A resource allocation model for the growth of photosynthetic cells
It is vital to understand how evolution shaped the metabolic network and metabolic fluxes of photosynthetic organisms. This understanding is highly relevant from a fundamental point of view, but it should also allow systems biology strategies for the prediction of metabolic fluxes that will aid in the design of synthetic cells.
Determining the important physical and biochemical constraints is a fundamental challenge for systems biologists. Constraint-based reconstruction and analysis (COBRA) offer one class of methods for biological network computation at the system level, using reconstructions of the biochemical network on a genome-scale. In particular, Flux Balance Analysis (FBA) simulates flux distributions by optimizing a cellular objective, such as maximizing growth rates subject to physicochemical, regulatory, and environmental constraints. In FBA, all constraints are linear. The resulting computational utility comes at the cost of ignoring reaction kinetics and requiring sufficient concentrations of enzymes to catalyze the anticipated metabolic flux. However, it seems that the predicted results are quite inaccurate beyond the identification of essential genes, likely because metabolite concentrations are not taken into account.
A new modeling scheme, growth balance analysis (GBA), which explicitly considers metabolite concentrations, can facilitate a comprehensive understanding of phenotypic behavior from fundamental biochemical and biophysical constraints; it can thus provide mechanistic insights at the systems level. By applying this methodology in unicellular photosynthetic organisms, we can demonstrate this context capability in the understanding allocation of cellular resources.
Starting date: 01.02.2021 / Doctoral Researcher
Thesis committee members: Prof. Dr. Martin Lercher, Prof. Dr. Ilka Axmann, Prof. Dr. Oliver Ebenhöh
Ghaffarinasab, S., & Motamedian, E. (2021). Improving ethanol production by studying the effect of pH using a modified metabolic model and a systemic approach. Biotechnology and Bioengineering, 118(8), 2934-2946. doi.org/10.1002/bit.27800