This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The long-term goal of this project is to detect, predict, and explain the evolutionary processes being explored in projects 1-3. The faculty researchers participating in this project interact with each other and with the researchers working on projects 1-3. We have developed and enhanced theoretical models describing different aspects of evolutionary processes, such as epistatic interactions between genes, the effects of migration and spatial restrictions on evolving populations, and the emergence of microbial consortia in biofilms. We have also developed new, highly efficient, algorithms and statistical models for aligning sequences and inferring phylogenies. In all cases, we have tested our methods on empirical data, usually generated by other COBRE researchers. With these data, we have refined our algorithms and models, either by improving or better quantifying their performance. These results are significant in that they mark progress toward more realistic and predictive models of evolutionary processes, such as acquired antibiotic resistance, competitive exclusion, the spread of virulent microbes, or the transmission of disease related alleles. In addition, two new faculty members have joined this effort. Dr. Frank Gao is a mathematician who has taken a six-month sabbatical with Dr. Larry Forney to learn about biofilm growth and development with the intent to develop a new research program on modeling adaptive evolution in microbial biofilms. Dr. Chris Williams is a statistician who has taken a one-year sabbatical to and Consequently, this paragraph presents an overview of the activities in this project. We have demonstrated that mathematicians, computer scientists, and biologists can productively collaborate to solve problems of significant interest to each of the three disciplines. This has proven true with interactions involving faculty, students, and postdocs.
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