Michael A. Gilchrist, Ph.D.

Associate Professor
Department of Ecology & Evolutionary Biology
University of Tennessee, Knoxville

Senior Personnel
National Institute for Mathematical and Biological Synthesis
Knoxville, TN

Michael Gilchrist

Attention Potential Graduate Students & Post-Doctoral Researchers!

I am interested in mentoring motivated students and researchers with interested in learning how to develop biologically motivated mathematical models and how to apply these models to data using fundamental concepts in probability and statistics.

Graduate Students

Graduate students generally join my lab through the Dept. of Ecology and Evolutionary Biology (EEB) graduate program which provides guaranteed support for a minimum of 5 years for Ph.D. students. In order to maximize their probability of acceptance into the program, students should contact me directly with a CV and a brief description of their research interests.

Post-Doctoral Researchers

Currently, I do not have any funding to support a post-doctoral researchers, thus any potential post-doctoral researchers interested in working with me should look into the Post-Doctoral Fellowship program at the National Institute for Mathematical and Biological Synthesis. (NIMBIOS) where I can serve as a mentor. Interested researchers should also contact me to discuss potential research topics the workings of the fellowship program.

It is worth noting that the University of Tennessee Knoxville is a premier institution for the study of evolutionary and mathematical biology. There 5 mathematical biologists within EEB alone and numerous other researchers across campus. In addition, UT is home to NIMBioS, a major research center for the study of mathematics and biology. NIMBioS provides UT students and researchers the opportunity to meet with world renowned researchers in the field of mathematical biology and participate in various workshops and seminars.

Research Interests

Broadly speaking, my research interests lie at the interface of mathematics and biology. My research is motivated by the belief that (1) rigorous model formulation provides insight into the basic biological processes underlying a given phenomenon, (2) the fitting of such models to data allows us to evaluate alternative hypotheses about these processes, and (3) maximizes the amount of useful information we can extract from the data. The majority of my work focuses on understanding biological processes within the evolutionary context of natural selection, mutation, and genetic drift. The current focus of my lab is linking models of molecular scale processes to models of sequence evolution from the field of population genetics. However, I also have also focused on linking models of within-host and between-host scale processes in host-pathogen systems. While most of the work in my lab has an evolutionary focus, I am also interested in using modern, non-linear statistical techniques including Bayesian statistics, to improve our ability to analyze various types of biological data.

Genomics & Proteomics

The field of cellular biology is currently undergoing a revolution in data generation. The goal of my research in proteomics is to develop statistical models which help answer basic biological questions on protein function and expression through the use of genome scale datasets.

Figure 1: Comparison between observed and expected protein production rates for 5847 verified genes in the S. cerevisiae genome.

Protein Translation & Codon Usage Bias

Codon usage bias, i.e.~the non-uniform usage of synonymous codons within a gene sequence, is a ubiquitous biological phenomenon. Although these subtle patterns held within a gene may not seem very intriguing, they actually allow us to develop and apply fundamental concepts in evolutionary biology, such as Wright's Fitness Landscapes, while simultaneously extract information on gene expression from coding sequences themselves. My work in this area began a number of years ago with a simple model of ribosome movement along an mRNA [9]. This model allowed us to explore the role of nonsense errors (errors which lead to premature termination of protein translation) as a selective force on the evolution of codon usage bias. On a more fundamental level, this model also allowed us to develop a simple framework for linking genotype, codon usage, to phenotype, protein production costs. We can use this framework to predict the protein production rate of a given gene [13 and Figure 1)].

Figure 2: Distribution of NAI values for S. cerevisiae S288c and simulated genomes. Solid line represents null expectation.
These predictions are essentially based on the degree of adaptation a gene displays to minimize the cost of protein production through its codon usage. We've used these same concepts to develop the only biologically based measure of codon usage bias, the Nonsense Adaptation Index (NAI) [17 and Figure 2)]. This work has provided us a foundation for understanding the nature of different selective forces, such as nonsense errors and the cost of ribosome usage. In addition, we've begun expanding our modeling framework to include tRNA competition effects and intra-ribosomal tRNA-mRNA stability. One important result for this work is that missense errors are unlikely to be the primary selective force driving the evolution of codon bias, overthrowing one of the most favored hypothesis for codon bias [21].

