Computational approaches are fundamental to the development of new biology techniques and understanding. Building on our longstanding strength in the competencies required for massively parallel computation, Sandia’s efforts in computational biology involve developing and applying new algorithms, simulation methods, and software tools for bioinformatics, molecular biophysics and biochemistry, and complex biological systems. We are also applying these capabilities in the context of massively parallel computing architectures. Our computational-biology tools drive experimental work by enabling researchers to describe, model, and predict the behavior of cells, networks, pathways, and molecules.
Signature, our common descriptor for chemical compounds and biological entities (e.g., protein, DNA) serves as a bridge between cheminformatics and bioinformatics, while our particle-based cell simulator, ChemCell©, uses particles to represent proteins or other biomolecules.
Following are some additional examples of Sandia’s computational-biology tools and applications for Sandia’s bioscience projects and cancer research:
Tool: We analyze and mine the large amounts of data produced by high-throughput experiments (e.g., microarray analyses, flow cytometry combinations, and the sequencing of cells tagged with fluorescent fusion proteins).
Applications: Combining high-throughput, super-resolution optical microscopy with other high-throughput methods (e.g., microarray analyses) can provide physical and computational evidence for the way cells transit through their genomic and biochemical state space as a result of environmental changes and metagenomic modifications.
Sandia Relevance: Biofuels engineering.
Cancer Relevance: Basic cell biology, cancer cell biology.
Contact: George Davidson, firstname.lastname@example.org, (505) 844-0667.
Tool: We use existing, enhanced, and new bioinformatics tools to extract, analyze, and visualize high-volume data from high-throughput sequencing experiments. We also analyze protein–protein and protein–chemical interactions by examining the biological sequences and small molecules involved in these interactions.
Applications: Next-generation sequencing data analysis requires fast, accurate, and efficient tools that can handle very short reads (25–500 bp), for sequence alignment, contig assembly, and the identification of sequence variations. These genomic variations can be used to make inferences in a variety of applications, such as disease pathology and novel pathogen detection.
Sandia Relevance: Biodefense, infectious diseases.
Cancer Relevance: Cancer genomics.
Tool: We have developed a particle-based simulator called ChemCell. ChemCell treats proteins and other molecules as individual particles that diffuse within a cellular geometry and react with other molecules. These interactions are governed by rules derived from chemical rate equations input into the simulator. The simulator can track the time and spatial dependence of chemical species within a cell as biochemical networks are executed to respond to stimuli, transduce intracellular signals, or regulate a genomic response.
Applications: (a) ChemCell was used to monitor and characterize the oscillatory response of the NF-κB immune network to stimulation (e.g., by a pathogen binding to a macrophage). These studies were conducted in conjunction with single-cell fluorescent imaging, which also revealed oscillatory behavior. (b) ChemCell also modeled Ca++ ion release from the intracellular endoplasmic reticulum (ER), which stores buffered calcium, through IP3R ion channels, as a function of channel clustering on the ER surface (see figure). This simulation, which was conducted in tandem with continuum-level modeling, suggested that the experimentally observed clustering of channels might be a mechanism for limiting the release of Ca++ ions to prevent the cell’s overstimulation.
Sandia Relevance: Single-cell imaging, immune response, pathogen/cell interactions, cellular network engineering, carbon sequestration.
Cancer Relevance: Understanding cellular response at the single-cell level to genetic (cancerous) perturbations or drugs. This response may be qualitatively different from the average aggregate response of a collection of cells.
Contact: Steve Plimpton, email@example.com, (505) 845-7873.
Tool: We have developed a method for the inverse design of molecules. A key step in our method involves computing the Hilbert basis of a system of linear Diophantine equations. We then use enumeration to solve this system. Specifically, we employ the Fincke-Pohst algorithm, which suggests molecules that have novel structures while remaining similar to the molecules provided in the training sets.
Applications: We have used this method to successfully design small peptide antagonists to the leukocyte functional antigen-1 (LFA-1) and its intercellular adhesion molecule (ICAM-1). We have also used this method to design polymers with high glass-transition temperatures.
Sandia Relevance: Materials design.
Cancer Relevance: Drug design.
Contact: Shawn Martin, firstname.lastname@example.org, (505) 284-3601.
Tool: We perform molecular simulations of small molecules to elucidate differences in how these molecules will interact with proteins or other macromolecular targets. This approach can be used to predict various properties related to inhibition and pharmacokinetics. The tool may also help identify unknown drug targets based on inhibition data. Details are described in a 2008 Journal of Chemical Information and Modeling article (vol. 48, pp 1626–1637).
Applications: Our approach was benchmarked with a study that investigated steroid binding to corticosteroid-binding globulin (see figure). In collaboration with Tudor Oprea and Andrei Leitao, we investigated formylpeptide receptor binding from an assay performed at the University of New Mexico’s (UNM’s) Molecular Libraries Screening Center. We have also investigated the inhibition of NF-κB activation by resveratrol analogues in collaboration with a UNM medicinal chemistry group that includes David Vander Jagt and Lorraine Deck.
