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Stochasticity in gene expression can give rise to fluctuations in protein levels and lead to phenotypic variation across a population of genetically identical cells. Recent experiments indicate that bursting and feedback mechanisms play…

Molecular Networks · Quantitative Biology 2015-06-22 Niraj Kumar , Thierry Platini , Rahul V. Kulkarni

One of the grand challenges of cell biology is inferring the gene regulatory network (GRN) which describes interactions between genes and their products that control gene expression and cellular function. We can treat this as a causal…

Machine Learning · Computer Science 2023-12-27 Lazar Atanackovic , Alexander Tong , Bo Wang , Leo J. Lee , Yoshua Bengio , Jason Hartford

The intrinsic stochasticity of gene expression can lead to large variability of protein levels across a population of cells. Variability (or noise) in protein distributions can be modulated by cellular mechanisms of gene regulation; in…

Molecular Networks · Quantitative Biology 2011-03-02 Tao Jia , Rahul V. Kulkarni

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated…

Machine Learning · Statistics 2016-04-12 Jonathan H. Warrell , Musa M. Mhlanga

Spatial environmental variation can either amplify or suppress the fixation of beneficial mutants in structured populations, yet the interplay of ecological factors and spatial structure in determining which outcome occurs remains…

Populations and Evolution · Quantitative Biology 2026-05-07 Hossein Nemati , Kamran Kaveh , Jakub Svoboda , Mohammad Reza Ejtehadi , Krishnendu Chatterjee

In this paper we investigate a neural network model in which weights between computational nodes are modified according to a local learning rule. To determine whether local learning rules are sufficient for learning, we encode the network…

Neural and Evolutionary Computing · Computer Science 2025-12-08 Jonathan Baxter

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types, that elaborates this complexity, result from the activity of gene networks and…

Quantitative Methods · Quantitative Biology 2022-05-11 David A. Rand , Archishman Raju , Meritxell Saez , Francis Corson , Eric D. Siggia

Gene duplication is a fundamental evolutionary mechanism that contributes to biological complexity and diversity (Fortna et al., 2004). Traditionally, research has focused on the duplication of gene sequences (Zhang, 1914). However,…

Molecular Networks · Quantitative Biology 2024-05-07 Ashley Scruse , Jonathan Arnold , Robert Robinson

Characterization of pluripotent states, in which cells can both self-renew and differentiate, and the irreversible loss of pluripotency are important research areas in developmental biology. In particular, an understanding of these…

Molecular Networks · Quantitative Biology 2015-09-02 Tadashi Miyamoto , Chikara Furusawa , Kunihiko Kaneko

Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show…

Molecular Networks · Quantitative Biology 2017-02-08 Guillermo Rodrigo , Juan F. Poyatos

This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states…

Molecular Networks · Quantitative Biology 2017-02-27 Mahdi Imani , Ulisses Braga-Neto

It often is emphasized that gene expression is noisy. A seemingly contradictory view is that control mechanisms have been optimized to squeeze as much information as possible out of a limited number of molecules. Here we revisit these…

Biological Physics · Physics 2025-12-17 Nicholas Lawson , William Bialek

Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated…

Molecular Networks · Quantitative Biology 2024-02-16 Claus Kadelka , David Murrugarra

We study the evolution of the network properties of a populated network embedded in a genotype space characterised by either a low or a high number of potential links, with particular emphasis on the connectivity and clustering. Evolution…

Statistical Mechanics · Physics 2007-05-23 Paul Anderson , Henrik Jeldtoft Jensen

Gene expression is a noisy process that leads to regime shift between alternative steady states among individual living cells, inducing phenotypic variability. The effects of white noise on the regime shift in bistable systems have been…

Molecular Networks · Quantitative Biology 2017-08-23 Yogita Sharma , Partha Sharathi Dutta

Gradient dynamics play a central role in determining the stability and generalization of deep neural networks. In this work, we provide an empirical analysis of how variance and standard deviation of gradients evolve during training,…

Machine Learning · Computer Science 2025-09-09 Vincent-Daniel Yun

Molecular phenotypes are important links between genomic information and organismic functions, fitness, and evolution. Complex phenotypes, which are also called quantitative traits, often depend on multiple genomic loci. Their evolution…

Populations and Evolution · Quantitative Biology 2015-06-12 Armita Nourmohammad , Stephan Schiffels , Michael Laessig

We show that the effect of stress on the reproductive fitness of noisy cell populations can be modelled as first-passage time problem, and demonstrate that even relatively short-lived fluctuations in gene expression can ensure long-term…

Biological Physics · Physics 2015-06-03 Daniel A. Charlebois , Nezar Abdennur , Mads Kaern

Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical…

Molecular Networks · Quantitative Biology 2010-12-14 Berkin Malkoc , Duygu Balcan , Ayse Erzan

Inferring the structure of gene regulatory networks (GRN) from gene expression data has many applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously…

Machine Learning · Statistics 2012-05-08 Anne-Claire Haury , Fantine Mordelet , Paola Vera-Licona , Jean-Philippe Vert