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(Speaker Term: 7/1/13 - 6/30/15)


Department of Biological Sciences

University of Idaho

Gibb 134

875 Perimeter MS 3051

Moscow, ID  83844-3051 


Phone:  208-885-8594



Speaker’s Website:




Optimality and Tradeoffs in Metabolism: Do Our Models and Intuitions Hold Up?

Optimality and constraints are two powerful evolutionary concepts that underlie some of the most critical physiological and ecological models in use, yet the assumptions have rarely been tested. I will first discuss how the oft-used genome-scale models of metabolism (flux balance analysis, or FBA) assume biochemical networks work optimally due to prior selection. Examining our results for long-term experimental evolution of Escherichia coli, as well as existing data for a couple of other experiments, I will demonstrate that FBA fails to consistently predict the evolution of central metabolism. Second, I will show how our analysis of the same long-term populations of E. coli have actually exhibited very few tradeoffs on alternative compounds, show multiple novel gains of function, and show that the major force driving tradeoffs has been the neutral degradation that has occurred in lineages that have become mutators.                      


Evolution and Modeling of Cooperation in Synthetic, Spatially-structured, Multi-species Communities

Although microbiologists have almost exclusively studied microbes one species at a time in liquid medium, the vast majority of microbes live in structured, diverse communities. I will discuss our work showing that under these conditions, mutually cooperative processes such as cross-feeding can emerge and be refined due to the local feedbacks that direct benefits disproportionately to the genotypes that bear the costs of exchange. Furthermore, I will present how genome-level models can be embedded in a community context to predict growth and environmental changes through time and space, and thus provide testable predictions regarding the effects of spatial structure upon the evolutionary fate of genotypes and the ecological processes in which they are imbedded.    


Epistasis and Adaptation: Moving Toward Predicting Genetic Interactions During Evolution

If beneficial mutations interact in non-linear ways (i.e., epistasis), the later fate of populations can be strongly influenced by which mutations first occurred. I will first present how a general trend of diminishing returns can decelerate adaptation, and that a simple model of physiology could predict the phenotype of strains with 2, 3 or 4 alleles directly from the properties of the single mutant. Second, I will discuss how mutations that occurred in different replicate populations can interact quite poorly. A model involving catalysis and enzyme expression costs could predict these interactions, and should represent a very general trend across systems.    


Surprises at Two Scales Regarding the Raw Material of Adaptation: Selection on Codon Usage, and the Distribution of Fitness Effects of all Possible Beneficial Mutations

Population genetic theory is emerging to enable predictions of the dynamics of fitness improvement if one assumes knowledge of what selective effects of mutations are available, but we have very little data with information as to what is possible. Considering adaptation of whole organisms, I will describe the first experiment that has ever estimated this distribution of fitness effects of mutations, and the fact that it suggested mutations with large benefits were more likely than moderate ones. Changing focus to individual genes, I will present surprisingly strong selective effects that are possible from synonymous mutations, and how adaptation can rapidly and repeatedly fix poorly encoded alleles.    


Transposable Elements and Evolution: From Adaptation in Lab to Inferences from the Tree of Life

Transposable elements such as insertion sequences (ISs) have alternatively been viewed as genomic parasites or as powerful means to generate beneficial mutations. From work with experimental populations in the laboratory, I will demonstrate the remarkable diversity of IS-mediated mutations that contributed to adaptation. Turning to comparisons among natural strains, a global analysis of microbial genomes has revealed the volatility of IS "infections," with several surprises regarding barriers to exchange and persistence.



Chris Marx's primary interest is in using experimental evolution as an approach to quantitatively link cellular physiology to evolutionary and ecological outcomes. His ultimate goal is to move toward a degree of predictability, whereby models from metabolic engineering are merged with those from population genetics in order to forecast the probabilities of future evolutionary trajectories or ecological states based upon the internal function of the organisms involved. This research model builds upon his diverse training in biology and environmental engineering (double major at MIT; award for top student in environmental engineering), microbial physiology (U. Washington; award for top student), and evolutionary biology (Michigan State; NSF Microbial Biology Postdoctoral Fellowship). He has published in twenty papers since 2009 (including Science, PLoS Genetics, Cell Reports) and received six research grants (NIH, 2x NSF, 2x DOE, DoD; four as PI), including notably an NSF CAREER Award. Additionally, he has been quite active in teaching and outreach, having developed three courses, taught a course yearly in the Extension School, and founded the website for the dissemination of educational materials within the intersection of microbiology and evolutionary biology.



Primary Division:  R (Evolutionary & Genomic Microbiology)

Secondary Division:  K (Microbial Physiology & Metabolism)



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