Computational Biology

Welcome to our Computational Biology lab

Our lab focuses on developing algorithms to bridge between computer science and the life sciences. Our research contributions are in computational structural biology, biophysics, and bioinformatics. We investigate from a computational perspective problems concerning sequence, structure, dynamics, function, and interactions of biological molecules.

Our methods build on probabilistic search, optimization, and machine learning approaches, often combining ideas from sampling-based robot motion planning and evolutionary computation. Our research projects are diverse, detailed under Research. Recent work falls in the two categories highlighted below:

  • Evolution Strategies for Sampling Local Optima of Molecular Structure and Feature Spaces:

    • Clausen et al., "Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm," PLoS Comput Biol 2015
    • Sapin et al., "Evolutionary Search Strategies for Efficient Sample-based Representations of Multiple-basin Protein Energy Landscapes," IEEE Bioinf and BioMed 2015
    • Hashmi, Shehu, "idDock+:Integrating Machine Learning in Probabilistic Search for Protein-protein Docking," J Comput Biol 2015
    • Veltri et al., "Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming," IEEE/ACM Trans Comp Biol and Bioinf (TCBB) 2015
    • Kamath et al, "Effective Automated Feature Construction and Selection for Classification of Biological Sequences," PLoS One 2014
    • Olson, Shehu, "Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface," ACM Conf on Bioinf and Comp Biol 2013
  • Robotics-inspired Algorithms for Modeling Biomolecular Structures and Motions:

    • Maximova et al., "Computing Transition Paths in Multiple-Basin Proteins with a Probabilistic Roadmap Algorithm Guided by Structure Data," IEEE Bioinf and Biomed 2015
    • Molloy, Shehu, "A General, Adaptive, Roadmap-based Algorithm for Protein Motion Computation," IEEE Trans NanoBioScience (TNB) 2015
    • Molloy et al., "A Stochastic Roadmap Method to Model Protein Structural Transitions," Robotica 2015
    • Molloy et al., "Probabilistic Search and Energy Guidance for Biased Decoy Sampling in Ab-initio Protein Structure Prediction," IEEE/ACM Trans Comp Biol and Bioinf 2013
    • Molloy, Shehu, "Elucidating the Ensemble of Functionally-relevant Transitions in Protein Systems with a Robotics-inspired Method," BMC Struct Biol J 2013