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If a coronary blood vessel is occluded and the neighboring cardiomyocytes deprived of oxygen, subsequent reperfusion of the ischemic tissue can lead to oxidative damage due to excessive generation of reactive oxygen species. Car-diomyocytes and their mitochondria are the main energy producers and consumers of the heart, and their metabolic changes during ischemia seem to be a key driver of reperfusion injury. Here, we hypothesized that tracking changes in cardiomyocyte metabolism, such as oxygen and ATP concentrations, would help in identifying points of metabolic failure during ischemia and reperfusion. To track some of these changes continuously from the onset of ischemia through reperfusion, we developed a system of differential equations representing the chemical reactions involved in the production and consumption of 67 molecular species. This model was validated and used to identify conditions present during periods of critical transition in ischemia and reperfusion that could lead to oxidative damage. These simulations identified a range of oxygen concentrations that lead to reverse mitochondrial electron transport at complex I of the respiratory chain and a spike in mitochondrial membrane potential, which are key suspects in the generation of reactive oxygen species at the onset of reperfusion. Our model predicts that a short initial reperfusion treatment with reduced oxygen content (5% of physiological levels) could reduce the cellular damage from both of these mechanisms. This model should serve as an open-source platform to test ideas for treatment of the ischemia reperfusion process by following the temporal evolution of molecular concentrations in the cardiomyocyte., Journal of Biological Chemistry, 298 (5), ISSN:0021-9258, ISSN:1083-351X

Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can prevent death by restoring nutrient flow, ischemia/reperfusion injury can cause significant heart damage. The mechanisms that drive ischemia/reperfusion injury are not well understood; currently, few methods can predict the state of the cardiac muscle cell and its metabolic conditions during ischemia. Here, we explored the energetic sustainability of cardiomyocytes, using a model for cellular metabolism to predict the levels of ATP following hypoxia. We modeled glycolytic metabolism with a system of coupled ordinary differential equations describing the individual metabolic reactions within the cardiomyocyte over time. Reduced oxygen levels and ATP consumption rates were simulated to characterize metabolite responses to ischemia. By tracking biochemical species within the cell, our model enables prediction of the cell's condition up to the moment of reperfusion. The simulations revealed a distinct transition between energetically sustainable and unsustainable ATP concentrations for various energetic demands. Our model illustrates how even low oxygen concentrations allow the cell to perform essential functions. We found that the oxygen level required for a sustainable level of ATP increases roughly linearly with the ATP consumption rate. An extracellular O2 concentration of ∼0.007 mm could supply basic energy needs in non-beating cardiomyocytes, suggesting that increased collateral circulation may provide an important source of oxygen to sustain the cardiomyocyte during extended ischemia. Our model provides a time-dependent framework for studying various intervention strategies to change the outcome of reperfusion.

The availability of large-scale network data has given rise to the opportunity to investigate higher level organization of these networks using graph theoretic analysis. In this paper, we demonstrate a novel network decomposition tool called FacetsViewer in order to make sense of the deluge of network data. In contrast to traditional graph clustering techniques, it finds not just a single decomposition of the network, but a multi-faceted atlas of semantically meaningful decompositions that portray alternative perspectives of the landscape of the underlying network. Each facet in the atlas represents a distinct interpretation of how the network can be meaningfully decomposed and organized. To this end, FacetsViewer maximizes interpretative value of the atlas by optimizing inter-facet semantic and structural orthogonality. Specifically, we demonstrate various features of FacetsViewer and its superior ability to generate and visualize multi-faceted atlas of complex networks.

Target characterization of a biological network identifies characteristics that distinguish targets (nodes that can serve as molecular targets of drugs) from other nodes. In this demonstration, we present TENET (Target charactErization using NEtwork Topology), a software that facilitates topological features-based characterization of known targets in signaling networks modelling dynamic interactions within biological systems. TENET is based on a support vector machine (SVM)-based approach and generates a characterization model. These models specify topological features that can discriminate known targets and how these features are combined to quantify the likelihood of a node being a target. Hence, TENET can be used for prioritizing targets and for identifying novel candidate targets that share similar characteristics with known targets. The interactive user interface that TENET provides facilitates users' study and understanding of topological characteristics of targets in signaling networks.

