Projects

Environmental Effects on Protein-Protein Interactions

A complete understanding of how environmental factors influence HIV transmission is needed for the development of an effective HIV vaccine. I develop and utilize state-of-the-art modelling and simulation methods to investigate protein-protein interactions involved in viral transmission at the molecular level, and determine how relevant environmental factors (such as salt concentration and pH) affect these interactions. This project is a collaboration with researchers at Los Alamos National Laboratory.

Mutational Analysis of Metalloproteins

Modelling and simulation provide a rational approach for predicting how mutations to metalloprotein enzymes will degrade or enhance catalysis of viable substrates. These predictions greatly aid the search for useful mutations by prioritizing the screening order of mutants in the laboratory, lowering costs and producton times. Key targets have been enzymes which degrade chemical warfare nerve agents and may be used for therapeutic or sanitation purposes. This project is a collaboration with researchers at Los Alamos National Laboratory.

Multiscale Modeling and Simulation of Multidrug Resistance

Bacteria have developed several mechanisms which contribute to multidrug resistance and hinder the development of new antibiotic treatments. If the mechanisms involved in resistance were more fully understood, novel treatments might be developed which could rescue our current stream of antibiotics. I use modelling and simulation to study the structure and function of multidrug resistance efflux pumps, one of the main contributors to antibiotic resistance. I aim to uncover a complete mechanistic understanding of pump function in order to aid efforts to effectively subvert their function. This project is a collaboration with researchers at Los Alamos National Laboratory.

Molecular Simulation Analysis

There are many biological and chemical processes which can be studied in remarkable detail using computational modeling and simulation. The significant computational cost of these approaches suggests that rigorous analysis of the resulting data is necessary to justify the consumed computational resources, and state-of-the-art statistical and machine learning methods are poised to fill this need. However, these methods are often developed outside of the scientific domains where they are applied, and the nuances faced when working with real data make it difficult to discern when these approaches are achieving their intended purpose. I use domain knowledge to construct model-based validation frameworks which help to resolve such issues. Past domains of interest include intrinsically disordered and natively folded proteins. This project is a collaboration with researchers at the University of California, Merced.

Robot Prefrontal Cortex (PFC) Working Memory Toolkit (WMtk)

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 systems. Current work involves combining the toolkit with models of other brain systems and creating more efficient knowledge representation structures which are more flexible and comprehensive than those currently used by toolkit. 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, motor skill learning, and object manipulation.

