CCMB Distinguished Lectures Series 2007-2008
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CCMB Distinguished Lecture Series |
Jim Collins
Center for BioDynamics
and
Department of Biomedical Engineering
Boston University
Engineering Gene Networks:
Integrating Synthetic Biology & Systems Biology
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Abstract:
Many fundamental cellular processes are governed by genetic programs which employ
protein-DNA interactions in regulating function. Owing to recent technological advances,
it is now possible to design synthetic gene regulatory networks, and the stage is
set for the notion of engineered cellular control at the DNA level.
Theoretically, the biochemistry of the feedback loops associated with protein-DNA
interactions often leads to nonlinear equations, and the tools of nonlinear analysis
become invaluable. In this talk, we describe how techniques from nonlinear dynamics
and molecular biology can be utilized to model, design and construct synthetic gene
regulatory networks. We present examples in which we integrate the development of
a theoretical model with the construction of an experimental system. We also discuss
the implications of synthetic gene networks for biotechnology, biomedicine and biocomputing.
In addition, we present integrated computational-experimental approaches that enable
construction of first-order quantitative models of gene-protein regulatory networks
using only steady-state expression measurements and no prior information on the
network structure or function. We discuss how the reverse-engineered network models,
coupled to experiments, can be used: (1) to gain insight into the regulatory role
of individual genes and proteins in the network, (2) to identify the pathways and
gene products targeted by pharmaceutical compounds, and (3) to identify the genetic
mediators of different diseases.
Wednesday, November 7th, 2007
4:00pm
CIT Building, Room 227
CCMB Distinguished Lecture Series |
James Yorke, Ph.D.
Distinguished University Professor of Mathematics and Physics
Institute for Physical Sciences and Technology (IPST)
University of Maryland
Determining the DNA sequence, a billion dollar logic puzzle |
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Abstract:
The genome of an individual is the collection of DNA in each of his/her/its cells.
It can be expressed as one or more sequences of the letters A, C, G, T. For mammals
the genome has about 3 billion letters while for a bacteria it has a couple million.
The dominant method used for determining the sequence is called whole genome shotgun
assembly. Using this method, The National Institutes of Health has spent about one
billion dollars determining genomes of many species in the past five years. Parts
of genome turn out to be easier to determine than other parts but overall each genome
becomes a giant jigsaw puzzle. At the University of Maryland, we try to find techniques
for solving as much of the puzzle as possible. The most difficult parts of puzzle
to assemble are often the parts that have been mutating the most in the recent millions
of years. We are also trying to determine the patterns of repeats.
Monday, October 15th, 2007
4:00 pm, CIT Building, Room 241 ~ SWIG Boardroom
Hosted by: Suzanne Sindi
CCMB Distinguished Lecture Series |
Nancy Amato, Ph.D
Parasol Lab, Department of Computer Science
Texas A&M University
Using Motion Planning to Study Molecular Motions
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Abstract:
Protein motions, ranging from molecular flexibility to large-scale conformational
change, play an essential role in many biochemical processes. For example, some
devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad
Cow) are associated with the misfolding of proteins. Despite the explosion in our
knowledge of structural and functional data, our understanding of protein movement
is still very limited because it is difficult to measure experimentally and computationally
expensive to simulate.
In this talk we describe a method we have developed for modeling protein motions
that is based on probabilistic roadmap methods (PRM) for motion planning. Our technique
yields an approximate map of a protein's potential energy landscape and can be used
to generate transitional motions of a protein to the native state from unstructured
conformations or between specified conformations. We describe a method based on
rigidity theory that allows us to sample conformation space more efficiently than
our initial sampling strategy and enables us to study a broader range of motions
for larger proteins and new analysis tools that enable us to extract kinetics information,
such as folding rates. For example, we show that rigidity-based sampling results
in maps that capture subtle folding differences between protein G and its mutations,
NuG1 and NuG2, and we illustrate how our technique can be used to study large-scale
conformational changes in calmodulin, a 148 residue signaling protein known to undergo
conformational changes when binding. More information regarding our work, including
an archive of protein motions generated with our technique, are available from our
protein folding server:
http://parasol.tamu.edu/foldingserver/.
