Skip to Main content Skip to Navigation
Conference papers

Inference of the statistics of a modulated promoter process from population snapshot gene expression data

Eugenio Cinquemani 1
1 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
Abstract : In previous work, we have developed mathematical tools for the analysis of single-cell gene expression data from population snapshots, and an inference algorithm for the estimation of stationary statistics of promoter activation. In this work, we address the inference problem in the nonstationary case of modulated processes. This is of special relevance to control scenarios, where an exogenous input modulates the time evolution of promoter activation. We provide an effective method for the computation of the output statistics of a reaction network with a nonstationary, causal input process of modulated form. Based on this we devise and demonstrate an algorithm for the reconstruction of the promoter (input) process statistics from snapshot data.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-03085422
Contributor : Team Microcosme / Team Ibis Connect in order to contact the contributor
Submitted on : Monday, December 21, 2020 - 5:51:58 PM
Last modification on : Friday, January 21, 2022 - 3:11:02 AM

File

nonstat.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03085422, version 1

Collections

Citation

Eugenio Cinquemani. Inference of the statistics of a modulated promoter process from population snapshot gene expression data. IFAC-V 2020 - 1st Virtual IFAC World Congress, Jul 2020, Berlin, Germany. pp.1-6. ⟨hal-03085422⟩

Share

Metrics

Les métriques sont temporairement indisponibles