Work

Papers

  • Khan, N. and Phillips, J. L. (in press). Combined model for sensory-based and feedback-based task switching: solving hierarchical reinforcement learning problems statically and dynamically with transfer learning. In Proceedings of the 32nd International Conference on Tools with Artificial Intelligence.
  • Syzonenko, I. and Phillips, J. L. (2020). Accelerated protein folding using greedy-proximal A*. Journal of Chemical Theory and Computation. [LINK]
  • Williams, A. S. and Phillips, J. L. (2020). Transfer reinforcement learning using output-gated working memory. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, NY. [PDF] or [preprint-PDF]
  • Connors, K. A., Beasley, A., Barron, M. G., Belanger, S. E., Bonnell, M., Brill, J. L., de Awart, D., Kienzler, A., Krailler, J., Otter, R., Phillips, J. L., and Embry, M. R. (2019). Creation of a curated aquatic toxicology database: EnviroTox. Environmental Toxicology and Chemistry. [LINK]
  • Morton, S. P., Phillips, J. B., and Phillips, J. L. (2019). The molecular basis of pH-modulated HIV gp120 binding revealed. Evolutionary Bioinformatics. [LINK]
  • Morton, S. P., Howton, J., and Phillips, J. L. (2018). Sub-class differences of pH-dependent HIV GP120-CD4 interactions. In Proceedings of the 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (CSBW), Washington, DC. [PDF] or [PDF]
  • Phillips, J. L., Colvin, M. E., and Newsam, S. (2018). Dimensionality estimation of protein dynamics using polymer models. In Proceedings of the 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (CSBW), Washington, DC. [PDF] or [PDF]
  • Syzonenko, I. and Phillips, J. L. (2018). Hybrid spectral/subspace clustering of molecular dynamics simulations. In Proceedings of the 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Washington, DC. [PDF] or [PDF]
  • Jovanovich, M. P. and Phillips, J. L. (2018). N-task learning: solving multiple or unknown numbers of reinforcement learning problems. In Proceedings of the 40th Annual Meeting of the Cognitive Science Society, Madison, WI. [PDF] [HTML]
  • Williams, A. S. and Phillips, J. L. (2018). Multilayer context reasoning in a neurobiologically inspired working memory model for cognitive robots. In Proceedings of the 40th Annual Meeting of the Cognitive Science Society, Madison, WI. [PDF] [HTML]
  • Mueller, R. T., Travers, T., Cha H.-J., Phillips, J., Gnanakaran, S., and Pos, K. M. (2017). Switch loop flexibility affects substrate transport of the AcrB efflux pump. Journal of Molecular Biology, 429 (24), 3863-3874. [LINK]
  • Morton, S. P., Phillips, J. B., and Phillips, J. L. (2017). High-throughput structural modeling of the HIV transmission bottleneck. 2017 IEEE International Conference on Bioinformatics and Biomedicine Workshops, Kansas City, MO. [PDF] or [preprint-PDF]
  • Howton, J. and Phillips, J. L. (2017). Computational modeling of pH-dependent gp120-CD4 interactions in founder and chronic HIV strains. Proceedings of the 8th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (CSBW), Boston, MA. [PDF] or [PDF]
  • DuBois, G. M. and Phillips, J. L. (2017). Working memory concept encoding using holographic reduced representations. Proceedings of the 28th Modern Artificial Intelligence and Cognitive Science Conference, Fort Wayne, IN. [PDF]
  • Phillips, J. L., and Gnanakaran, S. (2015). A data-driven approach to modeling the tripartite structure of multidrug resistance efflux pumps. Proteins: Structure, Function, and Bioinformatics, 83 (1), 46-65. [PDF]
  • Gottardo, R., Bailer, R. T., Korber, B T., Gnanakaran, S., Phillips, J., Shen, X., Tomaras, G. D., Turk, E., Imholte, G., Eckler, L., Wenschuh, H., Zerweck, J., Greene, K., Gao, H., Berman, P. W., Francis, D., Sinangil, F., Lee, C., Nitayaphan, S., Rerks-Ngarm, S., Kaewkungwal, J., Pitisuttithum, P., Tartaglia, J., Robb, M. L., Michael, N. L., Kim, J. H., Zolla-Pazner, S., Haynes, B. F., Mascola, J. R., Self, S., Gilbert, P., Montefiori, D. C. (2013). Plasma IgG to linear epitopes in the V2 and V3 regions of HIV-1 gp120 correlate with a reduced risk of infection in the RV144 vaccine efficacy trial. PLoS One. [PDF]
  • Stieh, D., Phillips J. L., Rogers P. M., King, D. F., Cianci, G. C., Jeffs, S. A., Gnanakaran, S., and Shattock, R. J. (2013). Dynamic electrophoretic fingerprinting of the HIV-1 envelope glycoprotein. Retrovirology, 10 (33). [PDF]
  • Phillips, J. L., Colvin, M. E., and Newsam, S. (2011). Validating clustering of molecular dynamics simulations using polymer models. BMC Bioinformatics, 12 (1), 445. [PDF]
  • Yamada, J., Phillips, J. L., Patel, S., Goldfien, G., Calestagne-Morelli, A., Huang, H., Reza, R., Acheson, J., Krishnan, V. V., Newsam, S., Gopinathan, A., Lau, E. Y., Colvin, M. E., Uversky V. N., and Rexach M. F. (2010). A bimodal distribution of two distinct categories of instrinsically-disordered structures with separate functions in FG nucleoporins. Molecular and Cellular Proteomics, 9, 2205-2224. [PDF]
  • Lau, E. Y., Phillips, J. L., and Colvin, M. E. (2009). Molecular dynamics simulations of highly charged green fluorescent proteins. Molecular Physics: An International Journal at the Interface Between Chemistry and Physics, 107 (8), 1233-1241. [PDF]
  • Phillips, J. L., Colvin, M. E., Lau, E. Y., and Newsam, S. (2008). Analyzing dynamical simulations of intrinsically disordered proteins using spectral clustering. In IEEE BIBM 2008 Workshop on Computational Structural Bioinformatics, Philadelphia, PA. [PDF]
  • Tugcu, M., Wang, X., Hunter, J. E., Phillips, J., Noelle, D., and Wilkes, D. M. (2007). A computational neuroscience model of working memory with application to robot perceptual learning. In Proceedings of the 3rd International Conference on Computational Intelligence, Banff, Alberta, Canada. [PDF]
  • Phillips, J. L., and Noelle, D. C. (2006). Working memory for robots: inspirations from computational neuroscience. In Proceedings of the 5th International Conference on Development and Learning, Bloomington, IN. [PDF]
  • Phillips, J. L., and Noelle, D. C. (2005). A biologically inspired working memory framework for robots. In Proceedings of the 14th IEEE International Workshop on Robot and Human Interactive Communication, Nashville, TN. [PDF]
  • Phillips, J. L., and Noelle, D. C. (2005). A biologically inspired working memory framework for robots. In Proceedings of the 27th Annual Meeting of the Cognitive Science Society, Stresa, Italy.
  • Phillips, J. L., Kogekar, S., and Adams, J. A. (2004). Emergency automated response system (EARS). In Proceedings of the 48 Annual Meeting of the Human Factors and Ergonomics Society, New Orleans, LA. [PDF]
  • 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. [PDF]

