Model reduction and physical understanding of slowly oscillating processes: The circadian cycle

Dimitris A. Goussis, Habib N. Najm

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

A differential system that models the circadian rhythm in Drosophila is analyzed with the computational singular perturbation (CSP) algorithm. Reduced nonstiff models of prespecified accuracy are constructed, the form and size of which are time-dependent. When compared with conventional asymptotic analysis, CSP exhibits superior performance in constructing reduced models, since it can algorithmically identify and apply all the required order of magnitude estimates and algebraic manipulations. A similar performance is demonstrated by CSP in generating data that allow for the acquisition of physical understanding. It is shown that the processes driving the circadian cycle are (i) mRNA translation into monomer protein, and monomer protein destruction by phosphorylation and degradation (along the largest portion of the cycle); and (ii) mRNA synthesis (along a short portion of the cycle). These are slow processes. Their action in driving the cycle is allowed by the equilibration of the fastest processes; (1) the monomer dimerization with the dimer dissociation (along the largest portion of the cycle); and (2) the net production of monomer+dimmer proteins with that of mRNA (along the short portion of the cycle). Additional results (regarding the time scales of the established equilibria, their origin, the rate limiting steps, the couplings among the variables, etc.) highlight the utility of CSP for automated identification of the important underlying dynamical features, otherwise accessible only for simple systems whose various suitable simplifications can easily be recognized.

Original languageBritish English
Pages (from-to)1297-1332
Number of pages36
JournalMultiscale Modeling and Simulation
Volume5
Issue number4
DOIs
StatePublished - 2006

Keywords

  • Asymptotic analysis
  • Circadian rhythm
  • Computational singular perturbation
  • Drosophila
  • Reduced models

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