Professor Weinreich is a theoretical population geneticist who uses a combination of experimental, analytic and simulation techniques to further understanding of the general principles of biological adaptation.

Population genetics is the subdiscipline of evolutionary biology borne of the synthesis between Darwin’s model of natural selection and the particulate nature of inheritance described by Mendel and Morgan. Despite its 100+ year history (the first breakthrough being Fisher 1918), the current, breakneck pace of experimental innovations makes this something of a golden age for the field. Much of Weinreich's work is inspired by (and often also employs) two of these recent technical opportunities in particular: the ability to evolve and observe hundreds of replicate microbial populations in well-controlled laboratory environments, and the more than 100,000-fold drop in the cost of DNA sequencing since the year 2000. 

Professor Weinreich works on the evolutionary genetics of biological adaptation, in theory and in laboratory populations of microbes. His intellectual motivations can be divided into two areas.

1. For many years Weinreich has been fascinated by the evolutionary consequences of interactions among mutations, and his lab has pursued parallel theoretical and experimental approaches to frame and answer a succession of questions in this area. 

More specifically, a mutation’s effect on its carrier is often contingent on other mutations already in the genome, and geneticists call such mutational interactions epistasis. Colloquially, we can think of epistasis as measuring our surprise at the effect some set of mutations have on an organism, given information on the effects of subsets of those same mutations (Weinreich et al. 2013). Past results include

• The theoretical significance of sign epistasis (Weinreich et al. 2005).
• The empirical significance of sign epistasis in TEM-1 β-lactamase, an enzyme that confers bacterial resistance to penicillin and related compounds (Weinreich et al. 2006).
• Inferring the dimensionality of RA Fisher's phenotypic space from data on pairwise epistasis (Weinreich and Knies 2013).
• The mechanistic basis of sign epistasis in TEM-1 β-lactamase (Knies et al. 2017).
• The theoretical and empirical consequences of higher-order epistatic interactions (Weinreich 2011,Weinreich et al 2013; Weinreich et al 2018).

Current work on epistasis is funded by NSF EPSCoR award 1736253 and includes:

• A survey of epistatic interactions and mechanistic determinants among deleterious mutations. Some time ago Wylie and Shakhnovich 2011 demonstrated that a model in which missense mutations outside an enzyme's active site only perturb folding stability is entirely consistent with data from a bulk mutational survey in TEM-1 β-lactamase from Berstein et al 2006. In collaboration with Mandar Naik (Brown MPPB) and Brenda Rubenstein (Brown Chemistry), we are testing that model using individual randomly chosen missense mutations in TEM-1 β-lactamase.
• Deep mutational scanning (DMS) and the detection of epistasis. Most current DMS datasets exhaustively examine single mutations around some wild-type, and are thus mute on the question of epistasis. In collaboration with Lorin Crawford (Brown SPH), we are now exploring computational approaches to detecting epistasis from slightly larger DMS datasets. We are also building a DMS dataset on SHV-1 β-lactamase, another enzyme that confers bacterial resistance to penicillin and related compounds. SHV-1 is roughly 30% diverged from TEM-1.

2. Beginning roughly five years ago Weinreich began working on the evolution of modifiers genes. Modifiers are a diverse class of genes that influence the fidelity with which genetic material is translated into phenotype within individuals, as well as the fidelity with which it is transmitted over space and time. Here again, he uses complementary theoretical and experimental techniques. 

Modifier mutations are to be contrasted with so-called directly selected mutations, those that directly affect their carrier’s lifetime reproductive success such as those in the β-lactamases described above. Unlike these, natural selection acts on modifier mutations only via persistent statistical associations with directly selected mutations, and recently, Weinreich's group and others have discovered a fascinating and largely unappreciated population genetic characteristic of all such mutations. Moreover, he now hypothesizes that this insight may offer a novel opportunity to synthesize the population genetics of this diverse and perplexing class of mutations. Beyond our intrinsic, theoretical interest in these effects, modifier mutations are thought to be widespread in many natural microbial populations, including those constituting metazoan microbiota and those responsible for infectious disease, as well as in populations of cancer cells.

This line of work began with an extraordinary discovery of his postdoc, Dr. Eugene Raynes (PhD in biology from the University of Pennsylvania). Eugene’s thesis research examined the evolution of mutators, modifier mutations that increase their carrier’s mutation rate. In asexual populations, mutators cause genetically linked beneficial and deleterious mutations, and a mutator’s evolutionary fate is largely determined by selection acting on these directly selected mutations. Through a combination of experiments using asexual, replicate lab populations of the brewer's yeast Saccharomyces cerevisiae, individual-based computer simulations, and analytic modeling, Eugene has now shown that mutators are selectively favored in sufficiently large populations, but selectively disfavored below some critical population size. This phenomenon is called sign inversion, and these results are detailed in Raynes et al. 2018.

Sign inversion stands in sharp contrast to the classical understanding of the population-size dependence of selection acting on beneficial and deleterious mutations. There, selection becomes less efficient as population size declines but critically, in the classical framing the direction of selection is independent of population size. 