Ph.D. Dissertation

  • Phillips, J. L. (2012). Validation of computational approaches for studying disordered and unfolded protein dynamics using polymer models. University of California, Merced. [PDF]

M.S. Thesis

  • Phillips, J. L. (2004). Reinforcement learning of dimensional attention for categorization. Vanderbilt University. [PDF]

Posters

  • Phillips, J. L., Lau, E. Y., Colvin, M. E., and Newsam, S. (2010). Dimensionality reduction reveals differences between disordered protein dynamics and early-stage protein folding dynamics. 24th Annual Symposium of the Protein Society, San Diego, CA.
  • Phillips, J. L., Lau E. Y., Rexach, M., Newsam, S., and Colvin, M. E. (2010). Differences between unfolded and disordered protein dynamics. 1st Gordon Research Conference on Intrinsically Disordered Proteins, Davidson, SC.
  • Phillips, J. L., Lau E. Y., Krishnan, V. V., Rexach, M., Newsam, S., and Colvin, M. E. (2010). Metric scaling for dimensionality reduction of disordered protein dynamics. 54th Annual Meeting of the Biophysical Society, San Francisco, CA. (2010 Student Research Achievement Award)
  • Phillips, J. L., Manilay, J. O., and Colvin, M. E. (2010). Analytic parameter fitting in stochastic stem cell models. 54th Annual Meeting of the Biophysical Society, San Francisco, CA.
  • Phillips, J. L., Lau E. Y., Krishnan, V. V., Rexach, M., Newsam, S., and Colvin, M. E. (2009). Dynamics analysis of unstructured FG-nucleoporins. 23nd Annual Symposium of the Protein Society, Boston, MA. (2009 Best Student Poster Award)
  • Phillips, J. L., Lau E. Y., Krishnan, V. V., Rexach, M., Newsam, S., and Colvin, M. E. (2008). Characterizing intrinsically disordered FG-nucleoporins using molecular dynamics. 22nd Annual Symposium of the Protein Society, San Diego, CA.

