While Motters 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.
|Contact: Megan Fellman|