Metabolic system identification and optimization in continuous culture

Abstract

To date, there still exist some uncertain factors in the continuous fermentation of glycerol to 1,3-Propanediol (1,3-PD) by Klebsiella pneumoniae because of the limitation in bio-techniques. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of the substrate and the product across the cell membrane of the biomass. On the basis of different inferences of transport mechanisms, we reconstruct various metabolic systems and develop their dynamical systems. To determine the most reasonable metabolic system from all possible ones, we give a quantitative definition of biological robustness and propose an identification model on this basis. An improved Particle Swarm Optimization algorithm is developed to solve the identification model. Numerical results show that the identified system can describe the fermentation process well. Furthermore, to maximize the concentration of 1,3-PD, an optimization model is proposed. Numerical results show that the concentration of 1,3-PD can be increased considerably by employing the obtained optimal strategy.

Keywords:

• continuous culture
• nonlinear dynamical system
• biological robustness
• metabolic system identification
• IPSO algorithm

2000 AMS Subject Classifications :

• 49J15
• 49K15
• 93C15
• 93C95

Acknowledgements

This work is supported by the National Natural Science Foundation Grant (No. 10871033), the Natural Science Foundation for the Youth of China (No. 11001153), the Shandong Province Natural Science Foundation of China (No. ZR2010AQ016) and Fundamental Research Funds for the Central Universities.