title: Direct Lyapunov exponent analysis enables parametric study of transient signalling governing cell behaviour.
abstract: Computational models aid in the quantitative understanding of cell signalling networks. One important goal is to ascertain how multiple network components work together to govern cellular responses, that is, to determine cell 'signal-response' relationships. Several methods exist to study steady-state signals in the context of differential equation-based models. However, many biological networks influence cell behaviour through time-varying signals operating during a transient activated state that ultimately returns to a basal steady-state. A computational approach adapted from dynamical systems analysis to discern how diverse transient signals relate to alternative cell fates is described. Direct finite-time Lyapunov exponents (DLEs) are employed to identify phase-space domains of high sensitivity to initial conditions. These domains delineate regions exhibiting qualitatively different transient activities that would be indistinguishable using steady-state analysis but which correspond to different outcomes. These methods are applied to a physicochemical model of molecular interactions among caspase-3, caspase-8 and X-linked inhibitor of apoptosis--proteins whose transient activation determines cell death against survival fates. DLE analysis enabled identification of a separatrix that quantitatively characterises network behaviour by defining initial conditions leading to apoptotic cell death. It is anticipated that DLE analysis will facilitate theoretical investigation of phenotypic outcomes in larger models of signalling networks.
authors.affiliation: Biological Engineering Division, Center for Cell Decision Processes, MIT, Cambridge, MA 02139, USA. breea@mit.edu
authors.forename: B B
authors.initials: BB
authors.lastname: Aldridge
authors.forename: G
authors.initials: G
authors.lastname: Haller
authors.forename: P K
authors.initials: PK
authors.lastname: Sorger
authors.forename: D A
authors.initials: DA
authors.lastname: Lauffenburger
mesh_terms: D000465:Algorithms; D017209:Apoptosis / Q000502:physiology*; D051017:Apoptosis Regulatory Proteins / Q000378:metabolism*; D053148:Caspase 3 / Q000378:metabolism*; D053181:Caspase 8 / Q000378:metabolism*; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D007700:Kinetics; D008954:Models, Biological; D013997:Time Factors
chemical_list: D051017:Apoptosis Regulatory Proteins; D053148:Caspase 3; D053181:Caspase 8