Dynamic optimization of distributed biological systems using robust and efficient numerical techniques

BMC Systems Biology

Antonio A Alonso¹  Julio R Banga¹  Maria-Sonia G García¹  Eva Balsa-Canto¹  Carlos Vilas¹ 

[1] BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello, 6, 36208 Vigo, Spain

关键词: Pattern formation and control;    Hybrid optimization methods;    Global optimization methods;    Reduced order models;    Distributed biological systems;    Dynamic optimization;   

Others  :  1143862
DOI  :  10.1186/1752-0509-6-79

 received in 2011-12-01, accepted in 2012-06-22,  发布年份 2012

【 摘 要 】

Background

Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.

This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques.

Results

Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model.

Conclusions

In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.

【 授权许可】

   
2012 Vilas et al.; licensee BioMed Central Ltd.

【 图 表 】

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