The endothelial glycocalyx, a glycosaminoglycan layer located on the apical surface of vascular endothelial cells, has been shown to be important for several endothelial functions. Previous studies have documented that the glycocalyx is highly abundant in the mouse common carotid region, where the endothelium is exposed to laminar shear stress, and it is resistant to atherosclerosis. In contrast, the glycocalyx is scarce or absent in the mouse internal carotid sinus region, an area exposed to nonlaminar shear stress and highly susceptible to atherosclerosis. On the basis of these observations, we hypothesized that the expression of components of the endothelial glycocalyx is differentially regulated by distinct hemodynamic environments. To test this hypothesis, human endothelial cells were exposed to shear stress waveforms characteristic of atherosclerosis-resistant or atherosclerosis-susceptible regions of the human carotid, and the expression of several components of the glycocalyx was assessed. These experiments revealed that expression of several components of the endothelial glycocalyx is differentially regulated by distinct shear stress waveforms. Interestingly, we found that heparan sulfate expression is increased and evenly distributed on the apical surface of endothelial cells exposed to the atheroprotective waveform and is irregularly present in cells exposed to the atheroprone waveform. Furthermore, expression of a heparan sulfate proteoglycan, syndecan-1, is also differentially regulated by the two waveforms, and its suppression mutes the atheroprotective flow-induced cell surface expression of heparan sulfate. Collectively, these data link distinct hemodynamic environments to the differential expression of critical components of the endothelial glycocalyx.

In January of 2011, the Biomedical Engineering Society (BMES) and the Society for Physical Regulation in Biology and Medicine (SPRBM) held its inaugural Cellular and Molecular Bioengineering (CMBE) conference. The CMBE conference assembled worldwide leaders in the field of CMBE and held a very successful Round Table discussion among leaders. One of the action items was to collectively construct a white paper regarding the future of CMBE. Thus, the goal of this report is to emphasize the impact of CMBE as an emerging field, identify critical gaps in research that may be answered by the expertise of CMBE, and provide perspectives on enabling CMBE to address challenges in improving human health. Our goal is to provide constructive guidelines in shaping the future of CMBE.

Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/

In many microfluidic devices, fluid flow is generated using micropumps like peristaltic micropumps. However the hydrodynamic performance of peristaltic micropumps has not been fully understood and furthermore the effect of dynamic interaction of pumping membrane and fluid flow has not been studied yet. To fill this gap, we studied the hydrodynamic performance of a peristaltic micropump using a numerical model incorporating the fluid-solid interactions. The model consisted of 3 layers; the top layer was the flow channel of 10 μm high, the middle layer was the 5~30 μm thick pumping membrane and the bottom layer was the 3 or 5 pumping chambers. By applying a pumping sequence at a frequency between 16~166 Hz, we calculated flow rate for at least 4 cycles and used the fourth or fifth cycle to evaluate the flow rate per a cycle. We found that the numerical model closely replicated the frequency vs. flow rate relationship of a peristaltic micropump as shown earlier in experimental models. We further found that the flow rate of a peristaltic micropump could be improved by increasing the number of pumping chambers or the thickness of pumping membrane.

