期刊
IEEE CONTROL SYSTEMS LETTERS
卷 7, 期 -, 页码 1903-1908出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCSYS.2023.3282699
关键词
Biomass; Statistics; Sociology; Biological system modeling; Mathematical models; Biochemistry; Optimization; Cellular dynamics; population balance models; flux balance analysis; balanced growth
For more than two decades, Flux Balance Analysis (FBA) has been successfully used to predict growth rates and intracellular reaction rates in microbiological metabolism. However, the analysis often overlooks the segregation or heterogeneity between different cells. In this study, an extended FBA method is proposed to model cell size distributions in balanced growth conditions. The mathematical description of balanced growth in terms of cell mass distribution, quantified by the Number Density Function (NDF), is presented.
For over two decades, Flux Balance Analysis (FBA) has been successfully used for predicting growth rates and intracellular reaction rates in microbiological metabolism. An aspect that is often omitted from this analysis, is segregation or heterogeneity between different cells. In this letter, we propose an extended FBA method to model cell size distributions in balanced growth conditions. Hereto, a mathematical description of the concept of balanced growth in terms of cell mass distribution is presented. The cell mass distribution, quantified by the Number Density Function (NDF), is affected by cell growth and cell division. An optimization program is formulated in a general manner in which the NDF, average cell culture growth rate and reaction rates per cell mass are treated as optimization variables. As qualitative proof of concept, the methodology is illustrated on a core carbon model of Escherichia coli under aerobic growth conditions. This illustrates feasibility and applications of this method, while indicating some shortcomings intrinsic to the simplified biomass structuring and the time invariant approach.
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