro- and macroelectrodes in the human brain. We propose a physiological description of these multilevel interactions, based on phase–amplitude coupling of neuronal oscillations that operate at multiple frequencies and on different spatial scales. Specifically, the amplitude of the oscillations on a particular spatial scale is modulated by phasic variations in neuronal excitability induced by lower frequency oscillations that emerge on a larger spatial scale. Following this general principle, it is possible to scale up or scale down the multiscale brain dynamics. It is expected that large-scale network oscillations in the low-frequency range, mediating downward effects, may play an important role in attention and consciousness.},
author = {Valderrama, Mario and Botella Soler, Vicente and Le Van Quyen, Michel},
booktitle = {Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain},
editor = {Meyer, Misha and Pesenson, Z.},
isbn = {9783527411986 },
publisher = {Wiley-VCH},
title = {{Neuronal oscillations scale up and scale down the brain dynamics }},
doi = {10.1002/9783527671632.ch08},
year = {2013},
}
@article{499,
abstract = {Exposure of an isogenic bacterial population to a cidal antibiotic typically fails to eliminate a small fraction of refractory cells. Historically, fractional killing has been attributed to infrequently dividing or nondividing "persisters." Using microfluidic cultures and time-lapse microscopy, we found that Mycobacterium smegmatis persists by dividing in the presence of the drug isoniazid (INH). Although persistence in these studies was characterized by stable numbers of cells, this apparent stability was actually a dynamic state of balanced division and death. Single cells expressed catalase-peroxidase (KatG), which activates INH, in stochastic pulses that were negatively correlated with cell survival. These behaviors may reflect epigenetic effects, because KatG pulsing and death were correlated between sibling cells. Selection of lineages characterized by infrequent KatG pulsing could allow nonresponsive adaptation during prolonged drug exposure.},
author = {Wakamoto, Yurichi and Dhar, Neraaj and Chait, Remy P and Schneider, Katrin and Signorino Gelo, François and Leibler, Stanislas and Mckinney, John},
journal = {Science},
number = {6115},
pages = {91 -- 95},
publisher = {American Association for the Advancement of Science},
title = {{Dynamic persistence of antibiotic-stressed mycobacteria}},
doi = {10.1126/science.1229858},
volume = {339},
year = {2013},
}
@article{2818,
abstract = {Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are selective for only a small number of linear projections of a potentially high-dimensional input. In this review, we explore recent modeling approaches where the neural response depends on the quadratic form of the input rather than on its linear projection, that is, the neuron is sensitive to the local covariance structure of the signal preceding the spike. To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic) stimulus distribution, we review several inference methods, focusing in particular on two information theory–based approaches (maximization of stimulus energy and of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered covariance and extensions of generalized linear models). We analyze the formal relationship between the likelihood-based and information-based approaches to demonstrate how they lead to consistent inference. We demonstrate the practical feasibility of these procedures by using model neurons responding to a flickering variance stimulus.},
author = {Rajan, Kanaka and Marre, Olivier and Tkacik, Gasper},
journal = {Neural Computation},
number = {7},
pages = {1661 -- 1692},
publisher = {MIT Press },
title = {{Learning quadratic receptive fields from neural responses to natural stimuli}},
doi = {10.1162/NECO_a_00463},
volume = {25},
year = {2013},
}
@article{2850,
abstract = {Recent work emphasizes that the maximum entropy principle provides a bridge between statistical mechanics models for collective behavior in neural networks and experiments on networks of real neurons. Most of this work has focused on capturing the measured correlations among pairs of neurons. Here we suggest an alternative, constructing models that are consistent with the distribution of global network activity, i.e. the probability that K out of N cells in the network generate action potentials in the same small time bin. The inverse problem that we need to solve in constructing the model is analytically tractable, and provides a natural 'thermodynamics' for the network in the limit of large N. We analyze the responses of neurons in a small patch of the retina to naturalistic stimuli, and find that the implied thermodynamics is very close to an unusual critical point, in which the entropy (in proper units) is exactly equal to the energy. © 2013 IOP Publishing Ltd and SISSA Medialab srl.
},
author = {Tkacik, Gasper and Marre, Olivier and Mora, Thierry and Amodei, Dario and Berry, Michael and Bialek, William},
journal = {Journal of Statistical Mechanics Theory and Experiment},
number = {3},
publisher = {IOP Publishing Ltd.},
title = {{The simplest maximum entropy model for collective behavior in a neural network}},
doi = {10.1088/1742-5468/2013/03/P03011},
volume = {2013},
year = {2013},
}
@article{2851,
abstract = {The number of possible activity patterns in a population of neurons grows exponentially with the size of the population. Typical experiments explore only a tiny fraction of the large space of possible activity patterns in the case of populations with more than 10 or 20 neurons. It is thus impossible, in this undersampled regime, to estimate the probabilities with which most of the activity patterns occur. As a result, the corresponding entropy - which is a measure of the computational power of the neural population - cannot be estimated directly. We propose a simple scheme for estimating the entropy in the undersampled regime, which bounds its value from both below and above. The lower bound is the usual 'naive' entropy of the experimental frequencies. The upper bound results from a hybrid approximation of the entropy which makes use of the naive estimate, a maximum entropy fit, and a coverage adjustment. We apply our simple scheme to artificial data, in order to check their accuracy; we also compare its performance to those of several previously defined entropy estimators. We then apply it to actual measurements of neural activity in populations with up to 100 cells. Finally, we discuss the similarities and differences between the proposed simple estimation scheme and various earlier methods. © 2013 IOP Publishing Ltd and SISSA Medialab srl.},
author = {Berry, Michael and Tkacik, Gasper and Dubuis, Julien and Marre, Olivier and Da Silveira, Ravá},
journal = {Journal of Statistical Mechanics Theory and Experiment},
number = {3},
publisher = {IOP Publishing Ltd.},
title = {{A simple method for estimating the entropy of neural activity}},
doi = {10.1088/1742-5468/2013/03/P03015},
volume = {2013},
year = {2013},
}
@article{2861,
abstract = {We consider a two-parameter family of piecewise linear maps in which the moduli of the two slopes take different values. We provide numerical evidence of the existence of some parameter regions in which the Lyapunov exponent and the topological entropy remain constant. Analytical proof of this phenomenon is also given for certain cases. Surprisingly however, the systems with that property are not conjugate as we prove by using kneading theory.},
author = {Botella Soler, Vicente and Oteo, José and Ros, Javier and Glendinning, Paul},
journal = {Journal of Physics A: Mathematical and Theoretical},
number = {12},
publisher = {IOP Publishing Ltd.},
title = {{Lyapunov exponent and topological entropy plateaus in piecewise linear maps}},
doi = {10.1088/1751-8113/46/12/125101},
volume = {46},
year = {2013},
}
@article{2863,
abstract = {Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.},
author = {Granot Atedgi, Einat and Tkacik, Gasper and Segev, Ronen and Schneidman, Elad},
journal = {PLoS Computational Biology},
number = {3},
publisher = {Public Library of Science},
title = {{Stimulus-dependent maximum entropy models of neural population codes}},
doi = {10.1371/journal.pcbi.1002922},
volume = {9},
year = {2013},
}
@article{2913,
abstract = {The ability of an organism to distinguish between various stimuli is limited by the structure and noise in the population code of its sensory neurons. Here we infer a distance measure on the stimulus space directly from the recorded activity of 100 neurons in the salamander retina. In contrast to previously used measures of stimulus similarity, this "neural metric" tells us how distinguishable a pair of stimulus clips is to the retina, based on the similarity between the induced distributions of population responses. We show that the retinal distance strongly deviates from Euclidean, or any static metric, yet has a simple structure: we identify the stimulus features that the neural population is jointly sensitive to, and show the support-vector-machine- like kernel function relating the stimulus and neural response spaces. We show that the non-Euclidean nature of the retinal distance has important consequences for neural decoding.},
author = {Tkacik, Gasper and Granot Atedgi, Einat and Segev, Ronen and Schneidman, Elad},
journal = {Physical Review Letters},
number = {5},
publisher = {American Physical Society},
title = {{Retinal metric: a stimulus distance measure derived from population neural responses}},
doi = {10.1103/PhysRevLett.110.058104},
volume = {110},
year = {2013},
}
@article{2914,
abstract = {The scale invariance of natural images suggests an analogy to the statistical mechanics of physical systems at a critical point. Here we examine the distribution of pixels in small image patches and show how to construct the corresponding thermodynamics. We find evidence for criticality in a diverging specific heat, which corresponds to large fluctuations in how "surprising" we find individual images, and in the quantitative form of the entropy vs energy. We identify special image configurations as local energy minima and show that average patches within each basin are interpretable as lines and edges in all orientations.},
author = {Stephens, Greg and Mora, Thierry and Tkacik, Gasper and Bialek, William},
journal = {Physical Review Letters},
number = {1},
publisher = {American Physical Society},
title = {{Statistical thermodynamics of natural images}},
doi = {10.1103/PhysRevLett.110.018701},
volume = {110},
year = {2013},
}
@article{3261,
abstract = {Cells in a developing embryo have no direct way of "measuring" their physical position. Through a variety of processes, however, the expression levels of multiple genes come to be correlated with position, and these expression levels thus form a code for "positional information." We show how to measure this information, in bits, using the gap genes in the Drosophila embryo as an example. Individual genes carry nearly two bits of information, twice as much as expected if the expression patterns consisted only of on/off domains separated by sharp boundaries. Taken together, four gap genes carry enough information to define a cell's location with an error bar of ~1% along the anterior-posterior axis of the embryo. This precision is nearly enough for each cell to have a unique identity, which is the maximum information the system can use, and is nearly constant along the length of the embryo. We argue that this constancy is a signature of optimality in the transmission of information from primary morphogen inputs to the output of the gap gene network.},
author = {Dubuis, Julien and Tkacik, Gasper and Wieschaus, Eric and Gregor, Thomas and Bialek, William},
journal = {PNAS},
number = {41},
pages = {16301 -- 16308},
publisher = {National Academy of Sciences},
title = {{Positional information, in bits}},
doi = {10.1073/pnas.1315642110},
volume = {110},
year = {2013},
}