Charles Lawrence
Professor of Applied Mathematics:
Applied Mathematics
Phone: +1 401 863 1479
Phone 2: +1 401 863 2351
Charles_Lawrence
brown.edu
Ph.D., Cornell University, 1971
Charles (Chip) Lawrence has been involved in computational biology research since the early 1980's. His research now specifically focuses on the application of Bayesian algorithms that he and his collaborators have developed, leading to biological insights on transcription regulation and identification of regulatory motifs in prokaryotic and eukaryotic sequences, comparative genomics, antisense oligonucleotide and siRNA design, the composition of nucleotide sequences, and detailed analyses of several protein families.
Biography
Charles (Chip) Lawrence has been involved in computational biology research since the early-1980s. At a time when research in the field was focused on algorithmic approaches, he was a pioneer in developing novel statistical approaches to biological sequence analysis. In fact, he was one of the first to recognize that the inherent statistical nature of genomic processes and the immense data resulting from genomic sequencing projects could only be fully analyzed by using statistical algorithms.
Interests
Charles (Chip) Lawrence has been involved in computational biology research since the early-1980s. At a time when research in the field was focused on algorithmic approaches, he was a pioneer in developing novel statistical approaches to biological sequence analysis. In fact, he was one of the first to recognize that the inherent statistical nature of genomic processes and the immense data resulting from genomic sequencing projects could only be fully analyzed by using statistical algorithms.
Of particular note are his contributions to the development of sequence alignment algorithms, specifically through the application of Bayesian statistical methods and the adaptation of a Gibbs sampling strategy to this problem. This accomplishment is clearly demonstrated by his seminal Science paper in 1993 describing the first application of the statistical technique Gibbs sampling to the problem of multiple sequence alignment. Also at the forefront is Chip's research with Ye Ding on Bayesian statistical approaches to RNA secondary structure prediction, yielding predictions on the full ensemble of probable structures that an RNA molecule may adopt.
The past several years of statistical algorithm development by Chip and his collaborators have yielded several widely used programs: the Gibbs Motif Sampler, the Bayes aligner, Sfold, BALSA, Gibbs Gaussian Clustering, and Bayesian Motif Clustering.
Chip's research continues to be focused on the application of Bayesian algorithms that he and his collaborators have developed, leading to biological insights on transcription regulation and identification of regulatory motifs in prokaryotic and eukaryotic sequences, comparative genomics, antisense oligonucleotide and siRNA design, the composition of nucleotide sequences, and detailed analyses of several protein families.
In addition to being at the forefront of research in computational biology, Chip has devoted time to education. He developed a tutorial on Bayesian statistics and Gibbs sampling which he presented at ISMB '97 and '98, as well as to several university audiences. Chip has mentored several young investigators, introducing to this interdisciplinary field not only scientists with backgrounds in statistics, but also scientists with backgrounds in computer science and biology.
Awards
Board member of the Scientific Working Group of National Institute of Allergy and Infectious Disease (NIAID-NIH) Bioinformatics Resource Center at The Institute of Genome Research (TIGR)
Mitchell Prize for outstanding applied Bayesian statistics paper in the year 2000
Visiting faculty, Institute of Pure and Applied Mathematics, UCLA 10/00, & 12/00
Rensselaer Alumni Association Fellow
Member American Statistical Association
Member International Society for Computational Biology
Member Sigma Xi
Affiliations
Statistical advisor: NIH NHGRI ENCODE Project Meeting at UC, Santa Cruz
Outside scientific advisory board member: TIGR Bioinformatics
Resource Center Meeting (Scientific Working Group)
Statistical advisor: NIH National Human Genome Research Institute NHGRI) ENCODE Consortium Meeting
Associate Editor, Public Library of Science (PLoS) Computational Biology
Editorial Board, Bioinformatics and Computational Biology
Genomic Sciences Graduate Program Review Team, North Carolina State University
Ad Hoc Study Section Member, LIM-NIH and NHGRI-NIH
Permanent member Genome Research Review Committee (NHGRI-NIH)
Teaching
Foundations in Statistical Inference in Molecular Biology (AM0282)
Funded Research
Current Grants
12/99-11/06 "Detecting Subtle Sequence Signals in Genomic Sequence"
Principal Investigator NIH: R01 HG01257
09/04-09/07 "Development of Bioinformatics and Experimental Technologies for Identification of Prokaryotic Regulatory Networks" Principal Investigator DOE: DEFG0103ER0305
07/03-06/08 "Rational Design Tools for Antisense Nucleic Acids"
Co-PI NIH/NIGMS: RO1 GM068726
10/01-09/06 "Identification and Characterization of Transcription Regulation Networks in Environmentally Significant Species"
Co-PI DOE: DEFG0201ER63204
Completed grants
07/02-06/05 "Statistical Tools for RNA Folding Prediction and Antisense Design for High Throughput Functional Genomics" Co-PI
10/01-12/04 "Bioinformatics Center Extramural Construction Facilities Grant" Scientific Director of the Center NIH: (Wadsworth Research Office) JH Galivan, PIC06 RR14537-01A2
LINKS
Also visit Professor Lawrence's Homepage
Also visit the Center for Computational Molecular Biology
CURRICULUM VITAE
