Optimization-based inference for temporally evolving networks with applications in biology

Young Hwan Chang, Joe Gray, Claire Tomlin

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.

Original languageEnglish (US)
Pages (from-to)1307-1323
Number of pages17
JournalJournal of Computational Biology
Volume19
Issue number12
DOIs
StatePublished - Dec 1 2012

Fingerprint

Genetic Network
Biological Networks
Biology
Research Design
Costs and Cost Analysis
Signaling Pathways
Optimization
Adjacency
Biological systems
Data-driven
Biological Systems
Cost functions
Cost Function
Connectivity
Simulation Study
Optimization Problem
Interval
Formulation
Graph in graph theory

Keywords

  • gene regulatory networks
  • inference of dynamic models
  • temporally evolving networks

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Modeling and Simulation
  • Computational Theory and Mathematics

Cite this

Optimization-based inference for temporally evolving networks with applications in biology. / Chang, Young Hwan; Gray, Joe; Tomlin, Claire.

In: Journal of Computational Biology, Vol. 19, No. 12, 01.12.2012, p. 1307-1323.

Research output: Contribution to journalArticle

@article{a775e5e6f27d4d1ba23d1f528a783b48,
title = "Optimization-based inference for temporally evolving networks with applications in biology",
abstract = "The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.",
keywords = "gene regulatory networks, inference of dynamic models, temporally evolving networks",
author = "Chang, {Young Hwan} and Joe Gray and Claire Tomlin",
year = "2012",
month = "12",
day = "1",
doi = "10.1089/cmb.2012.0190",
language = "English (US)",
volume = "19",
pages = "1307--1323",
journal = "Journal of Computational Biology",
issn = "1066-5277",
publisher = "Mary Ann Liebert Inc.",
number = "12",

}

TY - JOUR

T1 - Optimization-based inference for temporally evolving networks with applications in biology

AU - Chang, Young Hwan

AU - Gray, Joe

AU - Tomlin, Claire

PY - 2012/12/1

Y1 - 2012/12/1

N2 - The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.

AB - The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.

KW - gene regulatory networks

KW - inference of dynamic models

KW - temporally evolving networks

UR - http://www.scopus.com/inward/record.url?scp=84870662126&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870662126&partnerID=8YFLogxK

U2 - 10.1089/cmb.2012.0190

DO - 10.1089/cmb.2012.0190

M3 - Article

C2 - 23210478

AN - SCOPUS:84870662126

VL - 19

SP - 1307

EP - 1323

JO - Journal of Computational Biology

JF - Journal of Computational Biology

SN - 1066-5277

IS - 12

ER -