IMSc Webinar

Reverse modeling metabolic networks

Andrea De Martino

CNR Nanotec & Italian Institute of Genomic Medicine, Turin, Italy

[Google Meet Link]: meet.google.com/bmk-gpxy-uyd

[Download title and abstract of the talk]: www.imsc.res.in/~asamal/seminar/AndreaDeMartino_Oct29_2020.pdf

[Abstract]:
The growth performance of microbial populations can be studied by an information theoretic approach relating the mean single-cell growth yield (`fitness') to the entropy ofthe growth-yield distribution (‘information'). Within Mass-Balance models, one finds that,for any value of the information, the achievable fitness is strictly bounded, leading to atheoretical “rate-distortion curve” in the (information, fitness) plane. Next, values of fitnessand information for E. coli populations can be inferred from experimental massspectrometry data probing growth in different conditions. For a large number ofexperimental datasets, inferred points robustly approach the theoretical bound as thequality of the growth medium improves. Besides giving insight into the interplay betweenmetabolism and gene expression, this approach can yield information that is currentlyinaccessible by other methods, both in silico and experimental.