ArticleThe Journal of chemical physics2014
Unveiling the hidden structure of complex stochastic biochemical networks.
Article in The Journal of chemical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression.Nature communications · 2025Article
- Inferring phenomenological models of first passage processes.PLoS computational biology · 2021Article
- Direct detection of molecular intermediates from first-passage times.Science advances · 2020Article
- Universal Kinetics of the Onset of Cell Spreading on Substrates of Different Stiffness.Biophysical journal · 2019Article
- Single-molecule modeling of mRNA degradation by miRNA: Lessons from data.BMC systems biology · 2015Article
- Stochastic kinetics on networks: when slow is fast.The journal of physical chemistry. B · 2014Article
- Pathway structure determination in complex stochastic networks with non-exponential dwell times.The Journal of chemical physics · 2014Article
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Authors and funding
3 authors.
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Abstract
Complex Markov models are widely used and powerful predictive tools to analyze stochastic biochemical processes. However, when the network of states is unknown, it is necessary to extract information from the data to partially build the network and estimate the values of the rates. The short-time behavior of the first-passage time distributions between two states in linear chains has been shown recently to behave as a power of time with an exponent equal to the number of intermediate states. For a general Markov model we derive the complete Taylor expansion of the first-passage time distribution between two arbitrary states. By combining algebraic methods and graph theory approaches it is shown that the first term of the Taylor expansion is determined by the shortest path from the initial state to the final state. When this path is unique, we prove that the coefficient of the first term can be written in terms of the product of the transition rates along the path. It is argued that the application of our results to first-return times may be used to estimate the dependence of rates on external parameters in experimentally measured time distributions.
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Registered trials
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