A survey on piecewise-linear models of regulatory dynamical systems


Oktem H.

NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, vol.63, no.3, pp.336-349, 2005 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 63 Issue: 3
  • Publication Date: 2005
  • Doi Number: 10.1016/j.na.2005.04.041
  • Journal Name: NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.336-349
  • Keywords: hybrid systems, complex networks, regulatory dynamics, boolean networks, piecewise-linear systems, CELL-DIFFERENTIATION, BOOLEAN NETWORKS, LOGICAL ANALYSIS, MULTISTATIONARITY, MEMORY

Abstract

Recent developments in understanding the various regulatory systems, especially the developments in biology and genomics, stimulated an interest in modelling such systems. Hybrid systems, originally developed for process control applications, provide advances in modelling such systems. A particular class of hybrid systems which are relatively simpler to analyze mathematically but still capable of demonstrating the essential features of many non-linear dynamical systems is piecewise-linear systems. Implementation of piecewise-linear systems for modelling of regulatory dynamical systems requires different considerations depending on the status of the problem. In this work we considered three different cases. Firstly, we consider the inferential modelling problem based on the empirical observations and study the discrete piecewise-linear system, whose inverse problem is solvable under some assumptions. Secondly, we considered the problem of obtaining some complex regulatory systems by tractable piecewise-linear formulations and study the qualitative dynamic features of the systems and their piecewise-linear models. Finally, we considered Boolean delay equations for building abstract models of regulatory systems, which might be the simplest models demonstrating the essential qualitative features of our interest underlying adaption, learning and memorization. (c) 2005 Elsevier Ltd. All rights reserved.