People

Current Students

[Image (JPEG 23K): Scott Morton]

Scott P. Morton

Graduate Student
Ph.D. Program - Computational Science
Email: spm3c[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~spm3c/

Currently researching the ElectroStatic Surface Charge Pipeline for HIV antibody binding characteristics. Additionally converting all Bash scripts into a unified python language format with a json based configuration file. Further research goals include a rewrite of frodaN for ease of use and parallel processing potentials involving CUDA and/or MPI, Massively Parallel Processing (MPP) of model protein sequences for predicting binding characteristics of HIV and HIV antibodies, potential electronic circuitry characteristics of protein sequences as a means of binding energy.

Arthur S. Williams

Graduate Student
Ph.D. Program - Computational Science
Email: asw3x[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~asw3x/

Hierarchical and indirection-based working memory models for task generalization in partially observable reinforcement learning domains.

Will H. Haase

Undergraduate Student
B.S. Program - Computer Science
Email: whh2p[at]mtmail.mtsu.edu
WWW: None

Minimizing catatrophic interference via unitization and generative recurrent neural network models.

Nibraas A. Khan

Undergraduate Student
B.S. Program - Computer Science
Email: nak2z[at]mtmail.mtsu.edu
WWW: None

Combined working memory and N-task models for partially-, non-observable reinforcement learning problems.

Lucas Remedios

Undergraduate Student
B.S. Program - Computer Science
Email: lwr2k[at]mtmail.mtsu.edu
WWW: https://www.cs.mtsu.edu/~lwr2k/

Keras/TensorFlow tools for N-task learning.

Matthew T. Radice

Graduate Student
Ph.D. Program - Computational Science
Email: mtr3t[at]mtmail.mtsu.edu
WWW: None

Limitations of Q-Learning for partially observable reinforcement learning domains and compensatory mechanisms via working memory.

Past Students

Ivan Syzonenko

Graduate Student
Ph.D. Program - Computational Science
Email: is2k[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~is2k/

Ivan's research has focused on understanding and overcoming the limitations of machine learning algorithms, particularly clustering, in biomolecular simulation analysis. More recently, he is developing and testing methods for accelerated unbiased targeted MD simulations using classical pathfinding approaches with a focus on protein folding.

Huizhi Wang

Graduate Student
M.S. Program - Computer Science
Email: hw3m[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~hw3m/

Dimensional attention mechanisms for holographic reduced representational encodings and category learning.

Ngozi C. Omatu

Undergraduate Student
B.S. Program - Biology
Email: nco2f[at]mtmail.mtsu.edu
WWW: None

Dimensional attention for working memory: accelerated early learning with asymptotically optimal performance.

Mike Jovanovich

Graduate Student
M.S. Program - Computer Science
Graduation: Fall 2017
Email: mpj2n[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~mpj2n/

Neurobiologically plausible models of working memory, task switching, and task generalization.

Joshua M. Arnold

Undergraduate Student
B.S. Program - Computer Science
Email: jma5x[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~jma5x/

Coupled action-working memory learning for partially observable reinforcement learning problems.

[Image (JPEG 16K): Cody Crawford]

Cody Crawford

Graduate Student
M.S. Program - Computer Science
Graduation: Summer 2017
Email: crn2k[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~crn2k/

Journalism today is dealing with so much data that better methods are needed to process it. Latent Dirichlet Allocation (LDA) is often used to sort text into topics. The Afghan War Diary (AWD) was processed with LDA and model trees to ascertain fatality numbers. The AWD was used in a separate study that analyzed the documents with point process modeling (PPM) to predict where conflicts would occur in space and time. We have combined the two approaches in this study, hopeful that the results will allow us to predict where and when conflicts occur and if the fatality numbers can also be obtained in reference to that. We anticipate that our results will show that PPM combined with LDA and model trees will give more useful results than using either of the methods separately.

