Principal Member of the Technical Staff
(505) 844-9933 / email@example.com
(DOE/Sandia Lab Directed Research and Development)
The objective is to develop computational and experimental platforms that will enable the construction of multiscale theoretical models of the host and pathogen response during Ft-infection. Specifically we are exploring mechanisms related to: 1) the functional role of macrophages and effector molecules during immune response; 2) the response of Ft to changing environments. This multidisciplinary project is in collaboration with several Sandia scientist and labs/groups and the University of New Mexico Health Science Center.
(NIH K25 Mentored Quantitative Research Career Development Award; Sponsor National Institutes of Health’s National Heart, Lung, and Blood Institute)
We are using BioXyce to simulate latent tuberculosis infection (LTBI) in a murine model of Mtb infection. BioXyce models of Mtb-host interactions during latency will be used to generate empirically testable hypotheses regarding genetic and metabolic pathways in M. tuberculosis that contribute to LTBI and key signal transduction pathways in the mouse that impact the latency/reactivation process. NIH K25 grant in primary collaboration with University of New Mexico Health Science Center (R. Lyons, primary mentor) and secondary collaboration with Los Alamos National Laboratories (A. Perelson, secondary mentor).
BioXyce is a biological network modeling tool that is based on the massively parallel modeling and simulation tool Xyce, used within Sandia and the Deparment of Energy (DOE) to perform electrical circuit modeling [May and Schiek, 2009]. At the cellular level, biological regulation networks are modeled as electrical circuits where signals are produced, propagated and sensed. BioXyce uses the following equivalents: chemical mass as charge, mass flux as electric current, concentration as voltage, stoichiometric conservation as Kirchhoff’s voltage law, and mass conservation as Kirchhoff’s current law. With BioXyce, one can simulate large control networks consisting of entire cells, homogeneous cell cultures, or heterogenous interacting host-pathogen systems in order to understand the dynamics and stability of such systems. The input parameters for BioXyce, collected from literature and databases like BioCyc, KEGG, and BRENDA, are optimized using the DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) UQ (uncertainty quantification) toolkit.
Leveraging the BioXyce models of Mtb biochemical pathways involved in pathogen persistence, in collaboration with collaborators at the University of New Mexico-HSC Biocomputing Group and the University of North Carolina — Chapel Hill, we are developing a systems chemical biology (SCB) platform to enable insight on the ability of small molecules to perturb and modulate microbial systems [Oprea et al., 2007; May et al. 2008; Faulon et al., 2008]. Systems chemical biology is a method for evaluating the effect of small molecules at the level of the entire biological system, by taking into account complex network relationships within the system.
We are exploring the implications of understanding hybridization using the mathematical framework of error control coding theory and how this requires not only the use of information theoretic analysis tools, but compels us to view and model biomolecular systems as information transmission and processing systems. Using the genetic communication theory paradigm, we investigate coding theory algorithms for in silico categorization of single nucleotide polymorphisms based on the calculation of syndromes. We explore the use of coding theory frameworks in the design of in vitro computational biosensors, and design of error correcting biosensors for monitoring biomolecular systems.
DNA-based Intelligent Microsensors for Genetically Modified Organisms
In collaboration with the Brozik Lab at Sandia National Laboratories, we performed research on the development of application-specific, rapid, stand-alone computational biosensors for concurrent detection and classification of nucleic-acid targets using deoxyribozyme molecular beacons [Stojanovic et al., 2001; May, Dolan, et al., 2008; May, Lee, et al., 2008]. Although single-stranded DNA sensor technologies, such as DNA microarrays, are widely used in transcriptome profiling and ultra high-throughput technologies are becoming more accessible, molecular beacon probes are highly sensitive and specific bioreceptors and enable the development of fieldable biodetection systems. We have taken advantage of the deoxyribozyme molecular beacon’s computational capabilities and developed a biosensor system that can concurrently detect and, using deoxyribozyme logic gates, computationally classify targets in vitro [May, Lee, et al., 2008]. We have also demonstrated the ability to detect deoxyribozyme activity electrochemically, eliminating the need for fluorescence-based detection and reducing the noise in the empirical data [May et al., SNL SAND 2008].
|2002||PhD, Computer Engineering, North Carolina State University, Raleigh, NC|
|1999||MS, Computer Engineering, North Carolina State University, Raleigh, NC|
|1996||BS, Computer Engineering, North Carolina State University, Raleigh, NC|
Sandra Bennun holds a Bachelor, Masters, and PhD in Chemical Engineering, and has experience with classical chemical engineering process modeling and plant design as well as modeling of biopharmaceutical processes, and plant scheduling. Dr. Bennun’s previous work includes experimental and modeling research to study phase behavior of lipid membranes using Fluoresce microscopy, AFM and Molecular Dynamics. She has worked on cell cultures of CHO cells for production of recombinant proteins and modeling of protein glycosylation. Dr. Bennun’s current research focus is on construction of multiscale theoretical models to understand host pathogen interactions.
UNM/Sandia Laboratory Technician
Ryan received his bachelor’s degree in biochemistry from New Mexico State University. He has seven years experience in biological research in varying fields, including: neurosciences, microarray core facility, and infectious disease. Experimental background and biological techniques include: microbiology, molecular biology, biochemistry, and histology. Ryan is working on experimental models to facilitate the development of multiscale models of host and pathogen response mechanisms.