The results of their work were published Oct. 7 in the journal Nature Communications.
The new simulation is the largest of its kind yet, said Ilias Tagkopoulos, professor of computer science at UC Davis, who led the team.
"The number of layers, and the amount of data involved are unprecedented," he said. The dataset on which the model is based includes, for example, over 4,389 profiles of the expression of different genes and proteins across 649 different conditions. Both the dataset, named "Ecomics" and the integrated model, MOMA (Multi-Omics Model and Analytics) are available to other researchers to use and test.
The model could be useful to researchers as a fast and inexpensive way to predict how an organism might behave in a specific experiment, Tagkopoulos said. Although no prediction can be as accurate as actually performing the experiment, this would help scientists design their hypotheses and experiments. Applications range from finding the best growth conditions in biotechnology to identifying key pathways for antibiotic and stress resistance.