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Global optimization in systems biology: stochastic methods and their applications
Eva Balsa-Canto1, J R Banga, J A Egea
1(Bio)Process Engineering Group, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain. ebalsa@iim.csic.es
Advances in Experimental Medicine and Biology
|December 14, 2011
Summary
Mathematical optimization is crucial for systems biology modeling, identification, and designing synthetic biological behaviors. New global optimization methods and software tools offer efficient solutions for these complex nonlinear programming problems.
Area of Science:
- Systems biology
- Computational biology
- Mathematical modeling
Background:
- Mathematical optimization is fundamental to systems biology, underpinning model development, identification, and the design of synthetic biological behaviors.
- Systems biology problems are often formulated as nonlinear programming problems (NLPs) with dynamic and algebraic constraints.
- The inherent nonlinearity, high constraint levels, and large number of variables in systems biology models present significant computational challenges.
Purpose of the Study:
- To present novel global optimization methods and software tools for addressing complex challenges in systems biology.
- To demonstrate the utility of these advanced optimization techniques in model identification and stimulation design.
- To provide efficient and robust solutions for nonlinear programming problems in systems biology.
Main Methods:
- Development and application of novel global optimization techniques.
- Utilizing cooperative enhanced scatter search (eSS) for complex problem-solving.
- Employing specialized software tools like AMIGO and DOTcvpSB for systems biology modeling.
Main Results:
- Successful application of new global optimization methods to systems biology problems.
- Demonstrated effectiveness of eSS, AMIGO, and DOTcvpSB in model identification tasks.
- Facilitated the computation of optimal stimulation procedures for desired biological behaviors.
Conclusions:
- Novel global optimization methods and software tools significantly advance systems biology research.
- These tools provide efficient and robust solutions for nonlinear programming problems in modeling and simulation.
- The presented approaches enhance the ability to identify models and design synthetic biological functions.
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