Michael A. Gilchrist, Ph.D.
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.
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.
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.
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.
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 . 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)].
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).
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 ].
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.
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 . 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.
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.
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].
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 ].
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|
|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|