Protein Complex Composition

One of the central goals of proteomics is to determine the function of each protein. Because most proteins function in the context of a protein complex, one of the first steps towards identifying protein function is to correctly identify protein complex composition. Working with Andreas Wagner and Laura Salter, I have developed a Bayesian framework for inferring protein complex composition from high-throughput protein interaction datasets [ 5 ]. We have applied this framework to two high-throughput datasets which use similar affinity purification techniques for identifying yeast protein complexes.

My framework is based on a probabilistic model of how the data is actually generated. The approach I have developed has the distinct advantage that it can assess the quality of a dataset based on its internal self-consistency. Our results indicate that affinity purification based techniques miss 50 to 80 % of all proteins in a complex and usually include two or three additional, non-complex proteins. These high error rates makes it hard to have much confidence inferring complex composition from a single experiment. However, because our approach is Bayesian in nature we can incorporate information on protein complexes from multiple experiments, including those from different datasets. The result is that we are able to calculate the probability two proteins are in the same complex to a surprising degree of accuracy (see Figure 3).

Life-History Evolution

Trade-offs are ubiquitous in biological systems. From an evolutionary perspective, the most important trade-offs are generally between survival and reproduction. Understanding the nature of these trade-offs and their optimal solution is the goal of my research in life-history theory [ 2 , 4 , 6 , 10 ].

Host-Parasite Coevolution

Parasites within a host generally face a trade-off between reproducing at a high rate to facilitate transmission between hosts and illiciting a rapid host immune response. Conversely, hosts face a trade-off between having an immune response which can rapidly respond to an infection and one which responds too strongly, wasting resources and potentially damaging the host.

To address how natural selection shapes parasite replication and host immune response rates Akira Sasaki and I have developed a novel framework for modeling host-parasite coevolution [ 2 ]. Our framework consists of a set of coupled models. In the within-host model, the dynamics of a parasitic infection are determined by both the parasite's replication rate within a host and the host's immune response rate. This within-host model was nested in an age-structured, epidemiology model. By coupling these two models I was able to see how the optimal parasite replication rate changes with the host's immune response rate and vice versa.

My analysis shows that for any given parasite replication rate r there is a single optimal host immune response rate a and vice versa. Consequently, in this system, hosts and parasites coevolve toward a stable evolutionary equilibrium point in (r, a) space (see Figure 4). Whether or not there will be an evolutionary arms race between the host and parasite is a function of the relative cost coefficients and the initial state of the system.

Within-Host Viral Evolution   

Much of the work on parasite evolution ignores any evolution of a parasite within a host by assuming that no host is infected by more than one strain of parasite. This assumption is reasonable approximation for short lived infections. It, however, is clearly violated in the case of chronic infections such as those caused by HIV or hepatitis viruses.

In collaboration with Alan Perelson and others, we have addressed a number of basic questions about parasite evolution within a host [4, 6]. Our work indicates that natural selection within a host will generally favor viruses which maximize the expected number of virions produced over the life span of an infected cell.  Additional analysis shows that the optimal virion production is largely driven by the relationship between a virus’ production rate, the cell’s mortality rate, as well as the infectivity and clearance rate of a virion within the host [ 6 ].

Previously, Dan Coombs and I have expanded the scope of these models to explore how and when selection on a parasite within a host conflicts with selection on the virulence and transmission of an infection between hosts [8]. This analysis relies on equilibrium assumptions of within-host processes. More recently, we have expanded this framework to include the transitory dynamics involved in the approach to the equilibrium state 15.

