

RoseHulman NSFREU
Studies in Computational Biology
 Possible Topics of Study
 Haplotype Inference: Diploid organisms, such as humans, receive
half of their genetic code from each parent. The locations
on the genome in which diversity occurs are called single
nucleotide polymorphisms (SNPs), and the strings of parental
SNPs are called haplotypes. A paired collection of haplotypes
is called a genotype, and the problem of haplotyping is to
find a collection of haplotypes that can combine to create
the known genotypes. Since solutions are nonunique, an
inference rule is used to identify meaningful solutions.
 Flux Balance Analysis (FBA): The metabolic networks of several
single cell organisms is known, and FBA is a computational
model that is used to investigate wholecell metabolisms.
The underlying optimization model linear, although several
nonlinear extensions exists.
 Protein Structure Alignment: Aligning and grouping proteins is
one of the major problems in computational biology. A
new spectral method was introduced in 2010, and we study
this model. The alignment method maps the 3D atomic
coordinates into a high dimensional, smooth contact space
to make alignments.
 Sequence Alignment: Many biological entities are described
with finite sequences, and aligning these sequences to
make relational inferences is important. In particular,
multiple sequence alignment is an important problem across
a wide range of applications. These problems have natural
formulations as integer optimization problems, which
we study.
 Summer 2010
 Pictures:
 Final Papers:
 Past REU Publications:
 Diversity Graphs, 2009, coauthored with P. Blain,
C. Davis, J. Silva, and C. Vinzant, in Clustering
Challenges in Biological Networks, eds. S. Butenko,
W. Chaovalitwongse and P. Pardalos, World Scientific,
pages 129150.
 Asymptotic SignSolvability, Multiple Objective Linear
Programming, and The Nonsubstitution Theorem, 2006,
coauthored with L. Cayton, R. Herring, J. Holzer,
C. Nightingale, and T. Stohs, Mathematical Methods in
Operations Research, vol. 64, num. 3, pages 541555.
 An Extension of the Fundamental Theorem of Linear Programming,
2002, coauthored with A. Brown, A. Gedlaman, and
S. Martinez, Operations Research Letters, vol. 30, num. 5,
pages 281288.

