OctoPus: Experimental quantification and data-based modelling of amino acid synthesis cost in Pseudomonas putida
DNA replication, mRNA transcription and protein translation, also known as the central dogma of Biology, are the molecular processes that govern the flow of genetic information within biological systems. These processes are essential for cell growth and cell survival, and all cells have essentially the same resource pool available to conduct them, consisting of tRNAs, nucleotides, ribosomes and amino acids.
The resource pool of cells is limited, and the distribution of these resources among different cellular processes, also called resource allocation, dictate the functioning of a cell. When bacteria are stressed, cellular resources flow into processes necessary for cell survival rather than bacterial growth-related processes. Another example of resource allocation in cells is the production of heterologous proteins. This host-foreign process draws resources from the depletable cell resource pool, and consequently, less resources are available for host-intrinsic processes like cell growth. This phenomenon, known as metabolic burden, hampers the applicability of heterologous protein production on an industrial scale. To understand and mitigate the effects of metabolic burden, quantification of the synthesis expenses of different cell resources is key.
One of these key cellular resources are amino acids. Vast amounts of research have been conducted on the (over)production of amino acids in E. coli, but less is known about the cost of synthesis for different amino acids (AA). The goal of this project is to quantify the synthesis cost of different amino acids in the biotechnological relevant gram-negative bacteria Pseudomonas putida. The experimental part will focus on posing a quantifiable growth burden on P. putida by depleting certain AAs in the AA resource pool. In this context, the effectiveness of different genetic approaches (single or combinatorial) in increasing the AA demand of the cell are evaluated (see graphical abstract). One of these approaches, the enzymatic conversion of AAs, will at the same time aim at generating value-added AA derivatives that could be commercially interesting. The theoretical part will focus on integrating the experimental data of the AA burden strains into a flux balance model predicting the synthesis cost of different (groups of) AAs. This information can then serve as a foundation for the design and optimization of heterologous protein production processes.
Project supervisor: M.Sc. Marleen Beentjes
Project start date: 01.02.2021