The MDSCTK provides a clean interface for performing spectral clustering on molecular dynamics trajectories. It assumes some basic experience with GROMACS (http://www.gromacs.org) and a little R (http://www.r-project.org/), and uses the ARPACK (http://www.caam.rice.edu/software/ARPACK/) routines for performing sparse eigen decomposition as well as parallel, database-driven (ORACLE Berkeley DB) methods for fast computation of large, sparse RMSD distance matrices.
ESPS: Electrostatic Parameter Set
(COMING SOON!) The ESPS package contains a set of parameters for nonstandard residues and molecules for use with the PDB2PQR framework (http://www.poissonboltzmann.org/pdb2pqr/) which is in-turn used by programs like the Adaptive Poisson-Boltzmann Solver (http://www.poissonboltzmann.org/apbs/) to perform continuum electrostatic calculations. Currently, the package provides parameters for the GLYCAM (http://glycam.ccrc.uga.edu/) carbohydrate force field, the Modified Nucleic Acid Parameters Database (http://ozone3.chem.wayne.edu/cgi-bin/login/modi/login/showLoginPage.cgi), and non-Standard Amino Acids that are part of the MKTPP portion of the AmberTools12 package (http://ambermd.org/).
One past project focused on the development of biologically inspired computational mechanisms for effective robot learning and control. In particular, David Noelle (Univ. of Calif., Merced), and I developed a software toolkit that allows for the easy integration of a powerful computational neuroscience model of working memory into robotic (or really any artificial) systems. This model of working memory has been used to train robots to perform standard laboratory tests of working memory function, such as the delayed saccade task, as well tasks in robot navigation, motior skill learning, and object manipulation.
Another past project focused on the development of a tool to fit parameters for cell proliferation/differentiation models to experimental cell count distribtions. Common methods such as Monte Carlo simulations have trouble with this kind of problem since many cell count observations occur with very low probability. Therefore, I developed a tool to generate C codes for (unsimplified) analytic equations which can be used to numerically calculate the resulting distributions to machine precision and aid model fitting. Work reported in: "Analytic parameter fitting in stochastic stem cell models." J. L. Phillips, J. E. Manilay, and M. E. Colvin Biophysical Journal 98(3), 739a. (2010) doi:10.1016/j.bpj.2009.12.4052
This project focused on the development of a version of ALCOVE that utilized a more biologically plausible mechanism for learning dimensional attention based on temporal difference learning instead of traditional error-backpropagation learning. Work reported in: Phillips, J. L. and Noelle, D. C. (2004) Reinforcement learning of dimensional attention for categorization. In Proceedings of the 26th Annual Meeting of the Cognitive Science Society. Chicago, IL.