notes

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index

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applications of stochastic processes

statistical physics, stochastic processes

[[Bayesian probabilistic inference for stochastic processes

simulation

BioSimulator.jl in julia

chemical reaction networks chemical reaction network theory

Large deviations and dynamical phase transitions in stochastic chemical networks by Lazarescu and Esposito in 2019

Galstyan, V., & Saakian, D. B. (2012). Dynamics of the chemical master equation, a strip of chains of equations in d-dimensional space. Physical Review E, __86__(1), 011125. https://doi.org/10.1103/PhysRevE.86.011125

Approximation and inference methods for stochastic biochemical kinetics—a tutorial review by David Schnoerr, Guido Sanguinetti and Ramon Grima in 2017

Inferring gene regulatory networks from single-cell data a mechanistic approach by Ulysse Herbach, Olivier Gandrillon, et al in 2017

population dynamics

Saakian, D. B., Yakushkina, T., & Koonin, E. V. (2019). Allele fixation probability in a Moran model with fluctuating fitness landscapes. Physical Review E, __99__(2), 022407. https://doi.org/10.1103/PhysRevE.99.022407

Sella, G., & Hirsh, A. E. (2005). The application of statistical physics to evolutionary biology. PNAS, __102__(27), 9541–9546. https://doi.org/10.1073/pnas.0501865102

Introductory Lectures on Stochastic Population Systems by Donald Dawson in 2017

Population biology and criticality by Nico Stollenwerk and Vincent Johnson in 2010

marginal observations of biochemical systems biochemistry

Altaner, B., & Vollmer, J. (2012). Fluctuation-Preserving Coarse Graining for Biochemical Systems. Physical Review Letters, __108__(22), 228101. https://doi.org/10.1103/PhysRevLett.108.228101

Bravi, B., & Sollich, P. (2017). Statistical physics approaches to subnetwork dynamics in biochemical systems. Physical Biology, __14__(4), 045010. https://doi.org/10.1088/1478-3975/aa7363 Statistical physics approaches to subnetwork dynamics in biochemical systems by Bravi and Sollich in 2017

Computation and probabilistic inference

Mezard, M., & Montanari, A. (2009). __Information, physics, and computation__. Oxford University Press. Information, physics, and computation by Marc Mezard and Andrea Montanari in 2009

A high-bias, low-variance introduction to Machine Learning for physicists by Pankaj Mehta, Charles Fisher, David Schwab, et al in 2018

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