Publications

Carson, Bryan C.; Doyle, Barney L.; James, Conrad D.; May, Elebeoba E.; Powell, Amy J.; Ackerman, Eric A.

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.

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.; Crozier, Paul C.; Brozik, Susan M.

Abstract not provided.

May, Elebeoba E.; Misra, Milind

Abstract not provided.

May, Elebeoba E.; Dolan, Patricia L.; Crozier, Paul C.; Brozik, Susan 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.

May, Elebeoba E.; Dolan, Patricia L.; Crozier, Paul C.; Brozik, Susan M.

Abstract not provided.

2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA

Oishi, Meeko; May, Elebeoba E.

Accurate simulation of biological networks is difficult not only due to the computational cost associated with large-scale systems simulation, but also due to the inherent limitations of mathematical models. We address two components to improve biological circuit simulation accuracy: 1) feasible initial conditions, and 2) identification of critical yet unknown model parameters. For those parameters that may not be available from experimental data, we incorporate reachability analysis to enhance our optimization/simulation framework and estimate those parameters that are capable of creating behaviors consistent with known experimental data. We apply these techniques to a biological circuit model of tryptophan biosynthesis in E. coli, and quantify the improvement in simulation accuracy when reachability analysis is used. © 2008 IEEE.

May, Elebeoba E.; Dolan, Patricia L.; Crozier, Paul C.; Brozik, Susan M.

Abstract not provided.

May, Elebeoba E.; Schiek, Richard S.

Abstract not provided.

2005 NSTI Nanotechnology Conference and Trade Show - NSTI Nanotech 2005 Technical Proceedings

Schiek, Richard S.; May, Elebeoba E.

Genetic expression and control pathways can be successfully modeled as electrical circuits. To tackle large multicellular and genome scale simulations, the massively-parallel, electronic circuit simulator, Xyce™ [11], was adapted to address biological problems. Unique to this bio-circuit simulator is the ability to simulate not just one or a set of genetic circuits in a cell, but many cells and their internal circuits interacting through a common environment. Additionally, the circuit simulator Xyce can couple to the optimization and uncertainty analysis framework Dakota [2] allowing one to find viable parameter spaces for normal cell functionality and required parameter ranges for unknown or difficult to measure biological constants. Using such tools, we investigate the Drosophila sp. segmental differentiation network's stability as a function of initial conditions.

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.

May, Elebeoba E.; Schiek, Richard S.

Abstract not provided.

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

May, Elebeoba E.; Johnston, Anna M.

Abstract not provided.

May, Elebeoba E.

Abstract not provided.

May, Elebeoba E.

Using a multi-cellular, pathway model approach, we investigate the Drosophila sp. segmental differentiation network's stability as a function of initial conditions. While this network's functionality has been investigated in the absence of noise, this is the first work to specifically investigate how natural systems respond to random errors or noise. Our findings agree with earlier results that the overall network is robust in the absence of noise. However, when one includes random initial perturbations in intracellular protein WG levels, the robustness of the system decreases dramatically. The effect of noise on the system is not linear, and appears to level out at high noise levels.

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.

May, Elebeoba E.

Abstract not provided.

Schiek, Richard S.; May, Elebeoba E.

Abstract not provided.

Schiek, Richard S.; May, Elebeoba E.

Abstract not provided.

BioSystems

May, Elebeoba E.; Vouk, Mladen A.; Bitzer, Donald L.; Rosnick, David I.

Our research explores the feasibility of using communication theory, error control (EC) coding theory specifically, for quantitatively modeling the protein translation initiation mechanism. The messenger RNA (mRNA) of Escherichia coli K-12 is modeled as a noisy (errored), encoded signal and the ribosome as a minimum Hamming distance decoder, where the 16S ribosomal RNA (rRNA) serves as a template for generating a set of valid codewords (the codebook). We tested the E. coli based coding models on 5′ untranslated leader sequences of prokaryotic organisms of varying taxonomical relation to E. coli including: Salmonella typhimurium LT2, Bacillus subtilis, and Staphylococcus aureus Mu50. The model identified regions on the 5′ untranslated leader where the minimum Hamming distance values of translated mRNA sub-sequences and non-translated genomic sequences differ the most. These regions correspond to the Shine-Dalgarno domain and the non-random domain. Applying the EC coding-based models to B. subtilis, and S. aureus Mu50 yielded results similar to those for E. coli K-12. Contrary to our expectations, the behavior of S. typhimurium LT2, the more taxonomically related to E. coli, resembled that of the non-translated sequence group. © 2004 Elsevier Ireland Ltd. All rights reserved.

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.

Schiek, Richard S.; May, Elebeoba E.

Abstract not provided.