Detailed balance, local detailed balance, and global potential for stochastic chemical reaction networks
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Publication:5156806
DOI10.1017/apr.2021.3zbMath1476.92059arXiv1901.09140OpenAlexW3207588171MaRDI QIDQ5156806
Da-quan Jiang, Youming Li, Chen Jia
Publication date: 12 October 2021
Published in: Advances in Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1901.09140
large deviationmetastabilitychemical reaction systemsquasi-potentialmicroscopic reversibilityKolmogorov's cycle condition
Classical flows, reactions, etc. in chemistry (92E20) Large deviations (60F10) Applications of continuous-time Markov processes on discrete state spaces (60J28)
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Cites Work
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- A solution to the reversible embedding problem for finite Markov chains
- Large deviations for two-scale chemical kinetic processes
- Product-form stationary distributions for deficiency zero chemical reaction networks
- Lyapunov functions, stationary distributions, and non-equilibrium potential for reaction networks
- Strong approximation theorems for density dependent Markov chains
- Large deviations theory for Markov jump models of chemical reaction networks
- On the geometry of chemical reaction networks: Lyapunov function and large deviations
- Markovian modelling of gene product synthesis
- Atoms of multistationarity in chemical reaction networks
- Seemingly stable chemical kinetics can be stable, marginally stable, or unstable
- Tier structure of strongly endotactic reaction networks
- A detailed balanced reaction network is sufficient but not necessary for its Markov chain to be detailed balanced
- Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks
- Introduction to Smooth Manifolds
- Large Deviations and Metastability
- Multistationarity in Biochemical Networks: Results, Analysis, and Examples
- Kinetic Foundation of the Zero-Inflated Negative Binomial Model for Single-Cell RNA Sequencing Data
- Stochastic Analysis of Biochemical Systems
- Limit theorems for sequences of jump Markov processes approximating ordinary differential processes
- Matrix theory. Basic results and techniques
- Convex functions and their applications. A contemporary approach
