Brook Milligan


Department of Biology
New Mexico State University
Las Cruces, New Mexico 88003
575-646-7980   FAX: 575-646-5665
brook@nmsu.edu


1  Research Interests

2  Computational Biology and Informatics Facility

As Director of the Bioinformatics Core of the New Mexico INBRE Program, I oversee the development of the Computational Biology and Informatics Facility (CBIF). This facility provides local access to a variety of workstations. More importantly, however, CBIF has organized a large collection of scientific software, all of which is made available publicly. Ongoing work focuses on developing the resource materials to aid users in their effective use of this software. Finally, CBIF maintains an array of hardware resources to support scientific analyses. Anyone interested in this facility should visit the CBIF web site or contact me for further information and access.

3  Research Projects

3.1  Demography of Aquilegia

Our lab has been undertaking a long-term study of demography of the plant genus Aquilegia. These plants occur throughout the western United States, but are generally limited to either high elevations or mesic habitats. In the Southwest, their distribution is clearly delimited by surrounding desert vegetation. This provides a series of discrete replicate populations in which to study the effect of degree of isolation and population size on demographic and genetic properties of populations. For example, we are interested in whether small populations really do exhibit characteristics putting them at greater risk of extinction than large populations. In addition we are seeking relationships between environmental drivers, such as drought, and demographic performance of the populations. Elucidating such a relationship enables the analysis of persistence over the long-term based on, for example, dendrochronology records of climate, and under scenarios of potential future climates.

3.2  Molecular sequence alignment

Alignment of molecular sequences remains a core step in virtually all studies of molecular biology, genetics, or evolution. Nevertheless, many methods rely on a very ad hoc set of assumptions. Consequently, it is very difficult to compare different alignments and to provide compelling justification for one over another. We are seeking improved statistical methods to evaluate alignments of molecular sequences, thereby enabling statistical evaluation of alignments, exploration of alternative alignment models, and improved phylogenetic tree reconstruction.

3.3  Host-pathogen interaction

Developing and testing dynamic models of host-pathogen interaction are important for the prediction and understanding of emerging pathogens. Our focus in this area is to understand the prevalence of hantavirus in its rodent hosts in order to develop improved abilities to predict the emergence of new outbreaks. In conjunction with faculty from the Departments of Mathematical Sciences, we are trying to develop a framework that will enable the flexible development and testing of new, biologically realistic dynamics models. Together with data available from field studies in Paraguay, these models will be used to quantify the biological interaction between hantavirus and its hosts, with special attention to how environmental factors such as weather and land use affect the interaction.

3.4  Genetic relatedness

The degree of relatedness among individuals within a population is an important determinant of the pattern of all phenotypic traits, including prevalence of disease susceptibility in humans. Consequently, accurately quantifying genetic relatedness in nature populations is an important undertaking. The current state-of-the-art in this area is largely limited to the consideration of pairs or trios of individuals. However, modern studies often require knowledge of the joint relatedness among many more individuals, something that is fraught with error if based on an aggregation of pairwise relatedness measures. Our lab is developing novel approaches to estimate the joint relatedness among members of larger groups. Our goal is to improve the ability of geneticists to understand the basis of complex traits in natural populations.

Selected References

[1]
Chris Stubben and Brook Milligan. Estimating and analyzing demographic models using the popbio package in R. Journal of Statistical Software, 22(11), September 2007. http://www.jstatsoft.org/v22/i11.

[2]
Brook Milligan. Probability: a C++ library for probabilities and likelihoods. http://biology.nmsu.edu/software/probability/, October 2007. Version 0.4.

[3]
Colleen B. Jonsson, Brook G. Milligan, and Jeffrey B. Arterburn. Potential importance of error catastrophe to the development of antiviral strategies for hantaviruses. Virus Research, 107:195–205, 2005.

[4]
Brook G. Milligan. Maximum likelihood estimation of relatedness. Genetics, 163:1153–1167, 2003.

[5]
E. Pontelli, D. Ranjan, G. Gupta, and Brook Milligan. Design and implementation of a domain specific language for phylogenetic inference. Journal of Bioinformatics and Computational Biology, 1:201–230, 2003.

[6]
E. Pontelli, D. Ranjan, B. Milligan, and G. Gupta. ΦLOG: A domain specific language for solving phylogenetic inference problems. In IEEE Computer Society Bioinformatics Conference, pages 9–20. IEEE Computer Society, Institute of Electrical and Electronics Engineers, 2002.

[7]
Juan Raymundo Iglesias, Gopal Gupta, Enrico Pontelli, Desh Ranjan, and Brook Milligan. Interoperability between bioinformatics tools: A logic programming approach. In I. V. Ramakrishnan, editor, Practical Aspects of Declarative Languages: Third International Symposium, volume 1990 of Lecture Notes in Computer Science, pages 153–168. Springer-Velag, 2001.

[8]
Brook G. Milligan. Estimating long-term mating systems using DNA sequences. Genetics, 142:619–627, 1996.

[9]
Allan E. Strand, Brook G. Milligan, and Casey M. Pruitt. Are populations islands? Analysis of chloroplast DNA variation in Aquilegia. Evolution, 50:1822–1829, 1996.

[10]
Brook G. Milligan, James Leebens-Mack, and Allan E. Strand. Conservation genetics: beyond the maintenance of marker diversity. Molecular Ecology, 3:423–435, 1994.

[11]
Brook G. Milligan. Estimating evolutionary rates for discrete characters. In Robert W. Scotland, Darrell J. Siebert, and David M. Williams, editors, Models in Phylogeny Reconstruction, chapter 16, pages 299–311. Clarendon Press, Oxford, England, 1994.

[12]
Michael Lynch and Brook G. Milligan. Analysis of population genetic structure with RAPD markers. Molecular Ecology, 3:91–99, 1994.

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