Speaker
Description
To this day, there is still no existing quantitative model that can simulate the reaction rates and concentration changes in the comprehensive biochemical reaction network of a cell. Such a simulation model would be useful for understanding the nature of living cells.
A primary barrier is the lack of accurate parameters. Key biochemical parameters are notoriously difficult to measure, with published values for the same reaction often varying by orders of magnitude. This uncertainty severely limits the predictive power of mechanistic models. Furthermore, while advances in machine learning have enabled protein structure prediction, predicting quantitative functional properties (e.g., catalytic constants) remains a major hurdle, largely due to the scarcity of high-quality in vivo data for model training.
Here, we present a framework that overcomes this limitation. Using convex optimization, we integrate mechanistic constraints with high-throughput measurements, and demonstrate that this approach yields large-scale estimates of protein functional parameters that are consistent with in vivo phenotypes and diverse experimental data. These parameters enable accurate mechanistic simulation of cellular metabolism and provide a rich, quantitative dataset of biomolecule function, creating a valuable resource for training next-generation predictive models for protein design.