Fungal Fitness and Life-History Evolution

Just as parasites exploit a population of hosts and viruses exploit a population of host cells, filamentous fungi can be viewed as exploiting a population of resource patches. Using an age structured model similar in form to the ones used in my other research, Anne Pringle, Deborah Sulsky, and myself have shown that fungal fitness is proportional to total spore production over the lifetime of a patch. Based on this finding we have developed a model of fungal dynamics within a resource patch to understand the relationship between the growth and allocation strategy of a fungus and its fitness.

In our model of within-patch dynamics, the fungus extracts resources r from the patch at a rate proportional to its mass m. Extracted resources can then be allocated toward either the production of spores or hyphae (fungal mass). Our analysis indicates that the optimal allocation strategy is a dynamic one in which the fungus should grow until its mass m is equal to the natural log of the amount of remaining resources in the patch (see Figure 5). Qualitatively speaking, this type of allocation strategy is commonly found in nature, providing some empirical support for our findings. Our goal is to more rigorously test the model in the laboratory by directly measuring how fungal allocation strategy changes with mass and available resources.

Additional Research Topics

T-Cell Migration

As part of a project by Dr. Thandi Onami (Microbiology) and her graduate student John Harp, I developed models of T-cell migration and fitted them to experimental data from their lab. Our results indicate that naive and activated memory cells have different behavior within lymph tissues. [ 22 ].


Working with my colleague Dr. Russell Zaretzki (Statistics) we have developed models of mRNA tagging and shown how the number of potential tagging sites can bias the estimates of gene copy number using SAGE. We also provide computational methods for estimating and correcting for this bias [16, 18].

Molecular Evolution

In collaboration with Drs.Michael J. Hickerson and Naoki Takebayashi, I have worked to develop likelihood methods for estimating mutation rates and ancestral population sizes from molecular data with a known divergence time or event [ 3 ].

Development and Dominance

Working in collaboration with Dr. H. Fredrick Nijhout, we used a simple diffusion model of trait development to show that the non-linear nature of developmental can lead to strong dominant gene interactions between alleles [ 1 ]. While our model reinforces the Wrightian view of dominance being the by-product of other physiological processes, it is also consistent with Fisher’s view that dominance itself can evolve.


  $^*$Graduate student co-author
  $^\dagger$Post-Doctorate co-author

  1. Gilchrist, M.A., and H.F. Nijhout. 2001. Nonlinear Developmental Processes as Sources of Dominance. Genetics 159: 423-432. [Reprint]

  2. Gilchrist, M.A. and A. Sasaki. 2002. Modeling host-parasite coevolution: a nested approach based on mechanistic models. Journal of Theoretical Biology 218: 289-308. [Reprint]

  3. Hickerson, M.J., M.A. Gilchrist, and N. Takebayashi. 2003. Calculating a Molecular Clock from Phylogeographic Data: Moments and Likelihood Estimators. Evolution 57: 2216 -2225. [Reprint]

  4. Coombs, D., M.A. Gilchrist, J. Percus, and A.S. Perelson. 2003. Optimal Viral Production. Bulletin of Mathematical Biology 65: 1003-1023. [Reprint]

  5. Gilchrist, M.A., L.A. Salter, and A. Wagner. 2004. A Statistical Framework for Combining and Interpreting Proteomic Datasets. Bioinformatics 20: 689-700. [Reprint]

  6. Gilchrist, M.A., D. Coombs, and A.S. Perelson. 2004. Optimizing Within-host Viral fitness: Infected Cell Lifespan and Virion Production Rate. Journal of Theoretical Biology 229: 281-288. [Reprint]

  7. Nelson, P.W., M.A. Gilchrist, D. Coombs, J.M. Hyman, and A.S. Perelson. 2004. An age-structured model of HIV infection that allows for variations in the production rate of viral particles and the death rate of productively infected cells. Mathematical Biosciences & Engineering 1: 267-288. [Reprint]

  8. Gilchrist, M.A. and D. Coombs. 2006. Evolution of Virulence: Interdependence, Constraints, and Selection using Nested Models. Theoretical Population Biology 63: 145-153. [Reprint]

  9. Gilchrist, M.A. and A. Wagner. 2006. A Model of Protein Translation Including Codon Usage Bias, Nonsense Errors, and Ribosome Recycling. Journal of Theoretical Biology 239: 417-434. [Reprint]

