Department of Biology
New Mexico State University
Las Cruces, New Mexico 88003
575-646-7980 FAX: 575-646-5665
1 Research Interests
- Conservation biology
- Molecular sequence evolution
- Population demography
- Statistical modeling
- Scientific software engineering
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
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.
Chris Stubben and Brook Milligan.
Estimating and analyzing demographic models using the popbio package
Journal of Statistical Software, 22(11), September 2007.
Probability: a C++ library for probabilities and likelihoods.
http://biology.nmsu.edu/software/probability/, October 2007.
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.
Brook G. Milligan.
Maximum likelihood estimation of relatedness.
Genetics, 163:1153–1167, 2003.
E. Pontelli, D. Ranjan, G. Gupta, and Brook Milligan.
Design and implementation of a domain specific language for
Journal of Bioinformatics and Computational Biology,
E. Pontelli, D. Ranjan, B. Milligan, and G. Gupta.
ΦLOG: A domain specific language for solving phylogenetic
In IEEE Computer Society Bioinformatics Conference, pages
9–20. IEEE Computer Society, Institute of Electrical and Electronics
Juan Raymundo Iglesias, Gopal Gupta, Enrico Pontelli, Desh Ranjan, and Brook
Interoperability between bioinformatics tools: A logic programming
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.
Brook G. Milligan.
Estimating long-term mating systems using DNA sequences.
Genetics, 142:619–627, 1996.
Allan E. Strand, Brook G. Milligan, and Casey M. Pruitt.
Are populations islands? Analysis of chloroplast DNA variation in
Evolution, 50:1822–1829, 1996.
Brook G. Milligan, James Leebens-Mack, and Allan E. Strand.
Conservation genetics: beyond the maintenance of marker diversity.
Molecular Ecology, 3:423–435, 1994.
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.
Michael Lynch and Brook G. Milligan.
Analysis of population genetic structure with RAPD markers.
Molecular Ecology, 3:91–99, 1994.
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