Jonathan Howton

Graduate Student
M.S. Program - Computer Science
Graduation: Spring 2017
Email: jh6w[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~jh6w/

Jonathan's research focuses on using high-throughput structural analysis to understand how environmental factors affect protein binding particularly with regard to viral transmission.

[Image (JPEG 7K): Grayson Dubois] [Image (JPEG 178K): WMtk]

Grayson Dubois

Undergraduate Student
B.S. Program - Computer Science - B.S. Spring 2017
Email: Grayson.Dubois[at]mtsu.edu
WWW: http://www.cs.mtsu.edu/~gmd2n/

Grayson's research looks into a new method of representing concepts in artificial neural networks (ANNs) that mimic human working memory systems. This new method uses something called Holographic Reduced Representations (HRRs), which are powerful tools capable of representing compositional structure in distributed representations. Grayson has developed an engine for encoding and decoding HRRs and is working on integrating it into the Working Memory toolkit, a software library written in ANSI C++ designed to allow researchers to write simulations of learning tasks using ANNs modeled after working memory. The current toolkit requires the programmer to explicitly provide methods to convert concepts used by working memory from symbolic encodings (SE) to distributed encodings (DE). Grayson's HRR Engine will automate the process of SE/DE conversion, thus taking the burden off of the programmer and opening the door to future possibilities not possible with previous DE methods. These include but are not limited to the chunking of similar concepts in memory, transferability of learned behaviors between tasks, and long term memory.

Gary Hammock

Graduate Student
M.S. Program - Computer Science
Graduation: Fall 2016
Email: glh2y[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~glh2y/

Gary's research focuses on the development and testing of novel simulation-inspired methods for cryptography.

Robert Myers

Graduate Student
M.S. Program - Computer Science
Graduation: Spring 2016
Email: rvm2d[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~rvm2d/

Robert's research focused on the development and testing of distributed pathfinding algorithms.

[Image (PNG 20K): Michael Murphy] [Image (PNG 95K): Fractal]

Michael Murphy

Graduate Student
M.S. Program - Computer Science
Graduation: Spring 2016
Email: mcm7f[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~mcm7f/

Fractal dimension is a number that describes the self-similarity, or "complexity", of a geometry. In image processing, fractal dimension is often used as a novel method for contrasting and comparing image content. The Box-Counting Algorithm is one of the most popular methods of computing an estimate for the fractal dimension of an image, but the algorithm is influenced by many factors such as filtering and noise. Our research found a relationship between dimensional estimations and the variability in those estimations when using the Box-Counting Algorithm in the presence of increasing levels of uniform noise. This relationship provides a way to strengthen relative dimensional rankings between noisy images.

Stephen Kinser

Undergraduate Student
B.S. Program - Computer Science
Email: sdk2v[at]mtmail.mtsu.edu
WWW: http://www.cs.mtsu.edu/~sdv2k/

Stephen's research focused on converting a semi-autotmated protein electrostatics pipeline to a fully-automated version which requires significantly less user intervention.

About

[Image (JPEG 27K): Joshua L. Phillips]

Joshua L. Phillips

Associate Professor
Department of Computer Science
College of Basic and Applied Sciences
Middle Tennessee State University
Curriculum Vitae

Research Interests

My basic research interests are in computational biophysics and cognitive science. I focus primarily on the development of novel computational methods for addressing existing scientific or engineering problems related to molecular or structural biology. My background is in machine learning and neural networks, so my work often employs or is inspired by algorithmic approaches from these fields.

Contact

Office: KOM 356
1301 East Main Street
MTSU Box 48
Mufreesboro, TN 37132
615-494-7965 (office)
615-898-2397 (department)
615-898-5567 (fax)
Email: Joshua.Phillips@mtsu.edu
WWW: http://www.cs.mtsu.edu/~jphillips/
GITHUB: http://github.com/jlphillipsphd/
SLACK: https://phillipslab.slack.com/