Computational Biology

The Computational Biology curriculum is designed to help students learn how to leverage mathematical and computational approaches to understand biological and chemical processes.

Degree Plan

Research Topics

Mathematical and computational concepts, methods, and algorithms are being applied to all areas of basic and clinical life sciences, which results in a variety of research topics. Examples include:

Biophysics and Structural Biology – Protein structure and function prediction; analysis of biological sequences and 3D structures; macromolecular interactions and biological networks; molecular evolution
Chemical Biology – Analysis of small organic molecules; design of materials and drugs; chemical dynamics
Computational Neuroscience - Modeling neural circuits to study the neural basis of cognition in normal states and disease; developing novel statistical methods to analyze large-scale neural data
Genetics and Genomics – Analysis of DNA/RNA sequences; genome association to cellular and organismal function; statistical genetics
Imaging – Computer vision and pattern recognition applied to medical imaging and microscopy data, statistical and mathematical modeling of the spatiotemporal organization of image events from molecular to macroscopic scales
Medical Informatics – Machine learning of patterns in multivariate clinical databases, predictive modeling of clinical outcomes by variable association
Systems Biology – Computational reconstruction and analysis of biological networks; modeling of complex, nonlinear systems; spatiotemporal integration of chemical and mechanical processes across scales

Director

Research Interests: Statistical analysis and rational design of cellular systems

Technical Expertise: Principal components analysis and matrix factorization, statistical analysis of protein sequences, python for large scale data analysis

Faculty

Research Interests: Genomic, genetic, and molecular approaches to autism spectrum disorders

Technical Expertise: Genetic linkage and mapping, whole exome and genome sequence analysis

Research Interests: Computer vision; computational cell biology; regulation of cell morphogenesis; roles of cell morphogenesis in cancer

Technical Expertise: Computer vision; time-series analysis; model regression; continuum mechanics; software engineering

Research Interests: Autonomous Microscopy, Molecular Multiplexing, Optical Probe Development, and Content Rich Histopathology

Research Interests: Statistical methods, clinical trials, epidemiology, health economics

Research Interests: Single-cell regulatory genomics, cell fate engineering, function of non-coding elements and genetic variants.

Technical Expertise: Single-cell genomics, genome engineering, gene regulation, cell state engineering

Research Interests:Large language models, AI, spatial biology, image analysis, machine learning

Research Interests: Quantitative spatiotemporal structure-function relationships of multi-molecular assemblies; linking molecular and cellular behavior across multiple scales; computational image analysis and mathematical modeling

Technical Expertise: Computer vision, stochastic modeling and model calibration, time series analysis

Research Interests: Molecular recognition in protein folding, chaperone structural biology, and neurodegeneration

Technical Expertise: Biochemistry, biophysics, structural biology, chemical biology and protein modeling

Research Interests: Development of novel computational algorithms/models/pipelines for big, heterogeneous biological and medical data

Research Interests: Optimization in conformational and network space.

Technical Expertise: Statistical mechanics, protein folding, non-equilibrium physics

Research Interests: Mechanisms of protein folding, misfolding, and aggregation; Studying amyloid assembly and their heterotypic interactions with other cellular components, with a particular focus on neurodegenerative diseases, such as Alzheimer's and Parkinson's disease; Development of anti-amyloid inhibitors as novel therapeutics; Functional amyloids in organisms

Research Interests: Developing state-of-the-art machine learning approaches to extract radiological imaging and imaging-genomic biomarkers and to engender personalized prognostics in neuroscience and oncological applications

Technical Expertise: Advanced image analysis, neuroimaging, MRI, MEG/EEG, PET/SPECT

Research Interests: Understanding tissue organization via machine learning; intra-tumor heterogeneity; computational image analysis and spatial statistics

Technical Expertise: Machine learning (both classical and deep). Image analysis specifically for microscopy

Research Interests: Integrating structure, kinetics, and computation to understand the molecular determinants and regulatory mechanisms of microtubule dynamics

Research Interests: Biostatistics, statistical genomics, multi-omics

Research Interests: Antibiotic resistance and sensitivity; single molecule biophysics; synthetic biology

Research Interests: Immunogenomics, Tumor genomics, Bioinformatics, Biostatistics, and Machine learning

Research Interests: Biostatistics, Machine learning, Immunogenomics, and Tumor genomics

Research Interests: Improving treatments for cancer by applying computer science and statistical methodologies to analyzing high-throughput biological data. New models and tools are currently in development to assist the investigation of disease mechanisms and their related diagnostic innovations.

Research Interests: Biostatistics, bioinformatics, statistical genomics, clinical trial design, and biomarker studies

Technical Expertise: Developing and applying statistical and computational approaches to decipher genetic and genomic problems, in particular, human complex traits.

Research Interests: Statistical genetics, genetic epidemiology, bioinformatics, gene mapping for complex traits

Research Interests: Epigenomics, single-cell data analysis, machine learning and deep learning algorithm development

Research Interests: Statistical genetics, forward genetics screening, statistical computation, next-generation sequencing, genetic association studies

Technical Expertise: Statistical genetics, microbiome, statistical computation, bioinformatics, deep learning

Research Interests: Computational Neuroscience

Research Interests: Machine Learning and Statistical Methods for Genomics, Sequence Basis of Genome Regulation, Statistical Methods for Single-cell Data Analysis, The Evolution of Noncoding Genome.

Associate Members

These faculty members do not accept graduate students. They participate in teaching, co-mentoring, exam and dissertation committees, and all other program responsibilities.

Research Interests: Development of methods to study heteroplasmy and the somatic evolution of cancers.

Research Interests: Statistics, statistical genetics, genetic epidemiology, genome-wide association studies