Wednesday, October 10th, 2007
4:00pm, CIT Bldg, Room 241 ~ SWIG Boardroom
Hosted by: Franco P. Preparata
Refreshments will be served at 3:45pm
CCMB
Distinguished Lecture Series |
Stephen
Altschul
National Center for Biotechnology Information
National Library of Medicine
National Institutes of Health
"Protein Sequence
Database Searches Using Compositionally Adjusted
Amino Acid Substitution Matrices" |
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Abstract: Standard
amino acid substitution matrices are constructed
as log-odds ratios from large collections of alignments
of related proteins. Any such collection
has an implicit "standard" set of amino
acid background frequencies. The matrices produced,
however, often are used to compare proteins with
quite non-standard amino acid compositions. We
argue on theoretical grounds that this is inappropriate,
and have described a method for transforming a
standard matrix into one appropriate for comparing
proteins with any non-standard compositions. Compositionally-adjusted
matrices yield improved results from the twin perspectives
of alignment score and alignment quality when proteins
with strongly biased compositions are compared.
To what extent are such adjusted matrices of utility
for general purpose protein database searches? Using
standard test platforms, we compared a standard matrix
to compositionally-adjusted matrices, with relative
entropy left unconstrained, or constrained in various
ways. We found that constraining the relative
entropy of the compositionally adjusted matrix to
a fixed value in the new compositional context generally
produced the best results. We also found that if
the sequences compared are not known to have strong
compositional biases, then it is still on average
advantageous to use an adjusted matrix when the sequences
satisfy certain simple length or compositional inequalities.
Applying these findings to general-purpose database
searches can lead to a significant improvement in
retrieval performance, with a minimal increase in
execution time.
Wednesday,
April 9th, 2008
4:00 pm
CIT Building, Room 241 – SWIG Boardroom
Hosted by: Charles E. Lawrence
CCMB
Distinguished Lecture Series |
Jun
Liu
Department of Statistics
Harvard University
"Inference
of Patterns and Associations Using Dictionary
Models" |
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Abstract: Pattern
discovery is a ubiquitous problem in many disciplines.
It is especially prominent in recent years due
to our greatly improved data-generation capabilities
in science and technologies. The method I present
here is motivated by the "motif-finding" and "module-finding" problems
in biology, i.e., to find sequence patterns (i.e., "words")
that seem to appear more frequent than usual in
a given set of text sequences (i.e., sentences)
and to find which of these "words" tend
to co-occur in a sentence. A challenge in the motif-finding
problem is that there are no spacings and punctuations
between the words and the dictionary of "words" is
unknown to us. Existing methods are mostly "bottom-up" approaches,
i.e., to build up the dictionary starting with
single-letter words and then concatenate some existing
words that appear to occur next to each other in
sentences more frequently than chance. Our new
approach is a top-down strategy, which uses a tree
structure to represent the relationship among all
possible existing words and uses the EM algorithm
to estimate the usage frequency of each word. It
automatically trims down most of the incorrect "words" by
letting their usage frequencies converge to zero.
The module-finding
problem is closely related to the well-known "market
basket" problem, in which one attempts to
mine association rules among the items in a supermarket
based on customers' transaction records. It
is also related to the two-way clustering problem.
In this problem, we assume that the words are given,
and our goal is to find subsets of words that tend
to co-occur in a sentence.
We call the set of
co-occurring words (not necessarily orderly) a "theme" or
a "module". We can generalize the dictionary
model to the "theme"-model and use a
similar EM-strategy to infer these themes. I will
demonstrate its applications in a few examples
including an analysis of chinese medicine prescriptions
and an analysis of a chinese novel.
This is based on a joint work with Ke Deng and Zhi
Geng.
