The Stanford researchers' virtual model of M. genitalium was trained with heterogeneous data and reproduces independent experimental data across multiple cellular functions and scales. It provides a global analysis of the use and allocation of energy in the cell. It also identifies common molecular pathologies underlying single-gene disruption phenotypes.

Bioengineering researchers at Stanford University have created a computational model of an entire organism, according to a report published in Cell.

The Covert Lab incorporated more than 1,900 experimentally observed parameters into their model of the tiny parasite Mycoplasma genitalium.

This model lets them predict cellular behaviors that haven't been observed, as well as new biological processes and parameters.

The organism modeled is Mycoplasma genitalium, or M. genitalium, the smallest known genome that can constitute a cell.

"We synthesized research from the literature, but we also performed our own experiments," team leader Markus Covert, assistant professor of bioengineering at Stanford University, told TechNewsWorld.

The team went through hundreds of reports, and the model also "points out aspects of what we know based on the literature that are not internally consistent," Covert said. "These areas then become tagged as hot spots for further experimentation."

The team used data from more than 900 scientific papers to spell out every molecular interaction known that takes place in the life cycle of M. genitalium. It used that data, together with its own experiments, to create a computational model of the organism that incorporates more than 1,900 experimentally observed parameters.

The model integrates 28 submodels of cellular processes -- cell functions and variables -- grouped in five categories indicated by colors: DNA represented by red; RNA by green; proteins by blue; metabolites by orange; and all other processes by black. Colored lines between the variables and submodels indicate the cell variables predicted by each submodel.

The model was trained with heterogeneous data and reproduces independent experimental data across multiple cellular functions and scales. It provides a global analysis of the use and allocation of energy in the cell. It also identifies common molecular pathologies underlying single-gene disruption phenotypes.

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Virtual Organism Reveals Secrets of Cellular Processes