Endothelial cells are known to respond to flow onset by increasing actin turnover rate. Current models assume that an increase in the actin turnover rate should result in a rise in cell crawling speed. Here we report that confluent endothelial monolayer shows an unexpected behavior: cell crawling speed decreases by approximately 40% within the first 30 min of flow onset. A drop in crawling speed has not been observed in either subconfluent endothelial cells or in VE-cadherin-deficient cells. We found that flow onset caused an increase in the number of VE-cadherin-GFP molecules in the junctions and elicited changes in the cytoskeleton-associated fractions of alpha, beta -catenins and VE-cadherin. Flow application also increased the strength of interactions of endothelial cells with surfaces coated with recombinant VE-cadherin. These observations suggest that endothelial cell junctional proteins respond to flow transiently by increasing the strength of intercellular attachments early after flow onset and support the view on the active role of intercellular adhesions in mechanotransduction.

Motivation: Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features. Results: Tenet first computes a set of topological features and then leverages a support vector machine-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target. We empirically study the performance of Tenet from a wide variety of aspects, using several signalling networks from BioModels with real-world curated outcomes. Results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches. Availability and implementation: Our software is available freely for non-commercial purposes from: https://sites.google.com/site/cosbyntu/softwares/tenet Contact: hechua@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Given the growth of large-scale protein–protein interaction (PPI) networks obtained across multiple species and conditions, network alignment is now an important research problem. Network alignment performs comparative analysis across multiple PPI networks to understand their connections and relationships. However, PPI data in high-throughput experiments still suffer from significant false-positive and false-negatives rates. Consequently, high-confidence network alignment across entire PPI networks is not possible. At best, local network alignment attempts to alleviate this problem by completely ignoring low-confidence mappings; global network alignment, on the other hand, pairs all proteins regardless. To this end, we propose an alternative strategy: instead of full alignment across the entire network or completely ignoring low-confidence regions, we aim to perform highly specific protein-to-protein alignments where data confidence is high, and fall back on broader functional region-to-region alignment where detailed protein–protein alignment cannot be ascertained. The basic idea is to provide an alignment of multiple granularities to allow biological predictions at varying specificity. Results: DualAligner performs dual network alignment , in which both region-to-region alignment, where whole subgraph of one network is aligned to subgraph of another, and protein-to-protein alignment, where individual proteins in networks are aligned to one another, are performed to achieve higher accuracy network alignments. Dual network alignment is achieved in DualAligner via background information provided by a combination of Gene Ontology annotation information and protein interaction network data. We tested DualAligner on the global networks from IntAct and demonstrated the superiority of our approach compared with state-of-the-art network alignment methods. We studied the effects of parameters in DualAligner in controlling the quality of the alignment. We also performed a case study that illustrates the utility of our approach. Availability and implementation: http://www.cais.ntu.edu.sg/∼assourav/DualAligner/ Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at Bioinformatics online.

The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility.

Cytoplasmic actin distributes between monomeric and filamentous phases in cells. As cells crawl, actin polymerizes near the plasma membrane of expanding peripheral cytoplasm and depolymerizes elsewhere. Thus, the finite actin filament lifetime, the diffusivity of actin monomer, and the distribution of actin between the polymer and monomer phases are key parameters in cell motility. The dynamics of cellular actin can be determined by following the evolution of fluorescence in the techniques of photoactivated fluorescence (PAF) or fluorescence recovery after photobleaching (FRAP) of microinjected actin derivatives. A mathematical model is discussed that measures monomer diffusion coefficients, filament turnover rates, and the fraction of actin polymerized from measurements of the evolution of fluorescence from a photoactivated band [Tardy et al. (1995) Biophys. J., 69:1674–1682; McGrath et al. (1998) Biophys. J., in press]. Applying this model to subconfluent endothelial cells shows that ∼40% of the actin is polymer and that these filaments turn over on average every 6 minutes. This report dicusses how PAF and FRAP can be combined with more traditional biochemistry to probe actin cytoskeleton remodeling in endothelial cells. Microsc. Res. Tech. 43:385–394 © 1998 Wiley-Liss, Inc.

