All living things require metabolism. The way an organism metabolizes nutrients is a complex process, and simulating the chemical processes that keep life going is a difficult challenge.
Theoretically, the procedure can be represented by mathematical equations with parameters specific to each organism. But practically determining those parameters, however- is a complicated matter due to the lack of experimental data.
Scientists generally need a lot of experimental data and processing power to find these parameters. EPFL scientists proposed a deep-learning-based computational framework reproducing the dynamic metabolic properties observed in cells. The framework called REKINDLE could pave the way for more efficient and accurate modeling of metabolic processes.
Ljubisa Miskovic of EPFL’s Laboratory of Computational Systems Biotechnology and co-PI of the study said, “REKINDLE will allow the research community to reduce computational efforts in generating kinetic models by several orders of magnitude. It will also help postulate new hypotheses by integrating biochemical data in these models, elucidating experimental observations, and steering new therapeutic discoveries and biotechnology designs.”
Subham Choudhury, the first author of the study, said, “The overarching aim of metabolic modeling is to describe the cellular metabolic behavior to such a degree that understanding and predicting the eﬀects of variations in cellular states and environmental conditions can reliably be tested for a wide gamut of studies in health, biotechnology, and systems and synthetic biology. We hope that REKINDLE facilitates building metabolic models for the broader community.”
The technique has direct biotechnological applications because kinetic models are crucial for numerous investigations, including those on bioproduction, drug targeting, interactions between microbes, and bioremediation.
Choudhury said, “REKINDLE uses standard, widely used Python libraries that make it accessible and easy to use. Our main goal with this study is to pave the way to make these kind of modeling efforts open source and accessible so that anyone in the synthetic and systems biology communities can use them for their own research goal, whatever they may be.”