Switching Langevin Dynamics for Gene Regulatory Networks

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

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

Abstract

We consider the problem of estimating a gene regulatory network under switching noise dynamics. Several stochastic models based on discretized Langevin dynamics are introduced to model different types of intrinsic and extrinsic noise in gene regulatory networks. We propose a Bayesian technique that chooses the appropriate dynamical system to describe the noise. The resulting Bayesian hierarchical model uses a categorical distribution to probabilistically select the dynamical system. We then exploit particle filtering to infer the noise type as well as the state trajectory. We demonstrate the advantage of our proposed method through simulations.

Original languageEnglish (US)
Title of host publication56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1316-1320
Number of pages5
ISBN (Electronic)9781665459068
DOIs
StatePublished - 2022
Externally publishedYes
Event56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States
Duration: Oct 31 2022Nov 2 2022

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2022-October

Conference

Conference56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period10/31/2211/2/22

Keywords

  • Bayesian inference
  • gene regulatory networks
  • par-ticle filter
  • stochastic differential equations
  • stochastic modeling

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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