2017/10/17 by Eugenio Cinquemani, Cinquemani, Eugenio
Biochemistry, Genetics and Molecular Biology · Mathematics · #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Mathematics #Gene Regulatory Network Analysis #Gene expression and cancer classification #Microbial Metabolic Engineering and Bioproduction #Optimization and Control (math.OC) #Quantitative Methods (q-bio.QM) #math.OC #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1710.06259
arxiv created 2017/10/17 · openalex publication_date 2017/10/17 · arxiv updated 2017/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Stochastic reaction network models are widely utilized in biology and chemistry to describe the probabilistic dynamics of biochemical systems in general, and gene interaction networks in particular. Most often, statistical analysis and inference of these systems is addressed by parametric approaches, where the laws governing exogenous input processes, if present, are themselves fixed in advance. Motivated by reporter gene systems, widely utilized in biology to monitor gene activation at the individual cell level, we address the analysis of reaction networks with state-affine reaction rates and arbitrary input processes. We derive a generalization of the so-called moment equations where the dynamics of the network statistics are expressed as a function of the input process statistics. In stationary conditions, we provide a spectral analysis of the system and elaborate on connections with linear filtering. We then apply the theoretical results to develop a method for the reconstruction of input process statistics, namely the gene activation autocovariance function, from reporter gene population snapshot data, and demonstrate its performance on a simulated case study.