Characterizing the relationship between steady state and response using analytical expressions for the steady states of mass action models.

TitleCharacterizing the relationship between steady state and response using analytical expressions for the steady states of mass action models.
Publication TypeJournal Article
Year of Publication2013
AuthorsLoriaux P M, Tesler G, Hoffmann A
JournalPLoS Comput Biol
Volume9
Issue2
Paginatione1002901
Date Published2013
ISSN1553-7358
Abstract

The steady states of cells affect their response to perturbation. Indeed, diagnostic markers for predicting the response to therapeutic perturbation are often based on steady state measurements. In spite of this, no method exists to systematically characterize the relationship between steady state and response. Mathematical models are established tools for studying cellular responses, but characterizing their relationship to the steady state requires that it have a parametric, or analytical, expression. For some models, this expression can be derived by the King-Altman method. However, King-Altman requires that no substrate act as an enzyme, and is therefore not applicable to most models of signal transduction. For this reason we developed py-substitution, a simple but general method for deriving analytical expressions for the steady states of mass action models. Where the King-Altman method is applicable, we show that py-substitution yields an equivalent expression, and at comparable efficiency. We use py-substitution to study the relationship between steady state and sensitivity to the anti-cancer drug candidate, dulanermin (recombinant human TRAIL). First, we use py-substitution to derive an analytical expression for the steady state of a published model of TRAIL-induced apoptosis. Next, we show that the amount of TRAIL required for cell death is sensitive to the steady state concentrations of procaspase 8 and its negative regulator, Bar, but not the other procaspase molecules. This suggests that activation of caspase 8 is a critical point in the death decision process. Finally, we show that changes in the threshold at which TRAIL results in cell death is not always equivalent to changes in the time of death, as is commonly assumed. Our work demonstrates that an analytical expression is a powerful tool for identifying steady state determinants of the cellular response to perturbation. All code is available at http://signalingsystems.ucsd.edu/models-and-code/ or as supplementary material accompanying this paper.

DOI10.1371/journal.pcbi.1002901
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/23509437?dopt=Abstract
PMCPMC3585464
Alternate JournalPLoS Comput. Biol.
PubMed ID23509437