ArticleThe journal of physical chemistry. B2011
Modeling stochastic dynamics in biochemical systems with feedback using maximum caliber.
Article in The journal of physical chemistry. B, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Avoiding matrix exponentials for large transition rate matrices.The Journal of chemical physics · 2024Article
- MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division.Biophysical journal · 2023Article
- Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks.Entropy (Basel, Switzerland) · 2021Article
- Inferring a network from dynamical signals at its nodes.PLoS computational biology · 2020Article
- The Maximum Caliber Variational Principle for Nonequilibria.Annual review of physical chemistry · 2020Review
- Quantitative Kinetic Models from Intravital Microscopy: A Case Study Using Hepatic Transport.The journal of physical chemistry. B · 2019Article
- Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network.The journal of physical chemistry. B · 2019Article
- An introduction to the maximum entropy approach and its application to inference problems in biology.Heliyon · 2018Review
- Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber.Biophysical journal · 2017Article
- High-Dimensional Mutant and Modular Thermodynamic Cycles, Molecular Switching, and Free Energy Transduction.Annual review of biophysics · 2017Review
- Data-driven quantification of the robustness and sensitivity of cell signaling networks.Physical biology · 2013Article
- Markov processes follow from the principle of maximum caliber.The Journal of chemical physics · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Complex feedback systems are ubiquitous in biology. Modeling such systems with mass action laws or master equations requires information rarely measured directly. Thus rates and reaction topologies are often treated as adjustable parameters. Here we present a general stochastic modeling method for small chemical and biochemical systems with emphasis on feedback systems. The method, Maximum Caliber (MaxCal), is more parsimonious than others in constructing dynamical models requiring fewer model assumptions and parameters to capture the effects of feedback. MaxCal is the dynamical analogue of Maximum Entropy. It uses average rate quantities and correlations obtained from short experimental trajectories to construct dynamical models. We illustrate the method on the bistable genetic toggle switch. To test our method, we generate synthetic data from an underlying stochastic model. MaxCal reliably infers the statistics of the stochastic bistability and other full dynamical distributions of the simulated data, without having to invoke complex reaction schemes. The method should be broadly applicable to other systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.