首页|Uppsala University Reports Findings in Cyanobacteria (Machine learning predicts system-wide metabolic flux control in cyanobacteria)
Uppsala University Reports Findings in Cyanobacteria (Machine learning predicts system-wide metabolic flux control in cyanobacteria)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Gram-Negative Bacteria-Cyanobacteria is the subject of a report. According to news reporting out of Uppsala, Sweden, by NewsRx editors, research stated, "Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights fo r the study of biological systems and biotechnological applications. However, qu antitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited." Our news journalists obtained a quote from the research from Uppsala University, "In this work, we present ARCTICA, a computational framework that integrates co nstraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonst rate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by en zymes within the Calvin- Benson-Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribul ose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the mos t, limiting step within the CBB cycle. Predicted metabolic reactions qualitative ly align with prior experimental observations, validating our modelling approach ."
UppsalaSwedenEuropeCyanobacteriaCyborgsEmerging TechnologiesGram-Negative BacteriaGram-Negative Oxygenic P hotosynthetic BacteriaMachine Learning
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