Model reduction of multiscale chemical langevin equations: A numerical case study

Vassilios Sotiropoulos, Marie Nathalie Contou-Carrere, Prodromos Daoutidis, Yiannis N. Kaznessis

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models. Second, models must take into consideration the disparate spectrum of time scales observed in biological phenomena, such as slow transcription events and fast dimerization reactions. In the last decade, significant efforts have been expended on the development of stochastic chemical kinetics models to capture the dynamics of biomolecular systems, and on the development of robust multiscale algorithms, able to handle stiffness. In this paper, the focus is on the dynamics of reaction sets governed by stiff chemical Langevin equations, i.e., stiff stochastic differential equations. These are particularly challenging systems to model, requiring prohibitively small integration step sizes. We describe and illustrate the application of a semianalytical reduction framework for chemical Langevin equations that results in significant gains in computational cost.

Original languageEnglish (US)
Article number4785453
Pages (from-to)470-482
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number3
StatePublished - Jul 2009

Bibliographical note

Funding Information:
This work was supported by a grant from the US National Science Foundation (CBET-0425882 and CAREER award CBET-0644792 to Y.N.K.). Computational support from the Minnesota Supercomputing Institute (MSI) is gratefully acknowledged. This work was also supported by the National Computational Science Alliance under TG-MCA04N033.


  • Chemical langevin equations (CLEs)
  • Model reduction
  • Multiscale models
  • Stiff biomolecular systems
  • Stochastic chemical kinetics

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