This page lists
complete biographies of BiC members.
Professor
Michael Zuker 
back to Prof. Zuker's main biography page
Current
Position: Professor of Mathematical Sciences, Rensselaer
Polytechnic Institute.
Research
interests: The central theme in Prof. Zuker's research
has been the development of algorithms to predict RNA and
DNA secondary structure by free energy minimization using
empirically derived thermodynamic parameters. Modeling and
algorithm development have been closely coupled with the derivation
of "nearest neighbor" and other energy rules in
the laboratories of D.H. Turner, (RNA parameters, Department
of Chemistry, University of Rochester, Rochester, NY) and
of J. SantaLucia (DNA parameters, Department of Chemistry,
Wayne State University, Detroit, MI). |
|
 
Prof. Zuker
Prof. Lawrence
Dr.
Mannella
Prof. Bennett
Prof. Breneman
Prof.
Bystroff
Dr. Ding
Prof. Embrechts
Prof. Garde
Dr. Lee Ann McCue
Prof. Newberg
Prof. Salerno
Prof. Wentland
Prof. Zaki
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The algorithms allow the use of several types of constraints that
can incorporate experimental information on the existence or non-existence
of specific base pairs.
Applications: 1. Predictions of RNA secondary structures, with or
without constraints, has been a very useful tool for producing a reasonably
small number of secondary structures that can be experimentally tested
for validity. 2. Secondary structure predictions for single RNA viruses
have been useful in predicting or identifying functionally important
regions. 3. RNA folding software has been useful in designing anti-sense
nucleic acids or even ribozymes that can be used to target a mRNA.
4. DNA secondary structure predictions have proved very useful in
the design of probes, PCR primers and molecular beacons.
More recently, Prof. Zuker's group has derived RNA nearest neighbor
rules by analyzing base pair stacking frequencies in a large database
of known secondary structures. Current work is focused on the computation
of partition functions for systems containing two, usually different,
molecules that can fold as well as hybridize with each other. This
work allows one to predict optical density and heat capacity melting
curves, and is a profound improvement over the folding of a single
sequence. Prof. Zuker's group is also developing a fast search algorithm
based on hashing to find likely targets of a DNA "query"
sequence in genomic DNA. Unlike the BLASTS software, scoring is based
on nearest neighbor free energy rules, and G·T base pairs are
considered as exact "matches" along with Watson-Crick base
pairs.
Future research: 1. The development of algorithms for the
interactions of more than one molecule of DNA or RNA will remain
a high priority. 2. Dr Zuker is interested in developing methods
to find non-coding RNA (ncRNA) in large tracts of DNA. In addition,
the computation of common secondary structures for homologous RNAs
remains a challenge.
For more information and links related to Prof. Zuker's research,
visit his Nucleic
Acids Folding page.
Education: Professor Zuker received his Ph.D. in Mathematics
from Massachusetts Institute of Technology in 1974.
Current Position: Chief of the Biometrics/Bioinformatics
Laboratory, Wadsworth Center.
Research interests: Statistical
bioinformatics is Prof. Lawrence's laboratory area of expertise.
His research focuses on studies of transcription regulation using
Bayesian statistical methods to make inferences from multiple genome
sequences. The long-term goal of this research is to elucidate the
complete wiring diagrams of core transcription regulatory networks.
Work on this topic is funded by grants from DOE for studies in prokaryotes,
and by NIH for studies in humans and other vertebrates. Current
aims of the prokaryotic research are two-fold. 1) Extension of a
recent work on the prediction of cis-regulatory elements in E. coli
to several other pathogenic species and to species of environmental
interest. Additionally, Prof. Lawrence's team is seeking to develop
and apply novel Bayesian methods to predict sets of co-regulated
genes (regulons), and to develop more sensitive methods to scan
genomes in order to identify additional genes that belong to a regulon.
