Inference of temporally evolving network dynamics with applications in biological systems

Young Hwan Chang, Claire Tomlin

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

6 Scopus citations

Abstract

Modeling of biological signal pathways forms the basis of systems biology. Also, the problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this paper, we propose a data-driven inference scheme to identify dynamics with a local point of view, the Jacobian matrix. A graph model is a natural way to represent a biological signal pathway and doesn't require any constraints on dynamics such as mass action kinetics or Hill function representations, used in Ordinary Differential Equation (ODE) models. A graph is a set of vertices which represents state, and a set of edges which depicts the relationship or connection between two or more states. Once a system is abstracted by a graph, in order to identify the activity level of the corresponding interactions based on a given data set, we reformulate the problem as a Linear Quadratic (LQ) Optimal Control problem by transforming the unknown entries of the activity of edges into the control inputs of the LQ setting. In the formulation of the LQ problem, we use an adjacency map as a priori information and define a performance index which both drives the connectivity of the graph to match the biological data as well as generates a sparse network. Through simulation studies on simple examples, it is shown that this scheme can help to capture the topological change of a biological signal pathway and show the influence or activity of each edge over time. Also, we sketch briefly the potential application of this approach to correcting the graph model.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Pages3706-3711
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
CountryUnited States
CityOrlando, FL
Period12/12/1112/15/11

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Chang, Y. H., & Tomlin, C. (2011). Inference of temporally evolving network dynamics with applications in biological systems. In Proceedings of the IEEE Conference on Decision and Control (pp. 3706-3711). [6160849] https://doi.org/10.1109/CDC.2011.6160849