A reliable Taylor series-based computational method for the calculation of dynamic sensitivities in large-scale metabolic reaction systems: Algorithm and software evaluation

Fumihide Shiraishi, Tomofumi Tomita, Michio Iwata, Aziz A. Berrada, Hiroshi Hirayama

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Dynamic sensitivity analysis has become an important tool to successfully characterize all sorts of biological systems. However, when the analysis is carried out on large scale systems, it becomes imperative to employ a highly accurate computational method in order to obtain reliable values. Furthermore, the preliminary laborious mathematical operations required by current software before the computation of dynamic sensitivities makes it inconvenient for a significant number of unacquainted users. To satisfy these needs, the present work investigates a newly developed algorithm consisting of a combination of Taylor series method that can directly execute Taylor expansions for simultaneous non-linear-differential equations and a simple but highly-accurate numerical differentiation method based on finite-difference formulas. Applications to three examples of biochemical systems indicate that the proposed method makes it possible to compute the dynamic sensitivity values with highly-reliable accuracies and also allows to readily compute them by setting up only the differential equations for metabolite concentrations in the computer program. Also, it is found that the Padé approximation introduced in the Taylor series method shortens the computation time greatly because it stabilizes the computation so that it allows us to use larger stepsizes in the numerical integration. Consequently, the calculated results suggest that the proposed computational method, in addition to being user-friendly, makes it possible to perform dynamic sensitivity analysis in large-scale metabolic reaction systems both efficiently and reliably.

Original languageEnglish
Pages (from-to)73-85
Number of pages13
JournalMathematical Biosciences
Volume222
Issue number2
DOIs
Publication statusPublished - Dec 1 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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