Ultrasound imaging is currently used as a primary diagnostic tool in cardiology, abdominal disorders, pulmonary medicine, trauma, and obstetrics. Because of its relatively low capital and operating costs as well as its growth potential, it represents one of the major diagnostic modalities of future health care. However, the use of ultrasonography as a mobile and powerful modality is controlled by the availability of a highly skilled technician to acquire the images and an experienced physician to interpret them. This paper discusses the technology required to increase the availability of a diagnosing physician by employing telerobotics. With this technology, the physician can guide the motion of the transducer by the technician from a remote location. Thus, the physician controls the examination and renders the diagnosis. It is shown that communication lines at 1.5 Mbits/s (T-1 speed) can, with appropriate compression, support both real-time viewing of the ultrasound images and telerobotic manipulation of the transducer. The incremental costs of telediagnosis for an examination are estimated to be a small fraction of the base charges and significantly less than the expense of bringing a physician to a remote location or transporting a patient to a regional medical center. Telediagnosis can, in addition, provide benefits from immediate interpretation and consultation that cannot be duplicated using store-and-forward scenarios.

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 Program

Background 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. Methods 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. Results 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.

Background The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Results We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. Conclusion By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

Biological network analysis often aims at the target identification problem, which is to predict which molecule to inhibit (or activate) for a disease treatment to achieve optimum efficacy and safety. A related goal, arising from the increasing availability of high-throughput screening (HTS), is to suggest many molecules as potential targets. The target prioritization problem is to predict a subset of molecules in a given disease-associated network which is likely to include successful drug targets. Sensitivity analysis prioritizes targets in a dynamic network model according to principled criteria, but fails to penalize off-target effects, and does not scale for large networks. In this demonstration, we present PANI(Putative TArget Nodes PrIoritization), a novel interactive system that addresses these limitations. It prunes and ranks the possible target nodes by exploiting concentration-time profiles and network structure (topological) information and visually display them in the context of the signaling network. Through the interactive user interface, we demonstrate various innovative features of PANI that enhance users' understanding of the prioritized nodes.

Recent functional genomics research has yielded a large in silico gene regulatory network model (622 nodes) for endomesoderm development of sea urchin, a model organism for embryonic development. The size of this network makes it challenging to determine which genes are most responsible for a given biological effect. In this paper, we explore feasibility and accuracy of existing in silico techniques for identifying key genes that regulate Endo16, a widely-accepted gastrulation marker. We apply target prioritization tools (sensitivity analysis and PANI) to the endomesoderm network to identify key regulators of Endo16 and validate the results by comparing against a set of benchmark Endo16 regulators collated from literature survey. Our study reveals that global sensitivity analysis methods are prohibitively expensive and inappropriate for large networks. We show that PANI efficiently produces superior prioritization results compared to both random prioritization and local sensitivity analysis (LSA) techniques. Specifically, the area under the ROC curve was 0.625, ~0.5, and 0.549 for PANI, random prioritization, and LSA, respectively. Our study reveals that certain unique characteristics of the endomesoderm network affect the performance of target prioritization techniques. In addition to identifying many known regulators of Endo16, PANI also discovered additional regulators (e.g., Snail) that did not appear initially in the benchmark regulators set.

Transforming growth factor-β1 (TGF-β1) is a potent regulator of extracellular matrix production, wound healing, differentiation, and immune response, and is implicated in the progression of fibrotic diseases and cancer. Extracellular activation of TGF-β1 from its latent form provides spatiotemporal control over TGF-β1 signaling, but the current understanding of TGF-β1 activation does not emphasize cross talk between activators. Plasmin (PLS) and thrombospondin-1 (TSP1) have been studied individually as activators of TGF-β1, and in this work we used a systems-level approach with mathematical modeling and in vitro experiments to study the interplay between PLS and TSP1 in TGF-β1 activation. Simulations and steady-state analysis predicted a switch-like bistable transition between two levels of active TGF-β1, with an inverse correlation between PLS and TSP1. In particular, the model predicted that increasing PLS breaks a TSP1-TGF-β1 positive feedback loop and causes an unexpected net decrease in TGF-β1 activation. To test these predictions in vitro, we treated rat hepatocytes and hepatic stellate cells with PLS, which caused proteolytic cleavage of TSP1 and decreased activation of TGF-β1. The TGF-β1 activation levels showed a cooperative dose response, and a test of hysteresis in the cocultured cells validated that TGF-β1 activation is bistable. We conclude that switch-like behavior arises from natural competition between two distinct modes of TGF-β1 activation: a TSP1-mediated mode of high activation and a PLS-mediated mode of low activation. This switch suggests an explanation for the unexpected effects of the plasminogen activation system on TGF-β1 in fibrotic diseases in vivo, as well as novel prognostic and therapeutic approaches for diseases with TGF-β dysregulation.

Plasmin (PLS) and urokinase-type plasminogen activator (UPA) are ubiquitous proteases that regulate the extracellular environment. Although they are secreted in inactive forms, they can activate each other through proteolytic cleavage. This mutual interplay creates the potential for complex dynamics, which we investigated using mathematical modeling and in vitro experiments. We constructed ordinary differential equations to model the conversion of precursor plasminogen into active PLS, and precursor urokinase (scUPA) into active urokinase (tcUPA). Although neither PLS nor UPA exhibits allosteric cooperativity, modeling showed that cooperativity occurred at the system level because of substrate competition. Computational simulations and bifurcation analysis predicted that the system would be bistable over a range of parameters for cooperativity and positive feedback. Cell-free experiments with recombinant proteins tested key predictions of the model. PLS activation in response to scUPA stimulus was found to be cooperative in vitro. Finally, bistability was demonstrated in vitro by the presence of two significantly different steady-state levels of PLS activation for the same levels of stimulus. We conclude that ultrasensitive, bistable activation of UPA-PLS is possible in the presence of substrate competition. An ultrasensitive threshold for activation of PLS and UPA would have ramifications for normal and disease processes, including angiogenesis, metastasis, wound healing, and fibrosis.

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., EchoMail, Inc., International Center for Integrative Systems

For acute, chronic, or hereditary diseases of the liver, cell transplantation therapies can stimulate liver regeneration or serve as a bridge until liver transplantation can be performed. Recently, fetal hepatocytes, stem cells, liver progenitor cells, or other primitive and proliferative cell types have been employed for cell transplantation therapies, in an effort to improve the survival, proliferation, and engraftment of the transplanted cells. Reviewing earlier studies, which achieved success by transplanting mature hepatocytes, we propose that there is a switch-like regulation of liver regeneration that changes state according to a stimulus threshold of extracellular influences such as cytokines, matrices and neighboring cells. Important determinants of a successful clinical outcome include sufficient quantities and functional levels of the transplanted cells (even for short periods to alter the environment), rather than just engraftment levels or survival durations of the exogenously transplanted cells. The relative importance of these determining factors will impact future choices of cell sources, delivery vehicles, and sites of cell transplantation to stimulate liver regeneration for patients with severe liver diseases.

The Physiome Project comprises a worldwide effort to provide a computational framework for understanding human and other eukaryotic physiology. The aim is to develop integrative models at all levels of biological organization from genes to the whole organism. This is achieved via gene regulatory networks, protein pathways, integrative cell function, and tissue and whole organ structure/function relations. A key hallmark of the Physiome is that it covers many physical scales of description, from molecule-molecule interactions to whole cell behaviour to whole organ descriptions. This talk will stress the computational and semantic layers of the Physiome, the mathematical and logical “glue” that allows the various physiological scales to communicate and work with one another. The first knowledge domain is Ontologies. Ontology is a specific expression of known facts about the real world. Work on ontologies is being undertaken in order to organize biological data and knowledge at the different levels of the biological continuum. An additional and important component of this work is to facilitate easy and effective access to a range of databases, and to facilitate automated reasoning that can simultaneously extract information from many databases.