2) Work in collaboration with chemical engineers to develop high-throughput
technologies to validate these predictions and to identify cognate
transcription factors. Current work of Prof. Lawrence's laboratory
on human transcription regulation has three foci. 1) Development
of improved Markov chain Monte Carlo (MCMC) algorithms and Bayesian
models for the de novo prediction of cis-regulatory modules and
specific response elements using aligned human and mouse genome
sequences. 2) Development of rapid sequence scanning technologies
to predict additional genes CO-regulated by such cis-regulatory
modules. 3) Understanding the potential of multiple vertebrate sequences
for inference of cis-regulatory modules and response elements is
the basis of his laboratory's involvement in the NIH "zoo project".
Additional projects in the Prof. Lawrence's laboratory involve the
prediction of ensembles of RNA secondary structures, and the prediction
of the structure and function of proteins.
Education: Prof. Lawrence received his Ph.D. in Applied
Operations Research & Statistics from Cornell University in
1971.
Current Position:
Executive Director of the joint Wadsworth/RPI Center for Bioinformatics.
Dr. Mannella is also Director of Wadsworth's Division of Molecular
Medicine, and a professor and past chair of the Department of Biomedical
Sciences of the School of Public Health, University at Albany.
Research interests: His main research
interest is understanding how shape, particularly membrane topology,
influences biological function. The primary focus of this research
is the mitochondrion, the organelle that generates the ATP needed
to power the cell's molecular machinery.
Determination of the internal 3D structure of mitochondria involves
the relatively new technique of electron tomography, being developed
at the Wadsworth Center's NIH-funded national resource for the visualization
of biological complexity. The insights provided by electron tomography
and computer modeling are leading to a new appreciation of the complexity
and dynamics of mitochondrial architecture and how it can influence
energy production. Other research interests have included
the application of Bayesian statistics to predict membrane protein
structure from sequence data, and advanced approaches for image classification
and averaging.
Involvement in teaching and
education: While at Wadsworth, Dr. Mannella has been involved
in several educational and outreach programs. In particular, he
established in 1991 a popular 10-week summer training program for
undergraduates, with funding from the National Science Foundation.
He has been on the faculty of the Department of Biomedical Sciences
(School of Public Health, SUNY-Albany) since 1986 and has organized
and taught in graduate-level courses in Biophysics, Biochemistry,
Electron Microscopy, and Proper Conduct of Science.
Education: Dr. Mannella received a doctorate in Biophysics
at the University of Pennsylvania and was a National Cancer Institute
postdoctoral fellow at Roswell Park Cancer Institute.
Professor
Kristin P. Bennett
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Current Position:
Associate Professor, Department of Mathematical Sciences, Rensselaer
Polytechnic Institute.
Research
interests: The research of Professor Bennett centers on combining
operations research and artificial intelligence problem solving
methods. She uses mathematical programming approaches to problems
in artificial intelligence such as machine learning, neural networks,
pattern recognition, and planning. She applies these techniques
to medical, financial and scientific problems. While adapting algorithms
for parallel machines, she uses mathematical programming approaches
to other areas in computer sciences such as genetic algorithms and
database query optimization.
Education:
Professor Bennett received her Ph.D. from the University of
Wisconsin—Madison in 1993 .
Professor Curt M. Breneman
back to Prof. Breneman's main biography page
Current Position:
Professor of Chemistry, RPI
Research
interests: 1) The Automated Design and Discovery of Novel Pharmaceuticals
using Semi-Supervised Learning in Large Molecular Databases
2) Ab Initio Computational Chemistry
3) Rapid Construction of Molecular Electron Density Distributions:
Transferable Atom Equivalent (TAE) Modeling
4) Electron Density-Based QSAR and QSPR Descriptor Computation
5) Automated Drug Discovery Methods and "Materials by Design"
6) Molecular Recognition
7) Fuzzy Bar Code representations of DNA-protein interactions. View
abstract (opens new window).
Education:
Prof. Breneman received his Ph.D. in organic chemistry from the
University of California at Santa Barbara in 1987.
Professor Chris Bystroff
back to Prof. Bystroff's main biography page
Current Position: Assistant Professor of Biology, RPI
Research interests: Prof.
Bystroff's research addresses the protein folding problem, specifically
the nature of the folding pathway. Algorithms for protein structure
prediction are seen as models for the physical folding process.
