Publications

PLOS Computational Biology

Joo, Jaewook J.; Plimpton, Steven J.; Faulon, Jean-Loup M.

Abstract not provided.

Journal of Theoretical Biology

Joo, Jaewook J.; Plimpton, Steven J.; Faulon, Jean-Loup M.

Abstract not provided.

Joo, Jaewook J.; Plimpton, Steven J.; Faulon, Jean-Loup M.

Abstract not provided.

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

May, Elebeoba E.; Leitao, Andrei; Faulon, Jean-Loup M.; Joo, Jaewook J.; Misra, Milind; Oprea, Tudor I.

Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis (Mtb), is a growing international health crisis. Mtb is able to persist in host tissues in a nonreplicating persistent (NRP) or latent state. This presents a challenge in the treatment of TB. Latent TB can re-activate in 10% of individuals with normal immune systems, higher for those with compromised immune systems. A quantitative understanding of latency-associated virulence mechanisms may help researchers develop more effective methods to battle the spread and reduce TB associated fatalities. Leveraging BioXyce's ability to simulate whole-cell and multi-cellular systems we are developing a circuit-based framework to investigate the impact of pathogenicity-associated pathways on the latency/reactivation phase of tuberculosis infection. We discuss efforts to simulate metabolic pathways that potentially impact the ability of Mtb to persist within host immune cells. We demonstrate how simulation studies can provide insight regarding the efficacy of potential anti-TB agents on biological networks critical to Mtb pathogenicity using a systems chemical biology approach. © 2008 IEEE.

The Annals of New York Academy of Sciences

Joo, Jaewook J.; Plimpton, Steven J.; Swiler, Laura P.; Faulon, Jean-Loup M.

Abstract not provided.

Nature Chemical Biology

Rintoul, Mark D.; Faulon, Jean-Loup M.

Abstract not provided.

Faulon, Jean-Loup M.; Zhang, Zhaoduo Z.; Martino, Anthony M.; Timlin, Jerilyn A.; Haaland, David M.; Davidson, George S.; May, Elebeoba E.; Slepoy, Alexander S.

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

Brown, William M.; Faulon, Jean-Loup M.; Gray, Genetha A.; Hunt, Thomas W.; Schoeniger, Joseph S.; Slepoy, Alexander S.; Young, Malin M.

We have developed a novel approach to modeling the transmembrane spanning helical bundles of integral membrane proteins using only a sparse set of distance constraints, such as those derived from MS3-D, dipolar-EPR and FRET experiments. Algorithms have been written for searching the conformational space of membrane protein folds matching the set of distance constraints, which provides initial structures for local conformational searches. Local conformation search is achieved by optimizing these candidates against a custom penalty function that incorporates both measures derived from statistical analysis of solved membrane protein structures and distance constraints obtained from experiments. This results in refined helical bundles to which the interhelical loops and amino acid side-chains are added. Using a set of only 27 distance constraints extracted from the literature, our methods successfully recover the structure of dark-adapted rhodopsin to within 3.2 {angstrom} of the crystal structure.

Davidson, George S.; May, Elebeoba E.; Faulon, Jean-Loup M.

In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.

Proposed for publication in OMICS: A Journal of Integrative Biology, Vol. 6, No.4, 2002.

Heffelfinger, Grant S.; Faulon, Jean-Loup M.; Frink, Laura J.; Haaland, David M.; Hart, William E.; Lane, Todd L.; Heffelfinger, Grant S.; Plimpton, Steven J.; Roe, Diana C.; Timlin, Jerilyn A.; Martino, Anthony M.; Rintoul, Mark D.; Davidson, George S.

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to ''achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life.'' While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, ''Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling.'' This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.