  10. Gilchrist, M.A., D.L. Sulsky, and A. Pringle. 2006. Identifying Fitness and Optimal Life-History Strategies for an Asexual Filamentous Fungus. Evolution 60: 970-979. [Reprint]

  11. Ball, C.L.$^*$, M.A. Gilchrist, and D. Coombs. 2007. Modeling Within-Host Evolution of HIV: Mutation, Competition and Strain Replacement. Bulletin of Mathematical Biology 69: 2361-2385. [Reprint]

  12. Rong, L.$^*$, M.A. Gilchrist, Z. Feng. and A.S. Perelson. 2007. Modeling within-host HIV-dynamics and the evolution of drug resistance: Trade-offs in viral enzyme function and drug susceptibility. Journal of Theoretical Biology 247: 804-818. [Reprint]

  13. Gilchrist, M.A. 2007. Combining Models of Protein Translation and Population Genetics to Predict Protein Production Rates from Codon Usage Patterns. Molecular Biology & Evolution 24: 2362-2373. [Reprint]

  14. White, E.P. and M.A. Gilchrist. 2007. Effects of Temporal Structure of Individuals on the Species-Time Relationship in Two Desert Communities. Evolutionary Ecology Research 9: 1329-1347. [Reprint]

  15. Coombs, D., M.A. Gilchrist, and C.L. Ball$^*$. 2007. Evaluating the Importance of Within- and Between-Host Selection Pressures in the Evolution of Chronic Pathogens. Theoretical Population Biology 72: 576-591. [Reprint]

  16. Gilchrist, M.A., H. Qin$^\dagger$ and R. Zaretzki. 2007. Model for SAGE Data Analysis Accounting for Cleavage and Sampling Errors. BMC Bioinformatics 8: 403-411. [Reprint]

  17. Gilchrist, M.A., P. Shah$^*$, and R. Zaretzki. 2009. Measuring and detecting molecular adaptation in codon usage against nonsense errors during protein translation. Genetics 183:1493-1505. [Reprint]

  18. Zaretzki, R., M.A. Gilchrist, W.M. Briggs, and A. Armagan$.^\dagger$ 2010. Bias Correction and Bayesian Analysis of Aggregate Counts in SAGE Libraries. BMC Bioinformatics 11: 72. [Reprint]

  19. Roy$*$, B., J.N. Vaughn$^*$, B-H Kim$^*$, F. Zhou$^*$, M.A. Gilchrist, and A.G. Von Arnim. 2010. The h Subunit of eIF3 Helps to Maintain Reinitiation Competence during Translation of mRNAs Harboring Upstream Open Reading Frames. RNA 16: 748-761. [Reprint]

  20. Shah, P.$^*$ and M.A. Gilchrist. 2010. Thermosensing Property of RNA Thermometers Is Not Unique. PLoS One 5:e11308. [Reprint]
  21. Shah, P.$^*$ and M.A. Gilchrist. 2010. Effect of Correlated tRNA Abundances on Translation Errors and Evolution of Codon Usage Bias. PLoS Genetics 6:e1001128. [Reprint]

  22. Harp, J.R.$^*$, M.A. Gilchrist, and T.M. Onami. 2010. Memory T cells are enriched in lymph nodes of selectin-ligand deficient mice. Journal of Immunology 185: 5751-5761. [REPRINT]

  23. Shah, P.$^*$ and M.A. Gilchrist. 2011. Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift. Proceedings of the National Academy of Sciences U.S.A. 108: 10231-10236. [REPRINT]

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Contact Information:

Office Location Mailing Address Phone & Email
439 Hesler Biology Building 569 Dabney Hall Tel: (865)974-6453
University of Tennessee University of Tennessee Fax: (865)974-3067
Knoxville, TN 37996-1610 Knoxville, TN 37996-1610 email:     mikeg ~at~ utk dot edu
WWW: http://www.tiem.utk.edu/~mikeg/