Mathematical and Statistical Computing Laboratory
Acting Chief: Benes Trus, Ph.D.
The Mathematical and Statistical Computing Laboratory (MSCL) performs research and provides expert advice in the quantitative sciences to NIH researchers. Research is performed mainly in collaboration with scientists from other institutes at NIH. MSCL personnel can address mathematically and statistically oriented research problems that arise in biomedical practice. Presently, several disciplines are represented in which MSCL members have significant research capabilities.
- Statistical analysis of data from genomics, microbiome or viral quasi-species experiments and development of theoretical models and algorithms for the interpretation of such data. Current projects involve the development of customized pipelines and procedures that perform gene, transcript, and pathway biostatistical analysis on the raw DNA microarray, Next Generation Sequencing (NGS), and/or RNAseq data.
- Mathematical analysis and the development of analytical and modeling tools in neuroscience research. It includes modeling the dynamics of brain, neural, or other complex networks, and studying and reconstructing the neuronal connectivity.
- Application of physics, in particular statistical mechanics, to problems of interest at NIH. Current applications include the studies of random walk, diffusion, chemical reactions, and transport in complex geometries, as well as studies of protein folding, cell membrane transport, and cell-to-cell communication.
- Use of machine learning in biomedical practice, and the development of new learning algorithms and strategies. The applications include automated diagnosis, triaging, prognosis, risk estimation, identifying genetic variants, as well as other classification and reconstruction problems, such as categorization and reconstruction of neuronal architecture.
- General mathematical modeling, analysis, and interpretation of stochastic and nonlinear dynamical processes encountered in biology and medicine, such as dynamics of pathogen populations in hosts.