Work with power grids leads to cell biology discovery

“Our research is based on optimizing the use of resources already available in the cell,” said Motter. “We are exploring existing reactions and genes in the cell that the cell would not use or use to a lesser degree under normal conditions. This is different from traditional gene therapy, which is based on introducing new genes into the cell -- with its own advantages and problems because of that.”

The team’s use of predictive models is similar to how physicists use models, for example, to determine the position of the moon tomorrow at a specific time. Thanks to the recent wealth of available biological information, computational scientists now are beginning to develop quantitative models of biological systems that allow them to predict cellular behavior.

In one in silico experiment (via computer simulation) with E. coli, the researchers found that the deletion of one gene is lethal to the cell but when that same gene is removed along with other genes, it is not lethal. The gene, it turns out, is only essential in the presence of other genes. This touches the issue, says Motter, of whether organisms have an unconditional set of essential genes.

While Motter’s team has not done actual laboratory experiments, they have used their computational results to re-interpret and explain specific recent experimental results. They have applied physics methods to solve a biological problem. Their method, for example, can identify the genes whose removal restores growth in gene-deficient mutants of E. coli and S. cerevisiae, a type of yeast.

“From a phylogenetic viewpoint, yeast is more similar to humans than E. coli,” said Motter, a member of the Northwestern Institute on Complex Systems. “Of course, there is a distance between single-celled organisms and human cells, but our results should be seen as proof of principle. Many experimentalists are interested in our work, and part of this interest comes from its potential for disease treatment research. This work is a concrete application of complex networks to solve a real problem, and, as such, also requires substantial involvement of network theorists.”

“Needless to say, this work is built on previous research and would not have been possible without the very significant contribution of my collaborators,” said Motter.

In addition to Motter, other authors of the paper, titled “Predicting synthetic rescues in metabolic networks,” are Natali Gulbahce, of Los Alamos National Laboratory and Dana Farber Cancer Institute; Eivind Almaas, of Lawrence Livermore National Laboratory; and Albert-László Barabási, of Northeastern University.

The Molecular Systems Biology paper can be viewed at http://www.nature.com/msb/journal/v4/n1/full/msb20081.html.