Multi-scale models for gene network engineering

Yiannis N. Kaznessis

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


With current advances in biological knowledge, the potential exists for engineering novel gene regulatory networks, which allow the timely control of protein expression. Genome projects identify the components of gene networks in biological organisms, gene after gene, and DNA microarray experiments discover the network connections. Yet, the static pictures these experiments give cannot provide insight on the dynamic behavior of gene networks. The large number of components and interactions involved in dynamic gene regulation warrants a quantitative, computational perspective for investigating the dynamic behavior. The challenge lies with the fact that the timescales of phenomena involved in transcription/translation span multiple orders of magnitude. In this paper, multi-scale simulation methods developed to model gene regulatory networks are discussed. Details are provided for modeling biomolecular systems away from the thermodynamic limit and a hybrid algorithm is presented for simulating stochastic systems that contain both discrete and continuous representations. These simulations can provide useful insight for rationally engineering the components and the connections of novel gene network modules. Two examples, the bistable switch and the oscillator, are discussed. These examples demonstrate that ensembles of stochastic trajectories can provide insight into the dynamics of biomolecular interaction networks. This insight can guide the changes needed for the network to exhibit the desired dynamic behavior.

Original languageEnglish (US)
Pages (from-to)940-953
Number of pages14
JournalChemical Engineering Science
Issue number3
StatePublished - 2006

Bibliographical note

Funding Information:
This work was supported by grants from the National Science Foundation (BES-0425882 and EEC-0234112). 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 equation
  • Chemical master equation
  • Discrete-stochastic
  • Gene circuit engineering
  • Hybrid models
  • Multi-scale models
  • Regulatable gene networks


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