Sandia Relevance: Computer-aided molecular design, biodefense, materials design.
Cancer Relevance: Virtual screening, molecular mechanisms of disease.
Contact: Mike Brown, email@example.com, (505) 284-8938.
Tool: We develop, perform, and analyze molecular-dynamics simulations of liposomes. By using coarse-grained models for lipids, we can reach the timescales needed to simulate lipid diffusion and liposome structural transformations, which occur in processes such as membrane fusion.
Applications: Liposomes, which comprise sealed bilayer membranes of largely naturally occurring lipids or their analogs, share many similarities with biological membranes, including the ability to fuse cellular bilayers. Fusing membranes merge and mix their membrane lipids, as well as the aqueous compartments originally delimited by these membranes. When a liposome fuses with a cell membrane, the liposome’s aqueous compartment becomes contiguous with a cell’s cytosol. This fusogenic mechanism results in a complete mix of aqueous compartments and can be used to deliver encapsulated materials into a cell. Thus, liposomes can serve as drug delivery vehicles for therapeutic agents, including (1) drugs that were previously barred from testing because of their inability to cross the membrane barriers separating the cell cytosol from extracytosolic compartments and (2) large-molecule drugs (e.g., proteins, antisense oligonucleotides, ribozymes, and plasmid DNA) generated through recent advances in biotechnology. To realize the potential of liposome-based drug delivery approaches for the treatment of infectious diseases or cancer, encapsulated material must be able to escape the liposomal membrane and cross the cell membrane to reach the cytosol. An ideal way to achieve this goal is by engineering fusogenicity into the liposomal membrane so that a single fusion event can deliver the encapsulated material into the target cell’s cytosol. Our results have been documented in an article in Physical Review Letters.
Sandia Relevance: Infectious disease.
Cancer Relevance: Drug delivery.
Contact: Mark Stevens, firstname.lastname@example.org, (505) 844-1937.
Tool: We construct molecularly detailed models of cell components (e.g., lipid membranes, proteins) interacting with small molecules (e.g., toxins, drugs, imaging agents) and use statistical analysis to investigate how molecular structure controls function. The models are validated through close interactions with experimentalists. The insights gained through molecular simulation can be used to engineer cell components and design therapeutics.
Applications: Molecular simulation can be used to develop a science-based understanding of the structural determinants of channel function and the link between transmembrane potassium channels and innate immune response (see figure). We have coupled our modeling efforts with fluorescence imaging, biology, electrophysiology, and crystallography to gain insights into the mechanism of selective ion binding by potassium ion channels. These discoveries have been documented in the Biophysical Journal, the Journal of Molecular Biology, the Journal of the American Chemical Society, as well as a “new and notable” article in Biophysical Journal.
Sandia Relevance: Infectious disease, public health, water desalination, energy storage, biofuels engineering.
Cancer Relevance: Understanding and ultimately controlling how small molecules—such as drugs, toxins, and imaging agents—interact with cell components.
Contact: Susan Rempe, email@example.com, (505) 845-0253.
Tool: We have developed fast and efficient multivariate-analysis algorithms to extract and quantify spectroscopic signals for several spectroscopic imaging and nonimaging applications. These algorithms uncover all independently varying signals present in the spectroscopic data. When used in conjunction with Sandia’s fluorescence confocal hyperspectral imager, this tool provides an excellent way to investigate unknown biological systems.
Applications: Our multivariate-analysis techniques have been successfully applied to a variety of spectroscopic data sets (e.g., fluorescence, infrared, Raman, and magnetic resonance). Most notably, we have applied these analysis techniques to develop quantitative fluorescence-hyperspectral images for several biological investigations at Sandia (host–pathogen cell signaling and interactions with transmembrane channel proteins, the biofouling of reverse-osmosis membranes, and algal biofuel research). The figure shows a rendered 3-D image of live macrophage cells stained with the nucleic acid Syto 11. The colors correspond to the pure fluorescence spectral components. These slightly shifted Syto 11 emission components are consistent with the stains associated with either the DNA (short wavelength) or RNA (long wavelength) components. By using both the RNA (throughout the cytoplasm and nucleus) and the DNA (nucleus only) components, the cytoplasm and nucleus were separated and imaged with a single stain. These images yielded the nuclear-to-cytoplasm volume ratio, a key factor in determining the kinetics of the cellular-signaling process when cells are exposed to pathogens.
Sandia Relevance: Cell biology, biofuels, biofilm, plant physiology, neuroscience.
Cancer Relevance: Discovering the unique spectroscopic signatures associated with cancerous cells and tissue. Our algorithms can also be used to discriminate between normal and cancerous cells.
Contact: Howland Jones, firstname.lastname@example.org, (505) 284-1842.