A Distributed Computational Architecture for Integrating Multiple Biomolecular Pathways

Biomolecular pathways are building blocks of cellular biochemical function. Computational biology is in rapid transition from diagrammatic representation of pathways to quantitative and predictive mathematical models, which span time-scales, knowledge domains and spatial-scales. This transition is being accelerated by high-throughput experimentation which isolates reactions and their corresponding rate constants. A grand challenge of systems biology is to model the whole cell by integrating these emerging quantitative biomolecular pathway models. Current integration approaches do not scale. A new parallel and distributed computational architecture, CytoSolve, directly addresses this scalability issue. Results are presented in the solution of a concrete biological model: the Epidermal Growth Factor Receptor (EGFR) pathway model published by Kholodenko. The EGFR pathway is selected since known solutions exist for this problem, enabling direct confirmation of the CytoSolve approach. Results from this effort demonstrate that CytoSolve provides a core platform for addressing a grand challenge of systems biology to model the whole cell by integrating multiple biomolecular pathway models.Singapore-MIT Alliance (SMA

Lasing Efficiency and Photochemical Stability of IR Laser

The lasing efficiencies and photochemical stabilities of laser dyes useful in the 710-1080-nm spectral region have been investigated using a Q-switched ruby laser pumping source. The measured bleaching rates P defined as the probability of irreversible decomposition of a dye molecule per absorbed photon, varied from <= 1 exp-5 to 3 exp-4 for the different dye-solvent combinations investigated. Broad-band lasing efficiencies (the ratios of dye laser output to ruby radiation input) ranged from 4 to 43 percent. Shifts of wavelength tuning range with variations in solvent, dye concentration, and dye laser cavity geometry are reported

Hot-wire Measurements in Low Reynolds Number Hypersonic Flows

Measurements were made of the heat loss and recovery temperature of a fine hot-wire at a nominal Mach number of 5.8. Data were obtained over an eight-fold range of Reynolds numbers in the transitional regime between continuum and free-molecule flow. At high Reynolds numbers, the heat transfer data agree well with the results of Laufer and McClellan, which were obtained at lower Mach numbers. At lower Reynolds numbers, the results indicate a monotonic transition between continuum and free molecule heat transfer laws. The slope of the heat transfer correlation also appears to vary monotonically, with Nu=√Re at high Reynolds numbers and Nu ~ Re for Re < < 1. Data on the wire recovery temperature (corresponding to zero net heat transfer) were obtained for free-stream Knudsen numbers between 0.4 and 3.0. Comparison with previous data suggests that for Mach numbers greater than about two the normalized variation of recovery temperature in the transitional regime is a unique function of the free-stream Knudsen number. The steady-state hot-wire may be used to obtain two thermodynamic measurements: the rate of heat transfer from the wire and the wire recovery temperature. An illustrative experiment was performed in the wake of a transverse cylinder, using both hot-wire and pressure instruments in a redundant system of measurements. It was shown that good accuracy may be obtained with a hot-wire even when the Reynolds number based on wire diameter is small

OREMP: Ontology Reasoning Engine for Molecular Pathways

The information about molecular processes is shared continuously in the form of runnable pathway collections, and biomedical ontologies provide a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Here we propose a strategy that integrates these resources by means of extended ontologies built on top of a common meta-format. Information sharing, integration and discovery are the primary features provided by the system; additionally, two current field applications of the system are reported

In Silico Modeling of Shear-Stress-Induced Nitric Oxide Production in Endothelial Cells through Systems Biology

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.National Institutes of Health (U.S.) (Grant R01HL090856)Singapore-MIT Alliance Computational and Systems Biology Progra

CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer

Summary: Thousands of cancer exomes are currently being sequenced, yielding millions of non-synonymous single nucleotide variants (SNVs) of possible relevance to disease etiology. Here, we provide a software toolkit to prioritize SNVs based on their predicted contribution to tumorigenesis. It includes a database of precomputed, predictive features covering all positions in the annotated human exome and can be used either stand-alone or as part of a larger variant discovery pipeline

OREMPdb: a semantic dictionary of computational pathway models

<p>Abstract</p> <p>Background</p> <p>The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.</p> <p>Methods</p> <p>Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.</p> <p>Results</p> <p>OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared. </p

CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine