@article{457,
abstract = {Temperate bacteriophages integrate in bacterial genomes as prophages and represent an important source of genetic variation for bacterial evolution, frequently transmitting fitness-augmenting genes such as toxins responsible for virulence of major pathogens. However, only a fraction of bacteriophage infections are lysogenic and lead to prophage acquisition, whereas the majority are lytic and kill the infected bacteria. Unless able to discriminate lytic from lysogenic infections, mechanisms of immunity to bacteriophages are expected to act as a double-edged sword and increase the odds of survival at the cost of depriving bacteria of potentially beneficial prophages. We show that although restriction-modification systems as mechanisms of innate immunity prevent both lytic and lysogenic infections indiscriminately in individual bacteria, they increase the number of prophage-acquiring individuals at the population level. We find that this counterintuitive result is a consequence of phage-host population dynamics, in which restriction-modification systems delay infection onset until bacteria reach densities at which the probability of lysogeny increases. These results underscore the importance of population-level dynamics as a key factor modulating costs and benefits of immunity to temperate bacteriophages},
author = {Pleska, Maros and Lang, Moritz and Refardt, Dominik and Levin, Bruce and Guet, Calin C},
journal = {Nature Ecology and Evolution},
number = {2},
pages = {359 -- 366},
publisher = {Springer Nature},
title = {{Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity}},
doi = {10.1038/s41559-017-0424-z},
volume = {2},
year = {2018},
}
@article{543,
abstract = {A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, “efficient coding” posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.},
author = {Chalk, Matthew J and Marre, Olivier and Tkacik, Gasper},
journal = {PNAS},
number = {1},
pages = {186 -- 191},
publisher = {National Academy of Sciences},
title = {{Toward a unified theory of efficient, predictive, and sparse coding}},
doi = {10.1073/pnas.1711114115},
volume = {115},
year = {2018},
}
@misc{5584,
abstract = {This package contains data for the publication "Nonlinear decoding of a complex movie from the mammalian retina" by Deny S. et al, PLOS Comput Biol (2018).
The data consists of
(i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin.
(ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments are exact repeats or unique ball trajectories.
(iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions ("sites") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. },
author = {Deny, Stephane and Marre, Olivier and Botella-Soler, Vicente and Martius, Georg S and Tkacik, Gasper},
keywords = {retina, decoding, regression, neural networks, complex stimulus},
publisher = {IST Austria},
title = {{Nonlinear decoding of a complex movie from the mammalian retina}},
doi = {10.15479/AT:ISTA:98},
year = {2018},
}
@misc{5585,
abstract = {Mean repression values and standard error of the mean are given for all operator mutant libraries.},
author = {Igler, Claudia and Lagator, Mato and Tkacik, Gasper and Bollback, Jonathan P and Guet, Calin C},
publisher = {IST Austria},
title = {{Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring}},
doi = {10.15479/AT:ISTA:108},
year = {2018},
}
@misc{5587,
abstract = {Supporting material to the article
STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH
boundscoli.dat
Flux Bounds of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium.
polcoli.dat
Matrix enconding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium,
obtained from the soichiometric matrix by standard linear algebra (reduced row echelon form).
ellis.dat
Approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium
obtained with the Lovasz method.
point0.dat
Center of the approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium
obtained with the Lovasz method.
lovasz.cpp
This c++ code file receives in input the polytope of the feasible steady states of a metabolic network,
(matrix and bounds), and it gives in output an approximate Lowner-John ellipsoid rounding the polytope
with the Lovasz method
NB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260.
For further details we refer to PLoS ONE 10.4 e0122670 (2015).
sampleHRnew.cpp
This c++ code file receives in input the polytope of the feasible steady states of a metabolic network,
(matrix and bounds), the ellipsoid rounding the polytope, a point inside and
it gives in output a max entropy sampling at fixed average growth rate
of the steady states by performing an Hit-and-Run Monte Carlo Markov chain.
NB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260.
For further details we refer to PLoS ONE 10.4 e0122670 (2015).},
author = {De Martino, Daniele and Tkacik, Gasper},
keywords = {metabolic networks, e.coli core, maximum entropy, monte carlo markov chain sampling, ellipsoidal rounding},
publisher = {IST Austria},
title = {{Supporting materials "STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH"}},
doi = {10.15479/AT:ISTA:62},
year = {2018},
}
@article{607,
abstract = {We study the Fokker-Planck equation derived in the large system limit of the Markovian process describing the dynamics of quantitative traits. The Fokker-Planck equation is posed on a bounded domain and its transport and diffusion coefficients vanish on the domain's boundary. We first argue that, despite this degeneracy, the standard no-flux boundary condition is valid. We derive the weak formulation of the problem and prove the existence and uniqueness of its solutions by constructing the corresponding contraction semigroup on a suitable function space. Then, we prove that for the parameter regime with high enough mutation rate the problem exhibits a positive spectral gap, which implies exponential convergence to equilibrium.Next, we provide a simple derivation of the so-called Dynamic Maximum Entropy (DynMaxEnt) method for approximation of observables (moments) of the Fokker-Planck solution, which can be interpreted as a nonlinear Galerkin approximation. The limited applicability of the DynMaxEnt method inspires us to introduce its modified version that is valid for the whole range of admissible parameters. Finally, we present several numerical experiments to demonstrate the performance of both the original and modified DynMaxEnt methods. We observe that in the parameter regimes where both methods are valid, the modified one exhibits slightly better approximation properties compared to the original one.},
author = {Bodova, Katarina and Haskovec, Jan and Markowich, Peter},
journal = {Physica D: Nonlinear Phenomena},
pages = {108--120},
publisher = {Elsevier},
title = {{Well posedness and maximum entropy approximation for the dynamics of quantitative traits}},
doi = {10.1016/j.physd.2017.10.015},
volume = {376-377},
year = {2018},
}
@article{281,
abstract = {Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making.},
author = {Granados, Alejandro and Pietsch, Julian and Cepeda Humerez, Sarah A and Farquhar, Isebail and Tkacik, Gasper and Swain, Peter},
journal = {PNAS},
number = {23},
pages = {6088 -- 6093},
publisher = {National Academy of Sciences},
title = {{Distributed and dynamic intracellular organization of extracellular information}},
doi = {10.1073/pnas.1716659115},
volume = {115},
year = {2018},
}
@article{292,
abstract = {Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains.},
author = {Botella Soler, Vicent and Deny, Stephane and Martius, Georg S and Marre, Olivier and Tkacik, Gasper},
journal = {PLoS Computational Biology},
number = {5},
publisher = {Public Library of Science},
title = {{Nonlinear decoding of a complex movie from the mammalian retina}},
doi = {10.1371/journal.pcbi.1006057},
volume = {14},
year = {2018},
}
@article{305,
abstract = {The hanging-drop network (HDN) is a technology platform based on a completely open microfluidic network at the bottom of an inverted, surface-patterned substrate. The platform is predominantly used for the formation, culturing, and interaction of self-assembled spherical microtissues (spheroids) under precisely controlled flow conditions. Here, we describe design, fabrication, and operation of microfluidic hanging-drop networks.},
author = {Misun, Patrick and Birchler, Axel and Lang, Moritz and Hierlemann, Andreas and Frey, Olivier},
journal = {Methods in Molecular Biology},
pages = {183 -- 202},
publisher = {Springer},
title = {{Fabrication and operation of microfluidic hanging drop networks}},
doi = {10.1007/978-1-4939-7792-5_15},
volume = {1771},
year = {2018},
}
@article{306,
abstract = {A cornerstone of statistical inference, the maximum entropy framework is being increasingly applied to construct descriptive and predictive models of biological systems, especially complex biological networks, from large experimental data sets. Both its broad applicability and the success it obtained in different contexts hinge upon its conceptual simplicity and mathematical soundness. Here we try to concisely review the basic elements of the maximum entropy principle, starting from the notion of ‘entropy’, and describe its usefulness for the analysis of biological systems. As examples, we focus specifically on the problem of reconstructing gene interaction networks from expression data and on recent work attempting to expand our system-level understanding of bacterial metabolism. Finally, we highlight some extensions and potential limitations of the maximum entropy approach, and point to more recent developments that are likely to play a key role in the upcoming challenges of extracting structures and information from increasingly rich, high-throughput biological data.},
author = {De Martino, Andrea and De Martino, Daniele},
journal = {Heliyon},
number = {4},
publisher = {Elsevier},
title = {{An introduction to the maximum entropy approach and its application to inference problems in biology}},
doi = {10.1016/j.heliyon.2018.e00596},
volume = {4},
year = {2018},
}