Reverse Ecology

About

Reverse Ecology is the application of genomic approaches to living systems to uncover the genetic bases of functional variation in nature.  By discovering the genetic markers that are associated with a particular habitat or a distinct phenotypic trait, one can find the targets of natural selection with little knowledge of how selection targeted that trait.  This can be accomplished if a dense sample of random markers across the genome is surveyed, and only a subset of the markers shows a repeatable association between genotype and phenotype. The tools are now in place to extend this approach to communities and ecosystems.  If microbes are surveyed across an environmental gradient, the proportions of functionally equivalent neutral ‘species’ will not vary in a predictable pattern, while non-neutral ‘species’ will track the gradient.  These approaches have been limited to a few model organisms or communities where the genes or species are easily manipulated.  High throughput next generation sequencing technologies have redefined the notion of a ‘model’ organism.  With deep sequence information from virtually any (formerly) non-model organism one can ask biological questions at any scale: ecological, physiological, developmental, transcriptional, etc.  These new technologies will drive how genomes are sampled in the future, and are blurring the intellectual boundaries between ecosystems ecologists, microbial geneticists, biogeochemists, and computational biologists. There is a pressing need to train a cohort of PhDs who can integrate this flood of information into novel insights of how organisms function in their environments.

The Brown-MBL IGERT in Reverse Ecology will launch a new graduate program with this primary goal in mind.  Four training modules will serve as the core curriculum: 1) A year long immersion course targeting environmental variation at Long Term Ecological Research (LTER) sites run out of MBL where teams of students identify questions, design an experiment, sample the environment, perform high throughput sequencing to address those questions, learn genomic and computational analyses, develop inference tools and statistical skills, and prepare a multi-authored manuscript; 2) build a foundation of depth and breadth through targeted course work in biology, computer science, and applied math and statistics; 3) participate in jointly mentored research rotations where students and faculty from across disciplinary boundaries engage in ongoing research projects;  4) develop career skills in a new course on scientific professionalism in a global context where grant writing, public speaking, ethics, diversity and international perspectives on science are integrated.  The research themes will involve three broad areas where Brown and MBL are collaborating: 1) microbial and comparative genomics where diversity is explored from a phylogenomic perspective; 2) organismal responses to environmental gradients and stressors, where field experiments uncover how genetic variation responds to environmental change; 3) community genome assembly where the problem of assembling intact genomes from environmental samples is addressed using computational tools and focused samples from natural and experimental microbial communities.

The intellectual merit of this program is that a new wave of genomic technology is sweeping across all fields of biology and effective application of these tools will require new skills at the interface of the life, computer, and statistical sciences.  We will train PhDs at the emerging interface where these tools are applied to advance our understanding of how organisms function in their environments.  The broader impacts of this graduate program will be to establish a new model of doctoral training in the sciences, where PhDs are exposed to university, institutional, and corporate environments and are trained to be leaders in the identification and integration of scientific questions across formerly distant disciplines.