IBEST research - current collaborations
The IBEST community encompasses three disciplines: biological, mathematical and computational sciences. A hallmark of IBEST is the degree of collaboration among the members of the community. The research projects for a few of the past and continuing collaborations are listed here.
IBEST - research - Spatial Dynamics of Plasmid-Bacteria Interactions
right: Simulation: Growth rate ratio: 0.65 Segregation rate: 0.005
Steve Krone (Mathematics) and Eva Top (Biological Sciences) are collaborating on an NIH-funded project that is a joint theoretical and experimental investigation into the role of spatial structure in the spread and persistence of self-transmissible antibiotic resistance plasmids. Plasmids play a central role in the spread of antibiotic resistance among bacterial species, thereby decreasing the effectiveness of various chemotherapeutic agents for the treatment of infectious diseases. These mobile genetic elements are very common in bacteria and their horizontal transfer is key in the adaptive evolution of these organisms. The long-term goal of this study is to understand the population biology of transmissible plasmids in spatially structured microbial communities: What are the mechanisms that drive the horizontal transfer and persistence of these mobile genetic elements in bacterial communities? Why do they persist, even in the absence of selection for any of the genes they carry? How does the spatial structure of natural microbial communities influence the ecological and evolutionary dynamics of plasmid-bacteria interactions? The theoretical part of the project entails construction of 2-dimensional and 3-dimensional stochastic cellular automata (CA) models that can be used to accurately predict the spread and persistence of natural antibiotic resistance plasmids in bacterial colonies growing on agar surfaces and in biofilms. These two settings are needed for carefully controlled comparisons with non-spatial (liquid) environments and estimation of spatially relevant parameters, and for understanding the extent to which the complex spatially heterogeneous structure of biofilms further influences plasmid transfer and persistence. To validate and/or modify the models, a sequence of parallel in vitro and in silico experiments are performed. Each such pair of experiments serves to (a) validate or reject the particular model as pertains to a specific biological hypothesis; (b) suggest model refinements; (c) test the biological hypothesis; and (d) formulate alternative hypotheses in the event that the original hypothesis is rejected.
IBEST - research - Microbial Community Analysis 2
Terry Soule, James Foster and Conrad Shue, computer scientists, have been working with Larry Forney and Stephen Bent, microbial ecologists, to analyze the diversity of bacterial species from various environments. This collaboration, funded in part by the Proctor and Gamble Company, led to the MiCA website: a suite of web-based tools that enables researchers to perform analyses of microbial community structure based on terminal-restriction fragment length polymorphisms (T-RFLP). more…
IBEST - research - Evolving Ecological Networks
Steven Krone from Mathematics and Terry Soule from Computer Science are collaborating on a project entitled Evolving Ecological Networks. This is a simulation study that seeks to explore the structure of complex communities and aspects of their stability. In particular, they grow in silico communities of interacting species that are subject to Lotka-Volterra dynamics modeling competition, mutualism, and predator-prey interactions. These dynamics lead some species to go extinct, altering the network and sometimes causing instabilities that lead to further extinctions. They also model invasions of new species and, with microbial communities in mind, mutation and evolution. Rather than imposing some pre-determined structure on the species interactions, their goal is to determine general organizing principles of community construction. They are interested in knowing which combinations of interactions promote stability, how much connectivity is good for the community, the effects of high within-species diversity (quasispecies) on community evolution, etc.
IBEST - research - Decision Theoretic Approach to Model Selection
Paul Joyce from Mathematics and Jack Sullivan from Biological Sciences collaborated on a project entitled Decision Theoretic Approach to Model Selection for Phylogenetic Analysis. Phylogenetic estimation has largely come to rely on explicitly model-based methods. This approach requires that a model be chosen, and that the choice be justified. Joyce and Sullivan working with a student from UCLA and Dr. Joyce, graduate student developed a novel approach to model selection, which is based on the Bayesian Information Criterion, but incorporates relative branch-length error as a performance measure in a decision theory (DT) framework. Advantages of the DT method over existing model selection methods include the following:
- This DT method includes a penalty for over fitting, is applicable prior to running extensive analyses, and simultaneously compares all models being considered. Each model is weighted according to the posterior probability of the model conditional on the data. Since any model of evolution is only a crude approximation to reality, rather than focus ones attention on trying to find the ‘correct model’ it is better to have a measure of how plausible a model is given the data.
- The decision theoretic framework allows for much flexibility. One can decide based on biologically relevant criteria, what makes a model useful and use these criteria to give a higher penalty to models that do not meet the criteria than to those that do.
- Our method combines branch length estimates, model fit, and penalty for over fitting in a statistically rigorous way. This method is available in a program called DT-ModSel
IBEST - research - Potato Genetic Map
Jim Lorensen's Potato Genetic Map Database
