Computational Biology and Bioinformatics

(Andrew Barto, Oliver Brock, David Kulp, Hava Siegelmann, Ileana Streinu)

Computational Biology refers broadly to the application of mathematical modeling, high-throughput computing, data integration, and algorithm development to generate testable hypotheses about biological entities and processes. Using these approaches, we attempt to answer important questions in molecular biology, genetics, biologically-inspired computation, and neuroscience, such as how a protein folds, how genes are expressed and regulated, how system-level behavior arises from the genetic code, how evolutionary history can inform biological processes, how biological systems are able to process information robustly, and how they learn and adapt to the environment. Our research is fundamentally concerned with efficient approaches to traverse large search spaces, perform inferences over high dimensional data sets, formally integrate diverse biological knowledge, and model biological systems and their behavior. Bioinformatics refers to the data management and processing of biomolecular data often collected on a genome-wide scale. Computational biologists and bioinformaticists typically leverage data generated by modern high-throughput assays including microarrays, mass spectrometry, confocal microscopy, sequencing and other advances in biotechnology.

Autonomous Learning Laboratory
The Autonomous Learning Laboratory (ALL), formerly the Adaptive NetWorks (ANW) Laboratory, focuses on both machine and biological learning. Areas of study include reinforcement learning, artificial neural networks, and biologically-inspired models of adaptive motor control.

Biologically Inspired Neural & Dynamical Systems Laboratory
The Biologically Inspired Neural & Dynamical Systems Laboratory aims to apply techniques developed in computer science to problems in biology and to turn insights gained from biological systems to construct better computational algorithms. A specific goal is to employ computational techniques from machine learning, such as clustering and Bayesian network modeling, to solve problems in functional genomics. Another goal of the lab is to build mathematical models of neural circuitries in the brain.

Computational Biology Laboratory
The Computational Biology Laboratory uses a wide range of computational approaches to investigate fundamental biological problems in genetics, genomics, and structural molecular biology. These include regulatory network inference, the genetics of alternative splicing, protein structure prediction, and protein docking. We also work on technical improvements in variation detection and sequencing using DNA microarrays.

Laboratory in Kine(ma)tics and Geometry (LinKaGe)
The Laboratory in Kine(ma)tics and Geometry's research belongs to computational geometry: the investigation of algorithmic problems with geometric content. Its focus is on rigidity, flexibility and motion for constrained structures like linkages or frameworks in mechanics or robotics. In an interdisciplinary spirit, LinKaGe also considers applications to computational biology, and investigates computational methods for motion generation in molecules (in particular, proteins).

Robotics and Biology Laboratory
The Robotics and Biology Laboratory develops algorithms and methods that enable robots to perform complex tasks in unstructured and dynamic environments, a research area referred to as Autonomous Mobile Manipulation. Our approaches require the integration of hardware, control, planning, manipulation, perception, learning, and reasoning. Some of the insights from our robotics research also apply to structural molecular biology, where we investigate algorithms for protein structure prediction and protein docking.