Publications

Branda, Steven B.; Jebrail, Mais J.; Van De Vreugde, James L.; Langevin, Stanley A.; Bent, Zachary B.; Curtis, Deanna J.; Lane, Pamela L.; Carson, Bryan C.; La Bauve, Elisa L.; Patel, Kamlesh P.; Ricken, James B.; Schoeniger, Joseph S.; Solberg, Owen D.; Williams, Kelly P.; Misra, Milind; Powell, Amy J.; Pattengale, Nicholas D.; May, Elebeoba E.; Lane, Todd L.; Lindner, Duane L.; Young, Malin M.; VanderNoot, Victoria A.; Thaitrong, Numrin T.; Bartsch, Michael B.; Renzi, Ronald F.; Tran-Gyamfi, Mary B.; Meagher, Robert M.

Abstract not provided.

Branda, Steven B.; Jebrail, Mais J.; Van De Vreugde, James L.; Langevin, Stanley A.; Bent, Zachary B.; Curtis, Deanna J.; Lane, Pamela L.; Carson, Bryan C.; La Bauve, Elisa L.; Patel, Kamlesh P.; Ricken, James B.; Schoeniger, Joseph S.; Solberg, Owen D.; Williams, Kelly P.; Misra, Milind; Powell, Amy J.; Pattengale, Nicholas D.; May, Elebeoba E.; Lane, Todd L.; Lindner, Duane L.; Young, Malin M.; VanderNoot, Victoria A.; Thaitrong, Numrin T.; Bartsch, Michael B.; Renzi, Ronald F.; Tran-Gyamfi, Mary B.; Meagher, Robert M.

Abstract not provided.

Branda, Steven B.; Jebrail, Mais J.; Van De Vreugde, James L.; Langevin, Stanley A.; Bent, Zachary B.; Curtis, Deanna J.; Lane, Pamela L.; Carson, Bryan C.; La Bauve, Elisa L.; Patel, Kamlesh P.; Ricken, James B.; Schoeniger, Joseph S.; Solberg, Owen D.; Williams, Kelly P.; Misra, Milind; Powell, Amy J.; Pattengale, Nicholas D.; May, Elebeoba E.; Lane, Todd L.; Lindner, Duane L.; Young, Malin M.; VanderNoot, Victoria A.; Thaitrong, Numrin T.; Bartsch, Michael B.; Renzi, Ronald F.; Tran-Gyamfi, Mary B.; Meagher, Robert M.

Abstract not provided.

May, Elebeoba E.; Misra, Milind

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.

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.

Proposed for publication in IEEE Engineering in Medicine and Biology Magazine.

May, Elebeoba E.; Johnston, Anna M.

Abstract not provided.

May, Elebeoba E.; Johnston, Anna M.; Watson, Jean-Paul W.; Hart, William E.; Rintoul, Mark D.

We have found that developing a computational framework for reconstructing error control codes for engineered data and ultimately for deciphering genetic regulatory coding sequences is a challenging and uncharted area that will require advances in computational technology for exact solutions. Although exact solutions are desired, computational approaches that yield plausible solutions would be considered sufficient as a proof of concept to the feasibility of reverse engineering error control codes and the possibility of developing a quantitative model for understanding and engineering genetic regulation. Such evidence would help move the idea of reconstructing error control codes for engineered and biological systems from the high risk high payoff realm into the highly probable high payoff domain. Additionally this work will impact biological sensor development and the ability to model and ultimately develop defense mechanisms against bioagents that can be engineered to cause catastrophic damage. Understanding how biological organisms are able to communicate their genetic message efficiently in the presence of noise can improve our current communication protocols, a continuing research interest. Towards this end, project goals include: (1) Develop parameter estimation methods for n for block codes and for n, k, and m for convolutional codes. Use methods to determine error control (EC) code parameters for gene regulatory sequence. (2) Develop an evolutionary computing computational framework for near-optimal solutions to the algebraic code reconstruction problem. Method will be tested on engineered and biological sequences.

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.

May, Elebeoba E.; May, Elebeoba E.; Johnston, Anna M.; Hart, William E.; Watson, Jean-Paul W.; Pryor, Richard J.; Rintoul, Mark D.

A fundamental challenge for all communication systems, engineered or living, is the problem of achieving efficient, secure, and error-free communication over noisy channels. Information theoretic principals have been used to develop effective coding theory algorithms to successfully transmit information in engineering systems. Living systems also successfully transmit biological information through genetic processes such as replication, transcription, and translation, where the genome of an organism is the contents of the transmission. Decoding of received bit streams is fairly straightforward when the channel encoding algorithms are efficient and known. If the encoding scheme is unknown or part of the data is missing or intercepted, how would one design a viable decoder for the received transmission? For such systems blind reconstruction of the encoding/decoding system would be a vital step in recovering the original message. Communication engineers may not frequently encounter this situation, but for computational biologists and biotechnologist this is an immediate challenge. The goal of this work is to develop methods for detecting and reconstructing the encoder/decoder system for engineered and biological data. Building on Sandia's strengths in discrete mathematics, algorithms, and communication theory, we use linear programming and will use evolutionary computing techniques to construct efficient algorithms for modeling the coding system for minimally errored engineered data stream and genomic regulatory DNA and RNA sequences. The objective for the initial phase of this project is to construct solid parallels between biological literature and fundamental elements of communication theory. In this light, the milestones for FY2003 were focused on defining genetic channel characteristics and providing an initial approximation for key parameters, including coding rate, memory length, and minimum distance values. A secondary objective addressed the question of determining similar parameters for a received, noisy, error-control encoded data set. In addition to these goals, we initiated exploration of algorithmic approaches to determine if a data set could be approximated with an error-control code and performed initial investigations into optimization based methodologies for extracting the encoding algorithm given the coding rate of an encoded noise-free and noisy data stream.