Publication details

Title On the systematic development of large-scale kinetics using stability criteria and high throughput analysis of curated dynamics from genome-scale models
Document Type Article
Year 2023
Authors K. Mexis, S. Xenios, A. Kokosis
Source Computer Aided Chemical Engineering Volume 52,pp 2729-2734, 2023
Keywords metabolic engineering; machine learning; rule extraction; S.Cerevisiae; muconic acid
Abstract This paper is part of a general effort to integrate disjoint stages of the Design-Build-Test-Learn cycle that addresses the simultaneous design of biocatalysts with process engineering. Engineering kinetics are currently based on regression studies based on experimental data with limited reference to the underlying reaction pathways. Instead, the ORACLE framework offers an attractive environment to generate populations of large-scale (curated) dynamics and a platform for in-silico kinetic models to check for physiological relevance and stability. Stability checks have reported low rates of success and the paper explains a systematic approach that combines deterministic methods and data analytics to accelerate realizable kinetics that could be set a basis to connect the dynamics of the cell with the dynamics of the process. The work is demonstrated with the production of muconic from S. cerevisiae which is achieved by shunting the shikimic pathway. The ORACLE framework consists of two reduction stages where the first one ensures that the generated kinetic models are physiologically relevant and the second one checks the model’s stability. The conventional method produced 370 physiologically relevant models out of which 70 were stable (19.2%) whereas our approach increased uptake of acceptable solutions to 97.7%.
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