Michael A. Gilchrist, Ph.D.
Assistant Professor
Department of Ecology & Evolutionary Biology
University of Tennessee, Knoxville
Michael A. Gilchrist, Ph.D.Assistant Professor
| |
As an evolutionary biologist my research covers a wide array of topics, from intra-cellular processes to development to life-history evolution. All of this work is motivated by a keen interest in understanding the origin and optimal solution of biological trade-offs across various scales.
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.
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 1).
In addition to identifying protein function, two other major goals of proteomics is to understand the intra-cellular factors controlling a protein’s abundance and the selective forces determining its codon usage. To address these two goals I have been working with A. Wagner to understand how codon usage and mRNA abundance affect protein production rates. In a recently accepted study [ 9 ] we have developed a simple model of ribosome movement along an mRNA 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.
Our model is providing the foundation for attempting to infer the exact selective force of nonsense on codon usage bias and how this selective force changes with codon position. An illustration of this is a Stochastic Evolutionary Model of Protein Production (SEMPPR). In a recent publication I show how our protein translation model can be integrated with a model of gene fixation to make reliable, quantitative predictions about the protein production rate [13]. These predictions are essentially based on the degree of adaptation a gene displays to minimize the cost of nonsense errors through it codon usage.
In addition we can use this same model to better interpret available high-throughput dataset such as those measuring ribosome density on mRNAs for different genes. Insight on these forces have important implications for our understanding and interpretation of molecular evolution data.
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 , 8 , 9 ].
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 3). 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, 7]. 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 [ 7 ].
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 14.
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.
Working in collaboration with 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.
In collaboration with Michael 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 ].
| Michael A. Gilchrist Department of Ecology & Evolutionary Biology 569 Dabney Hall University of Tennessee Knoxville, TN 37996-1610 |
Tel: (865)974-6453 Fax: (865)974-3067 email: mikeg at utk dot edu WWW: http://www.tiem.utk.edu/~mikeg/ |