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Promotion talk.

Promotion Talk.

https://profiles.ucr.edu/app/home/profile/loralee

lab website: https://llarios12.wixsite.com/lariosecology

My research is focused on the evolutionary genomics of insect-pathogen interactions, emphasizing such questions as how natural selection operates on host immune systems and why individuals vary in susceptibility or resistance to infection. In my group, we like to think of the host as an assemblage of interacting physiological processes, where the immune system is embedded in the overall physiological context of the host. This motivates us to consider effects of abiotic environment on immune defense and means that genetic determination of variation in resistance may lie in genes outside of the canonical immune system. This thinking also extends directly to the evolution and mechanism of life history constraints. Importantly, the host is itself the “environment” in which an infecting pathogen lives, and differences in host physiological state or abiotic environment can alter microbial behavior and therefore ultimate outcomes of infection. Our overarching goal is to consider host and pathogen as interacting components of a single system, shaped by the environment, that ultimately determines the outcome of infection and disease. We primarily use bacterial infection in Drosophila melanogaster as an experimental model to deconstruct elements of the unified system, studying the components in tractable modular pieces. Understanding the dynamics of unified host-pathogen-environment systems is crucial, because these dynamics determine the ecology and evolution of disease in natural settings with consequence at higher biological scales

Topics to discuss foundational theories of ecology and evolution. 

CRN TBD  will be offered in Winter and Spring

I am an evolutionary geneticist focused on questions related to adaptation, speciation, and gene regulatory evolution. We synthesize population genetic, systems biology, and statistical genomics approaches to address fundamental questions in evolutionary biology. Our research involves diverse systems but focuses on house mice and other rodents.

Lab Website: https://www.katyamack.com/

Priya Moorjani is an Associate Professor in the Department of Molecular & Cell Biology and the Center for Computational Biology.  Her lab focuses on using statistical and computational approaches to study questions in human genetics and evolutionary biology. A central aim in the lab is to understand the impact of evolutionary history on genetic variation and to apply this knowledge to learn about human history and biology. To this end, we use genetic data from ancient specimens and present-day humans and primates to learn about: (1) how different evolutionary processes such as mutation rate evolve across primates, (2) when key events--such as introgression and adaptations--occurred in human history, and (3) how we can leverage these patterns to identify genetic variants related to human adaptation and disease. The research in the lab involves both development of new methods and large-scale genomic data analysis.

The Pennell lab uses phylogenetic trees to understand how evolutionary, epidemiological, and immunological processes shape patterns of diversity. To study this, we develop statistical/computational methods, derive theoretical models, and conduct large-scale empirical analyses. Our group uses “tree thinking” to address three major types of biological questions.First, we aim to understand how population genetic processes shape both the long-term evolution of phenotypes as well as the genetic architecture that underlies them. Recently our lab has been particularly focused on the evolution of functional genomic characteristics, such as gene expression. In this work we want to understand how selection, mutation, and drift shape the amount of mRNA or protein a cell produces.Second, we investigate the evolution of the adaptive immune system. Specifically, we study how the genes that encode antibodies and T-cell receptors diverge among populations and species and the consequences this evolution has on the ability of an organism to defend itself from pathogens.Third, we study the processes that generate diversity in the number of biological lineages. This research applies to a diverse array of fields from cell biology to epidemiology to macroevolution.

The Schrider Lab develops and applies computational tools to make inferences about evolution from population genetic datasets. Our research areas include but are not limited to the population genetics of adaptation, genomic copy number variants and other weird types of mutations, and the application of fancy machine learning tools to evolutionary questions.The Schrider Lab looking to grow. If you have interest in joining then please don’t hesitate to contact me.Broadly, the Schrider Lab is interested in a number of problems in population and evolutionary genomics, particularly in humans, the fruit fly Drosophila melanogaster, and the malaria vector mosquito Anopheles gambaie. Our main research areas are as follows:

The impact of natural selection on genetic variation

The patterns of genetic variation that we observe among individuals are shaped by several evolutionary forces. First, all variation is the result of mutational mechanisms (e.g. DNA replication error) that introduce new alternative versions of a gene (i.e. alleles). The fate of many of these alleles is determined by random chance, but those with strong enough fitness benefits will be subject to natural selection. Beneficial mutations will rapidly increase in frequency thereby facilitating adaptation, while harmful mutations will quickly be eliminated. These forces will shape patterns of variation in and around the selected region of the genome in characteristic ways. We work to uncover these signatures of selection, and to elucidate their impact on genetic diversity across the genomes of humans and other species. Our work in this area has produced evidence that adaptation has had a larger impact on genomic diversity in humans than previously appreciated.

Using machine learning to perform more powerful inference from population genetic data

One of the overarching goals in population genetics is to be able to examine an alignment of gene sequences from multiple individuals and infer the evolutionary forces shaping the diversity across sequences. These phenomena include population size changes, migration across populations, and natural selection. Directly drawing inferences about these forces a sequence alignment, which is simply a large matrix of As, Cs, Gs, and Ts, is far from straightforward. Often, the alignment is summarized by a statistic (e.g. Tajima’s D), and extreme values are taken as evidence of natural selection or demographic changes. One problem with this approach is that by boiling the data down to a single number one may lose a fair amount of potentially useful information. We have experimented with applying machine learning tools, which are well suited for large multidimensional data sets (e.g. a large vector of summary statistics rather than a single one), in order to make population genetic inferences while retaining as much information from the original data set as possible. These efforts generally yield far more accurate inferences than those from more traditional methods, and have enormous potential to drive new biological discoveries going forward as data sets continue to grow larger in both size and dimensionality.​

Surveying genomic structural variants and investigating the evolutionary forces acting on them

It seems that in common usage the word “mutation” is often synonymous with a single base change, such as a replacement of an adenine nucleotide with a guanine. However, many mutations affect more than a single base pair. For example, large genomic duplications or deletions, which can add or remove up to millions of base pairs at a time, occur quite frequently. Inversions, which can cause a large chunk of a chromosome to flip its orientation, are also common. These mutations, referred to as structural variants, are more difficult to detect than simple base changes even using modern DNA sequencing techniques, but often have greater consequences for both disease risk and evolution. We develop tools for detecting structural variants from DNA sequencing data, and work to uncover their evolutionary consequences: What fraction of these mutations are harmful? How often are they adaptive? In addition to structural variants, we also examine some other intriguing yet understudied mutational oddities such as multinucleotide mutations.

Dr. Jeffrey E. Barrick's undergraduate and graduate research focused on applied molecular evolution and bacterial genetics. He was a postdoctoral researcher with Richard Lenski and Charles Ofria at MSU studying bacterial genome evolution and artificial life from 2006 to 2011. Barrick was a professor of molecular biosciences at the University of Texas at Austin from 2011 to 2025. During this time, he led collaborative NSF, DARPA, ARO and USDA projects that developed methods for genetically engineering bacterial symbionts of insects. He is the recipient of an NIH Pathway to Independence Award and an NSF CAREER Award. He served on the Defense Science Study Group and is a fellow of the American Academy of Microbiology. Barrick returned to MSU as a Hannah Professor and became a member of the Department of Entomology in 2025. He is also affiliated with the Department of Microbiology, Genetics & Immunology.