Wednesday,
April 23rd 2008
4:00 pm
CIT Building, Room 241 – SWIG Boardroom
Hosted by: Charles E. Lawrence
Refreshments will be served at 3:45 pm
CCMB
Distinguished Lecture Series |
Joe
W. Gray
Staff Scientist/Division Director
Lawrence Berkeley National Laboratory UCSF
Comprehensive Cancer Center
"A
Systems Approach to Marker Guided
Therapy in Breast Cancer" |
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Joe W. Gray,
Ph.D., is Associate Laboratory Director
for Life and Environmental Sciences and Life
Sciences Division Director at the Lawrence Berkeley
National Laboratory (LBNL). He is also
Adjunct Professor of Laboratory Medicine at the
University of California, San Francisco (UCSF)
and program leader for the Breast Oncology Program
in the UCSF Comprehensive Cancer Center. Dr.
Gray's current research program focuses on in
molecular analysis technology, identification
of genomic aberrations that contribute to cancer
pathophysiology, development of efficient strategies
for enhanced marker guided cancer therapy –especially
for breast and ovarian cancer and early breast
cancer detection. His work is described
in more than 330 publications and 50 patents. Major
awards include the Radiation Research Society
Research Award (1985), the E.O. Lawrence Award
from the US Department of Energy (1986), Election
as a Fellow of the American Association for the
Advancement of Science (1996), the Curt Stern
Award from the American Society for Human Genetics
(2001), an Honorary Doctorate from the University
of Tampere, Tampere, Finland (2005) a DOD Innovator
Award (2007) and the Brinker Award (2007). Dr.
Gray earned a Ph.D. in physics from Kansas State
University.
Wednesday,
April 30th, 2008
Sidney Frank Hall, Room 220
Hosted by: Ben Raphael and John Sedivy
CCMB
Distinguished Lecture Series |
Gad
Kimmel
University of California, Berkeley
Faculty Candidate
Center for Computational Molecular
Biology
"Computational
Problems in Human Genetics" |
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Abstract: The
question how genetic variation and personal health
are linked is one of the compelling puzzles facing
scientists today. The ultimate goal is to
exploit human variability to find genetic causes
for multi-factorial diseases such as cancer and
coronary heart disease. Recent technology improvement
enables the typing of millions of single nucleotide
polymorphisms (SNPs) for a large number of individuals. Consequently,
there is a great need for efficient and accurate
computational tools for rigorous and powerful analysis
of these data. In my talk I am going to concentrate
on two computational problems, which are an essential
step in studying the data obtained by this technology:
Accurate and efficient significance testing with
a correction for population stratification and
estimating local ancestries in admixed populations.
Wednesday,
May 14th, 2008
4:00 pm
182 George Street, Applied Math Building – Room
#110
Hosted by: Charles E. Lawrence
CCMB
Distinguished Lecture Series |
Fumei
Lam
Brown University
"Imperfect
Ancestral Recombination Graph Reconstruction
Problem: A Hierarchy of Upper Bounds" |
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Abstract: Reconstruction
of evolutionary histories is a fundamental problem
in computational biology. It has been established
that accurately representing complete evolutionary
histories requires an underlying model that incorporates
non-tree operations, corresponding to the mixing
of genetic material from ancestral sequences. In
this talk, we will address the problem of finding
parsimonious evolutionary histories with both hybridization
and mutation events (the Imperfect Ancestral Recombination
Graph Reconstruction Problem). The power
of our framework is the connection between our
formulation and the Directed Steiner Arborescence
Problem in combinatorial optimization. We
implement linear programming techniques as well
as heuristics for the Directed Steiner Arborescence
Problem and apply these algorithms on simulated
and benchmark data sets.
This is joint work with Ryan Tarpine and Sorin Istrail.
Wednesday,
May 21, 2008
4:00 pm
CIT Building, Room 241, SWIG Boardroom
Hosted by: Sorin Istrail
Refreshments will
be served at 3:45 p.m.
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