The database of known protein structures serves as a source for
knowledge-based energy functions that describe the energetics of
residue-residue interactions without an all-atom model or explicitly
modeled solvent. By looking at recurrent sequence and structure
patterns in known proteins we hope to extend the limits of homology
detection and perhaps predict structure ab initio, without the use
of structural templates.
Future research: Understanding folding pathways may lead
to an understanding of why some proteins misfold to form amyloid
and others require molecular chapaerones for folding. Late folding
intermediate states might identify highly antigenic sites on the
protein surface. Alignments of three-dimensional structures without
regard to permutations in the sequence order can be used to develop
a new type of hidden Markov model, which may be able to detect very
remote homologous relationships between sequences.
Involvement in teaching and student education: Prof. Bystroff
teaches courses in Sequence Analysis, Molecular Modeling, and X-ray
Crystallography
Education: Prof. Bystroff received his B.A. at Carleton
College in 1983, and Ph.D. at University of California at San Diego
in 1988.
Dr. Ye Ding
back to Dr. Ding's main biography page
Current Position: Research Scientists at Bioinformatics
lab of the Wadsworth Center.
Research interests: Dr. Ding's
current research focuses on novel statistical algorithms for RNA
secondary structure prediction, and target accessibility prediction
and the rational design of RNA-targeting nucleic acids. In the post-genomic
era, RNA-targeting nucleic acids, in the form of RNA-targeting oligonucleotides,
trans-cleaving ribozymes and short interfering RNAs (siRNAs),
are becoming increasingly important for high throughput functional
genomics and drug target validation. Dr. Ding has been collaborating
with molecular biologists at Wadsworth Center to validate and improve
novel design methodology in both in vitro and in vivo
systems. Dr. Ding's work is currently funded by the National Science
Foundation to develop a software named Sfold, for statistical RNA
folding and rational design of RNA-targeting nucleic acids, and
to establish and maintain a Web server for on-line applications
by the scientific community. Sfold 1.0 is now available through
Web servers at mirror sites http:/sfold.wadsworth.org
and www.bioinfo.rpi.edu/applications/sfold.
Dr. Ding's research on RNA structure prediction and applications
will also be supported by the National Institutes of Health (NIH).
(see the links on Sfold Web server home page for information on
both NSF and NIH grants)
Future research: Dr. Ding's future research directions include:
1) prediction of target sites for microRNAs (natural regulatory
antisense RNAs); 2) application of RNA-targeting nucleic acids design
to infectious pathogens; 3) statistical algorithms for RNA pseudoknot
prediction; 4) statistical prediction of common structures of homologous
RNA sequences; 5) development of statistical models for the prediction
of small regulatory RNAs in eukaryotic genomes; 6) rational design
tools for molecular beacons and developement of a
module Sprobe for Sfold.
Education: Dr. Ye Ding received his Ph.D. in Statistics
from Carnegie Mellon University in 1990.
Professor
Mark Embrechts
back to Prof. Embrechts' main biography page
Current Position:
Assistant Professor of Chemistry, RPI
Research
interests: Prof. Embrecht's areas of interest relate to data
mining, soft computing, computational intelligence, and neural networks.
He is a member of IEEE, ANS and is past Chapter President of the
American Nuclear Society
Education:
Professor Embrechts received his Ph.D. from Virginia Polytechnic
Institute in 1981
Professor Shekhar Garde
back to Prof. Garde's main biography page
Current Position: Assistant Professor of Chemistry, RPI
Research interests: molecular thermodynamics and simulations
of biological systems, statistical mechanics of liquids and polymers,
and solvation phenomena -- especially in aqueous solutions (water
structure, hydrophobic interactions). Professor Grarde focuses on
understanding and modeling the role of water structure in inducing
interactions between various hydrophobic, polar, and ionic molecules
which ultimately leads to many important self-assembly processes
in water.
Education: Professor Garde received his Ph.D. from
Current Position:
Research Scientist, Bioinformatics Lab, Wadsworth Center.
