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.
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 significance 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. More recently his work on probabilistic alignment of marine ocean sediment records for the inference of ages based on paleo (history of) climate marine sediment proxies has emerged as an important tool in the field to assess the significance of lead/lag assertions.
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, and paleo-climate alignment algorithms HMM-Match and ProbStack.
Chip's research continues to be focused on the application of Bayesian algorithms with a focus in genomics and climatology. This work has led to biological insights on transcription regulation and identification of regulatory motifs in prokaryotic and eukaryotic sequences, comparative genomics, antisense oligonucleotide and siRNA design, and detailed analyses of several protein families, RNA structure prediction, identification of the role of repeated sequences in basic biological mechanisms and human genome variation, and inference of lead/lag relationships in paleo-climate studies.
In addition to being at the forefront of research in computational biology and paleo-climatology, Chip has devoted time to education. 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, biology, and geology.
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)
Fellow of the American Statistical Association
Mitchell Prize for outstanding applied Bayesian statistics paper in the year 2000
Centroid estimators for inference in high-dimensional discrete spaces (2008), Luis E. Carvalho, and Charles E. Lawrence, PNAS: USA, 105: 3209–3214. Reported as a must read paper in the Faculty of 1000.
Exact Calculation of Distributions on Integers, with Application to Sequence Alignment, Newberg and Lawrence, J. Computational Biology (January, 2009), selected as a highlighted article.
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