More recently, Professor Weinreich's group realized that theirs was not the only case of sign inversion: Whitlock et al 2016 demonstrated sign inversion for modifiers of recombination, Nowak et al 2004 demonstrated sign inversion for tit-for-tat cooperators, and Gillespie 1974 demonstrated sign inversion for a mutant with elevated variance in offspring number. The implications of these findings are now an area of active research in the group.

Professor Weinreich is actively seeking new lab members at all levels from High School interns to postdocs. Please click the Teaching tab above and see the BIOL 1950/1960/2980 description for ongoing student projects. Click here to visit the lab web page. Or send him an email describing your background and interests if you would like to join his group.

ACTIVE

None

COMPLETED

NSF EPSCoR Research Infrastructure Improvement Program: Track-2 Focused EPSCoR Collaboration: Using biophysical protein models to map genetic variation to phenotypes. August 1, 2017 – July 31, 2021. $6,000,000 total D⁣ $1,396109 D&IC to Brown University. Weinreich co-PI.

NSF Division of Environmental Biology, Evolutionary Genetics Cluster DEB-1556300 award. Collaborative Proposal: Risk and reward of high mutation rate: why large populations favor mutators while small populations inhibit them. Mar 1, 2016 – Feb 28, 2020. Weinreich PI $771,000 D&IC to Brown University. Includes $21,000 Research Experience for Teachers supplement awarded Feb 1, 2017.

NIH RO1-GM095728. Developing and Testing a Novel Geometric Model of Protein Evolution. Sept 1, 2011 – Aug 31, 2018. Sole PI: DMW. $1,414,522.  

NSF Emerging Frontiers Award 1038657.  Inferring Biological Mechanism from Mutational Interactions.  Sept 15, 2010 – Aug 31, 2014. Sole PI: DMW. $259,079.

NIH RO1 GM079536. The Evolution of Malarial Antifolate Resistance. Mar 1, 2007 – Feb 29, 2012. Author and co-investigator: DMW. PI: Dr. Daniel L. Hartl. $1,600,000.

NSF Population Biology DEB Award 0343598. Molecular evolvability in theory and in a bacterial drug-resistance gene.  Feb 1, 2004 – Jan 31, 2007. Author and Co-investigator: DMW PI: Dr. Daniel L. Hartl.  $236,000.

NIH National Research Service Award F32 GM20736. Molecular evolution in the bacteriophage ф6Aug 1, 2000 – Jul 31, 2003. Sole PI: DMW. $109,164

NSF Population Biology DEB Award 9981497. Recombination, dominance, and selection on amino acid mutations.  Mar 1, 2000 – Feb 28, 2002. Co-author and co-investigator: DMW PI: Dr. David Rand.  $172,367

NSF Doctoral Dissertation Improvement Grant Award DEB-97000982. Oct 1, 1997 – June 1, 1998. Sole PI: DMW. $7,940.

Click here for Professor Weinreich's NCBI bibliography and here for his Google Scholar profile

 

2011 -- Brown University Center for Computational Molecular Biology Sabbatical Travel Award.
2010 -- Brown University Center for Computational Molecular Biology Seed Award.
2009 -- Brown University Center for Computational Molecular Biology Travel Award.
2009 -- Brown University Center for Computational Molecular Biology Seed Award.
2008 -- Brown University Center for Computational Molecular Biology Teacher Award.
2008, 2009 -- Brown University NSF/EPSCoR Proteomics Instrumentation Use Award.
2008 -- Brown University Salomon Faculty Research Award.
2007 -- Brown University Center for Computational Molecular Biology Scholarship Innovator Award.
2000 -- NIH National Research Service Award.

Society for the Study of Evolution

Society of Molecular Biology and Evolution

Member, Faculty of 1000, Evolutionary & Comparative Genetics section in Genomics & Genetics

Center for Computational Molecular Biology, CCMB

 

BIOL 0380 -- Ecology and Evolution of Infectious Disease (Fall term each year). Infectious diseases remain among the leading causes of death worldwide, and this burden is disproportionately borne by children living in low- and middle-income countries. Thus, the management of infectious disease remains a critical intellectual challenge in the 21^st century. This course will develop and apply ecological and evolutionary theory to infectious microbes and their hosts, via the detailed examination of a number of case studies. This will be accomplished by a combination of lectures, discussions, and readings drawn mainly from the primary literature. Prerequisite: Biol 0200 or equivalent, or permission of instructor.

BIOL 1430 -- Population Genetics (Fall term in odd-numbered years). Population genetics considers the genetic basis of evolution: temporal changes in the genetic composition of populations in response to processes such as mutation, natural selection and random sampling effects. Starting from first principles, this course will develop a theoretical understanding of these dynamics. We will also explore the application of these tools to genomic-scale data in order to quantify the influence of various evolutionary processes at work in natural populations. Assessments will be based on extensive problem sets and a take-home final exam. Prerequisites: MATH 0100 and one of BIOL 0470 or 0480, or permission.