Computational Biology

Computational biology focuses on the application of computational techniques to problems in molecular biology, genomics, and biophysics. Using tools adapted from computer science, mathematics, statistics, physics, chemistry, and other quantitative disciplines, computational biologists address a wide variety of problems ranging from analysis of protein structure and function, to management of clinical data.

Faculty working in computational biology: 

Paul Adams

Adjunct Professor, Department of Bioengineering
Senior Scientist, Lawrence Berkeley Laboratory
Division Director, Molecular Biophysics & Integrated Bioimaging

Development of new algorithms and methods for structural biology. Structural studies of large macromolecular machines. Development of cellulosic biofuels.

Adam Arkin

Dean A. Richard Newton Memorial Professor, Bioengineering
Senior Faculty Scientist, Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory
Director, Berkeley Synthetic Biology Institute
CEO/CSO, DOE Systems Biology Knowledgebase
PI and Co-Director, ENIGMA SFA

The Arkin Lab’s research focuses on the systems and synthetic biology of microorganisms. They are experts in theory, computation and experiments surrounding the modeling of biological systems at the molecular and population level and have developed a number of genome scale technologies with which we can rapidly assess the genomic function of uncharacterized microorganisms. The lab’s models span the deterministic and stochastic analysis of both homogeneous and spatially distributed systems.

Teresa Head-Gordon

Chancellor’s Professor, Department of Bioengineering,
Department of Chemistry,
Department of Chemical and Biomolecular Engineering

My research program encompasses the development of general computational and experimental methodologies applied to biochemistry and biology in the areas of water and aqueous hydration, protein folding, structure prediction, protein complexes, membrane proteins, and non-disease and disease protein aggregation. I have also been involved in local and national service, education, and training, which extends to promoting and developing the blueprint for computational biology and biophysical research for the future.

Ian Holmes

Associate Professor, Bioengineering

The Holmes Lab brings techniques from machine learning, statistical linguistics, phylogenetics, and web development to bear on the interpretation and analysis of genomic data. Examples include the application of context-free grammars to understanding DNA and RNA structure; the use of phylogenetic methods in genome annotation, and to detect recombination breakpoints; the development of machine learning algorithms for bioinformatics models; the reconstruction of insertion, deletion and transposition events in genome evolutionary histories; statistical algorithms for metagenomics species distribution analysis; and dynamic-HTML web applications for collaborative genomic data analysis.

Patrick Hsu

Assistant Professor, Bioengineering

The Hsu Lab aims to understand and manipulate the genetic circuits that control brain and immune cell function to improve human health. We explore the rich biological diversity of nature to create new molecular technologies, perturb complex cellular processes at scale, and develop next-generation gene and cell therapies. To do this, our group draws from a palette of experimental and computational techniques including CRISPR-Cas systems, single cell genomics, engineered viruses, brain organoids, and pooled genetic screens.

Current interests include 1) inventing novel approaches for editing the postmitotic genome, 2) developing engineered vehicles for therapeutic macromolecule delivery, and 3) leveraging library screens and brain organoids to interrogate human neuroscience at scale.

Richard Karp

University Professor, Bioengineering
University Professor, Electrical Engineering & Computer Science
University Professor, Industrial Engineering & Operations Research

Algorithms in computational biology, inference of regulatory structure from protein-protein interaction data.

Liana Lareau

Assistant Professor

A single genome produces the huge diversity of cells and tissues needed to make a human by regulating gene expression to turn on and off the right genes at the right times. The final, post-transcriptional steps of gene expression — RNA processing and translation — are essential to the proper outcome. Our goal is to understand how these layers of regulation are encoded in gene sequences and how disruptions to this regulation can cause disease. Our research uses machine learning and other computational methods, coupled with high-throughput experiments, to understand how post-transcriptional regulation leads to robust and flexible control of gene expression.

Mohammad Mofrad

Professor, Bioengineering
Professor, Mechanical Engineering
Faculty Scientist, Lawrence Berkeley National Lab

Our research program is focused on understanding cell mechanobiology and molecular mechanisms involved in human disease, in particular cardiovascular dysfunctions, brain and neurological disorders, and cancer.

Boris Rubinsky

Professor Emeritus, Bioengineering
Professor of the Graduate School, Mechanical Engineering

Bioelectronic devices, biotransport, medical imaging, electrical impedance tomography.

Kimmen Sjölander

Professor, Bioengineering
Professor, Plant and Microbial Biology

My research focus is the application of computational methods for biological discovery.

Aaron Streets

Assistant Professor, Bioengineering

The Streets lab is interested in applying lessons from mathematics, physics, and engineering, to invent tools that help us dissect and quantify complex biological systems. Our goal is to uncover laws that govern the interactions of molecules inside the cell and the interactions between cells in a tissue or organism, by making precision measurements on single cells. In pursuit of this goal, we exploit three core technologies; microfluidics, microscopy, and genomics.