Jia, Chen

What can we learn when fitting a simple telegraph model to a complex gene expression model?

Jiao, F., Li, J., Liu, T., Zhu, Y., Che, W., Bleris, L., & Jia, C. (2024) · PLOS Computational Biology

Gene Regulatory Network Analysisstochastic dynamics and bifurcation

Cited by 26

What can we learn when fitting a simple telegraph model to a complex gene expression model?

Jiao, F., Li, J., Liu, T., Zhu, Y., Che, W., Bleris, L., & Jia, C. (2023) · PLoS Computational Biology

Gene Regulatory Network Analysisstochastic dynamics and bifurcation

Cited by 26

Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition

Wu, B., Holehouse, J., Grima, R., & Jia, C. (2023) · The Journal of Chemical Physics

Gene Regulatory Network Analysisstochastic dynamics and bifurcation

Cited by 19

Effective Markovian Dynamics Method for Solving Non-Markovian Dynamics of Stochastic Gene Expression

Li, Y., & Jia, C. (2025) · Physical Review Letters

Gene Regulatory Network Analysisstochastic dynamics and bifurcation

Cited by 7

Effective Markovian dynamics method of solving non-Markovian dynamics of stochastic gene expression

Li, Y., & Jia, C. (2024) · Physical Review Letters

Gene Regulatory Network Analysisstochastic dynamics and bifurcation

Cited by 7

Exact switching time distributions for autoregulated gene expression models with mRNA and protein descriptions

Liu, S., Wu, B., & Jia, C. (2024) · SIAM Journal on Applied Mathematics

Gene Regulatory Network AnalysisStochastic processes and statistical mechanics

Cited by 1

Exact Switching Time Distributions for Autoregulated Gene Expression Models with mRNA and Protein Descriptions

Liu, S., Wu, B., & Jia, C. (2025) · SIAM Journal on Applied Mathematics

Gene Regulatory Network AnalysisStochastic processes and statistical mechanics

Cited by 1