Sequential Bayesian Inference Using Stochastic Models of Gene Regulatory Networks

Nayely Velez-Cruz, Bahman Moraffah, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A key challenge in inferring the structure and dynamics of gene regulatory networks is the representation of various sources of stochasticity in mathematical models. In this work, we propose a modified stochastic Michaelis-Menten model to better capture the complex molecular mechanisms involved in gene regulation. We show that Bayesian sequential inference methods can be used to estimate gene expression values when utilizing this complex model. We also study the effect of various types of noise sources that arise from biological reactions and environmental fluctuations. We also extend these models to include time-varying kinetic order parameters so that they may be used for inference of time-varying gene regulatory networks.

Original languageEnglish (US)
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages568-572
Number of pages5
ISBN (Electronic)9781665458283
DOIs
StatePublished - 2021
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: Oct 31 2021Nov 3 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2021-October
ISSN (Print)1058-6393

Conference

Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove
Period10/31/2111/3/21

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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