Microbial Growth, Interactions and
Biogeographies from ‘Omics Data
University of Southern California
My lab’s contribution to CBIOMES will be three-fold: First we will use “data mining” to retrieve information about the distributions, activities, and potential activities of a broad variety of marine microorganisms, in large part from gene sequence databases. working with data experts and modelers we plan to optimize the use of these data as input towards the development of an ocean atlas of microbial identities and activities.
Second, again in collaboration with CBIOMES mathematicians and modelers, we will develop and improve models designed to use quantitative atlas data and other environmental data sources to determine associations and potential interactions among and between different kinds of microbes. Such associations could include positive ones (organisms in effect working together) or antagonistic ones (such as competition, predation, parasitism or chemical antagonism).
Thirdly we will evaluate a promising new approach to simultaneously determine the growth rates of many different kinds of microbes from the within-genome distributions of DNA extracted from mixed populations. Such growth rate information is extremely valuable when modeling an ecosystem. We expect that for some organisms at least, the information may be extracted from the billions of sequences generated by now-popular metagenomic studies, whereby the entire microbial community DNA is extracted, fragmented and sequenced as random pieces. This new approach requires determining which sequenced fragments belong in which organism and how they are ordered in the genome. We will test this method with laboratory cultures and manipulated field samples at known growth rates.