Development and application of Genetic Algorithms to optimize conditions for cultivation of microorganisms

From Master Projects
Jump to: navigation, search


About Development and application of Genetic Algorithms to optimize conditions for cultivation of microorganisms


Description

Improving the culture efficiency of newly isolated microorganisms is a long and laborious project, because of the many parameters that influence growth (e.g., temperature, pH, nutrient concentration, oxygen concentration). A culture medium with 16 components and 3 concentrations generates more than 43 million of possible combinations. To avoid this ‘trial-and-error- based’ approach, we will develop so-called 'genetic algorithms' (GA) to optimize culture conditions and so to improve the culture efficiency of isolated strains. GA use the principles of natural evolution to solve complex optimization problems. In successive iterations, the GA will suggest better culture conditions, which will be tested experimentally. The cycle continues until a suitable result is achieved.

In this research project, you will design a GA and test its performance in a computer model (in silico). A good GA would rapidly find the right in silico “growth medium” (ratio between glucose, amino acids, and other building blocks) for optimal growth in flux balance analysis models of E. coli or other organisms (Varma and Palsson, 1994; Van Hoek and Merks, 2012). In this problem, a well-designed GA - if performed manually in the lab - would require as little lab work as possible.

Supervisors

Further reading

  • Convert, M.W., et al. 2008. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24: 2044-2050.
  • Hutwimmer, S., et al. 2008. Algorithm-based design of novel synthetic media for Metarhizium anisopliae simulating its nutritional conditions in the environment. J. Appl. Microbiol. 105: 459-468.
  • Nagata, Y. & Chu, K.H. 2003. Optimization of a fermentation medium using neural networks and genetic algorithms. Biotechnol. Letters 25: 1837-1842.
  • Van der Star, W.R., et al. The membrane bioreactor: a novel tool to grow anammox bacteria as free cells. Biotechnol. Bioeng. 101: 286-294.
  • Van Hoek, M.J.A. and Merks, R.M.H. 2012. Redox balance is key to explaining full vs. partial switching to low-yield metabolism. BMC Syst Biol, 6:22.
  • Varma, A. and B.O. Palsson. 1994. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60: 3724-3731.