Managing complexity of cellular systems: theoretical tools for modeling of metabolic reaction networks

Description

The study on biological systems attracts more and more interest since these systems offer interesting solutions to problems in diverse areas ranging from sustainable production of chemicals and fuels, environmental problems to medical questions that pose challenges from both fundamental and applied sciences point of view.

This project is concerned with the effort of improving our current understanding on biological systems by making use of mathematical models. The purpose of these models is to predict gene targets for successful redesign of biological systems (leading to new production processes) or to identify molecular interactions (leading to new drugs). This endeavor is rather challenging not only because of the high number of different molecules in the system, the even higher number of (non-)linear interactions but also because these dynamic non-linear systems have the unique property of being adaptive, evolving and anticipatory with its environment. The motivation behind such research is numerous, e.g. from a fundamental science point of view, this attempt leads to knowledge on how these systems are (dis)functioning which in turn might induce the cure for various medical problems or alternatively from an engineering point of view, it leads to the improvement of the performance of the cells toward the production of an economically interesting product.

Being composed of many non-linearly interacting (sub)parts, the complex nature of the biological systems renders intuitive interpretation of experimental observations difficult, if not impossible. Mathematical models would help not only the structural understanding and interpretation of the data, e.g. description of the complex kinetic behavior, but also allow performing exploratory simulations, design and optimization of such systems allowing identification of targets for molecular, genetic intervention. Ultimately, the true understanding might be achieved if we can assign numbers to a biological object, simulate it on a computer and finally predict correctly its behavior.

Models for biological systems can be mainly classified under two major categories, stochastic and deterministic. Stochastic models are generally used to describe events where low number of species occurs (e.g. gene induction), while deterministic models are used to describe systems where the continuum assumption holds, i.e. on a practical sense, when there are sufficient molecules (> 1000) involved in a specific process (e.g. models for metabolic reaction networks). The below picture depicts a more detailed classification of the available techniques for modeling biological systems.

In an attempt to improve our current understanding on how the cellular systems function, this project aimed specifically to provide new insights on dynamic modeling for such systems. Within this area, we focused on the parameter estimation and parameter identifiability analysis for a kinetic model and experimental data from in vivo kinetic experiments using whole cells. A number of such tools are developed and applied for building models of industrially relevant microorganisms (e.g. Saccharomyces cerevisiae and Penicillium chrysogenum).

Dissertation

Nikerel, I.E., Managing complexity of cellular systems: theoretical tools for modeling of metabolic reaction networks, November 16, 2009, 231 pp.

Publications from dissertation

• Nikerel, I.E., Canelas, A.B., Jol, S.J., Verheijen, P.J.T., Heijnen, J.J. (2011). Construction of kinetic models for metabolic reaction networks: Lessons learned in analysing short-term stimulus response data  Mathematical and Computer Modelling of Dynamical Systems, 17 (3), pp. 243-260. DOI
• Nikerel, IE, Winden, WA van, Verheijen, PJT & Heijnen, JJ (2009). Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics. Metabolic engineering, 11, 20-30. DOI
• Nikerel, I.E., Ateş, Ö., Öner, E.T. (2008). Effect of bioprocess conditions on growth and alkaline protease production by halotolerant Bacillus licheniformis BA17 Applied Biochemistry and Microbiology, 44 (5), pp. 487-492. DOI 
• Nikerel, IE, Winden, WA van, Gulik, WM van & Heijnen, JJ (2007). Linear-logarithmic kinetics - a framework for modeling kinetics of metabolic reaction networks. Eurosim - simulation news europe, 17(1), 19-26.
• Nikerel, IE, Winden, WA van, Gulik, WM van & Heijnen, JJ (2006). A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics. Bmc bioinformatics, 7:540. DOI
• Nikerel, I.E., Öner, E., Kirdar, B., Yildirim, R. (2006). Optimization of medium composition for biomass production of recombinant Escherichia coli cells using response surface methodology, Biochemical Engineering Journal, 32 (1), pp. 1-6. DOI
• Nikerel, I.E., Toksoy, E., Kirdar, B., Yildirim, R. (2005). Optimizing medium composition for TaqI endonuclease production by recombinant Escherichia coli cells using response surface methodology Process Biochemistry, 40 (5), pp. 1633-1639. DOI