Research
interests: The research of Dr. McCue focuses on transcription
regulation in prokaryotes, using bioinformatics and comparative
genomics approaches to study regulatory networks at the whole-genome
scale. Her research involves identifying the cis-acting regulatory
signals in prokaryotic promoters and working to understand how trans-acting
transcription factors specifically recognize and interact with their
cognate sites. Current projects include: 1) the de novo prediction
of cis-regulatory sites on the genome scale for Escherichia coli
K12, Synechocystis PCC6803, Shewanella oneidensis, Yersinia pestis,
and Pseudomonas aeruginosa by comparative genomics; 3) the improvement
of methods for scanning genomes for additional regulatory sites,
thereby further expanding known or predicted regulons; 4) the analysis
of co-expression data from microarray or reporter fusion studies
to identify regulatory motifs in Mycobacterium tuberculosis, Deinococcus
radiodurans, Shewanella oneidensis, and Pseudomonas aeruginosa;
5) the development of biochemical and genetic methods for large-scale
validation of predictions and identification of cognate transcription
factors. The work of her group is currently funded by a grant from
DOE.
Future work
will focus on two broad areas: 1) Continued work with the model
organism E. coli to build in additional higher-order features of
gene expression. 2) Continued work with other bacterial species
will focus on improving methods to identify regulatory interactions
that are key to pathogen survival in the host and metabolic pathways
important for bioremediation.
Education:
Dr. McCue received her Ph.D. in Microbiology from The Ohio State
University in 1994.
Professor Lee Newberg
back to Dr. Newberg's main biography page
Current Position:
Joint appointment as a Research Scientist in the Bioinformatics
Laboratory, Wadsworth Center and as a Research Associate Professor
in the Rensselaer Computer Science Department.
Research
interests: Algorithmic, statistical, and mathematical combinatorics
approaches to computational molecular biology. In particular, using
multiple species and phylogenetic relationships, to enhance algorithms
for locating transcription factor binding sites; relevant to the
regulation of gene expression.
Short
biography: Prof. Lee Newberg worked in computational biology
as an undergraduate with Eric Lander (Whitehead Institute, MIT)
and as a graduate student with Richard Karp (University of California,
Berkeley) producing several useful algorithms and mathematical results
suitable for publication. He then left the academic track to follow
his wife's academic track as an astrophysicist. His first position
after graduate school as a software engineer and ultimately acting
director of The University of Chicago Biological Sciences Division
Office of Academic Computing gave him the opportunity to use his
computational and biological skills to create course software for
the teaching of molecular biology to undergraduates. His next position
as a Quantitative Researcher in charge of the quantitative aspects
of the Citadel Investment Group Global Equity Arbitrage trading
desk offered him little ability to do bioinformatics but much opportunity
to hone his algorithmic, mathematical, and statistical skills. He
has recently returned to academia to pursue his interest in bioinformatics
and computational biology.
Education: Prof. Lee Newberg received his B.S. degrees in
Mathematics and Physics from MIT in 1986 and his Ph.D. degree in
Computer Science from The University of California at Berkeley in
1993.
Professor
John Salerno
back to Prof.Salerno's main biography page
Current Position:Professor
of Biology, Rensselaer Polytechnic Institute.
Research
interests: Structure, Function and Control of Nitric Oxide
Synthases; P450 superfamily enzymes Structure and Function in small
heat shock protein superfamily Bioinformatics, structural modeling,
protein design and directed evolution.
Courses:
Bioinformatics II: Molecular Modeling (Spring 2003)
Education:
Professor Salerno received his Bachelor degree in Physics from the
Massachusetts Institute of Technology in 1972 and his Ph.D. in Biophysics
from the University of Pennsylvania, School of Medicine in 1977.
Professor Mark Wentland
back to Prof. Wentland's main biography page
Current Position: Professor of Chemistry, Rensselaer Polytechnic
Institute.
Short Biography: Mark Wentland began his 33 year career in drug
design and discovery in 1970 when he joined the medicinal chemistry
department at Sterling Winthrop Inc. Prior to this he was at Rice
University where he earned his Ph.D. in synthetic organic chemistry
under the direction of the late Professor Robert V. Stevens. During
his 24 years at Sterling Winthrop, he held various positions of
scientific and administrative responsibility with his last positions
being Sterling Winthrop Fellow and Oncology Discovery Co-Chair.
In 1994, he joined the chemistry faculty at Rensselaer Polytechnic
Institute in Troy, NY. During the period 1971-1994, he was Adjunct
Professor of Chemistry at Rensselaer and taught over 30 graduate-level
organic and medicinal chemistry courses. At Rensselaer, he maintains
a federally-funded research program aimed at the design and synthesis
of novel, long-acting oral agents to treat cocaine and heroin addiction
in humans. In addition to himself, the research group has on the
average, 6 members (3 postdocs, 2 graduate students, and 1 undergraduate).
Courses: Professor Wentland also has developed two new
courses, Drug
Discovery (CHEM-4330, 6330) and Medicinal Chemistry (CHEM-4300,
6300) where students learn and practice applications of bioinformatics
and genomics to drug design and discovery. He regularly presents
workshops on these same topics at pharmaceutical and biotech companies.
During his career, he has led efforts resulting in the discovery
of nine drug candidates, six of which have been advanced to clinical
trials. His homepage can be found here.
Research interests: In collaboration with Dr. Jean M. Bidlack
and coworkers at the University of Rochester and with funding from
the National Institute on Drug Abuse, the main goal of Professor
Wentland's research is to design and synthesize potential medications
to treat cocaine and heroin abuse in humans. His group's working
hypothesis is that agents possessing the opioid receptor profile
of kappa agonist/mu antagonist have the potential for treating addiction
via modulation of tonal dopamine levels in the nucleus accumbens
(the pleasure seeking area) of the human brain. The lead structures
for these studies are cyclazocine and ethylketocyclazocine (EKC);
cyclazocine is currently being evaluated in clinical trials by NIDA
for remediation of cocaine addiction. Cyclazocine, however, is short
acting in humans and animals via O-glucuronidation. In hopes of
identifying novel isosteric replacements for the phenolic OH of
cyclazocine, Professor Wentland and coworkers recently reported
the synthesis and biological properties of 8-CAC. This cyclazocine
analogue has a carboxamide group in place of the phenolic OH and
displays very high affinity for opioid receptors and has 15 hour
duration of action in a mouse antinociception model; for comparison,
cyclazocine's duration of action is 2 hours. Currently, the major
focus of Professor Wentland research group's design efforts are
centered about exploring new structure-activity relationships that
have emerged from the discovery of 8-CAC.
Education: Professor Wentland received his Ph.D. degree
in chemistry from Rice University, Houston, TX, in 1970.
Professor Mohammed J. Zaki
back to Prof. Zaki's main biography page
Current Position: Assistant Professor of Computer Science,
Rensselaer Polytechnic Institute.
Research interests: the design
of efficient, scalable, and parallel algorithms for various data
mining techniques. Professor Zaki is specially interested in developing
novel data mining techniques for bioinformatics.
Contact Map Mining for Protein Structure Prediction: Given a protein
amino acid sequence (linear structure), determining its three dimensional
folded shape (tertiary structure) is referred to as the Structure
Prediction Problem, one of the grand challenges in Bioinformatics.
Instead of traditional simulation studies, I am learning to predict
the structure from known protein structures (e.g., Protein Data
Bank), using Protein Contact Maps (two dimensional representations
of the three dimensional structure of proteins). My work is targeting
two main problems: 1) Given a database of protein sequences and
their 3D structure in the form of contact maps, build a model to
predict if pairs of amino acids are likely to be in contact or not.
2) Discover common (non-local) contact patterns or ``features''
that characterize physical ``protein-like'' contact maps.
Future Research: to address
other important problems in bioinformatics, namely, multiple sequence
alignment via sequence mining techniques, gene mapping and finding,
discovering regulatory networks, and so on. Given that the biological
data being collected is growing at an exponential rate, our high
performance data mining system (under development) will support
the entire mining process for protein folding and other bioinformatics
problems.
For more information and links related to Prof. Zaki's research,
visit his Data Mining page.
Education: Professor Zaki received his Ph.D. degree in computer
science from the University of Rochester in 1998.
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