Flow-induced mechanotransduction in vascular endothelial cells has been studied over the years with a major focus on putative connections between disturbed flow and atherosclerosis. Recent studies have brought in a new perspective that the glycocalyx, a structure decorating the luminal surface of vascular endothelium, may play an important role in the mechanotransduction. This study reports that modifying the amount of the glycocalyx affects both short-term and long-term shear responses significantly. It is well established that after 24 h of laminar flow, endothelial cells align in the direction of flow and their proliferation is suppressed. We report here that by removing the glycocalyx by using the specific enzyme heparinase III, endothelial cells no longer align under flow after 24 h and they proliferate as if there were no flow present. In addition, confluent endothelial cells respond rapidly to flow by decreasing their migration speed by 40% and increasing the amount of vascular endothelial cadherin in the cell-cell junctions. These responses are not observed in the cells treated with heparinase III. Heparan sulfate proteoglycans (a major component of the glycocalyx) redistribute after 24 h of flow application from a uniform surface profile to a distinct peripheral pattern with most molecules detected above cell-cell junctions. We conclude that the presence of the glycocalyx is necessary for the endothelial cells to respond to fluid shear, and the glycocalyx itself is modulated by the flow. The redistribution of the glycocalyx also appears to serve as a cell-adaptive mechanism by reducing the shear gradients that the cell surface experiences.

In this paper, we address the problem of efficient processing of XQueries in single-user relational environment where the queries are formulated using a user-friendly GUI. We take a novel and non-traditional approach to improving query performance by prefetching data during the formulation of a query. The latency offered by GUI-based query formulation is utilized to prefetch portions of the query results. To realize this, we present an algorithm for prefetching based on data synopses statistics and GUI actions during visual query formulation. Experimental evaluation indicates that prefetching is viable as the combined time taken by all the prefetching operations is not significantly more than normal query execution time. Our experiments in the context of biological data show that prefetching improves the query response time by 7-96% with a greater improvement for larger data sets. Also, we show the impact of errors committed by users during query formulation on the query performance.

Fluid shear stress stimulation induces endothelial cells to elongate and align in the direction of applied flow. Using the complementary techniques of photoactivation of fluorescence and fluorescence recovery after photobleaching, we have characterized endothelial actin cytoskeleton dynamics during the alignment process in response to steady laminar fluid flow and have correlated these results to motility. Alignment requires 24 h of exposure to fluid flow, but the cells respond within minutes to flow and diminish their movement by 50%. Although movement slows, the actin filament turnover rate increases threefold and the percentage of total actin in the polymerized state decreases by 34%, accelerating actin filament remodeling in individual cells within a confluent endothelial monolayer subjected to flow to levels used by dispersed nonconfluent cells under static conditions for rapid movement. Temporally, the rapid decrease in filamentous actin shortly after flow stimulation is preceded by an increase in actin filament turnover, revealing that the earliest phase of the actin cytoskeletal response to shear stress is net cytoskeletal depolymerization. However, unlike static cells, in which cell motility correlates positively with the rate of filament turnover and negatively with the amount polymerized actin, the decoupling of enhanced motility from enhanced actin dynamics after shear stress stimulation supports the notion that actin remodeling under these conditions favors cytoskeletal remodeling for shape change over locomotion. Hours later, motility returned to pre-shear stress levels but actin remodeling remained highly dynamic in many cells after alignment, suggesting continual cell shape optimization. We conclude that shear stress initiates a cytoplasmic actin-remodeling response that is used for endothelial cell shape change instead of bulk cell translocation.

Fibrotic diseases are often caused by chronic injuries, and are characterized by pathological over-activation of certain stages of wound healing, particularly extracellular matrix secretion. Matrix secretion is driven by the fibrotic cytokine TGF-β1 (transforming growth factorβ1), which is secreted by cells as a latent complex, and must be activated extracellularly for its biological function. Plasmin is a direct proteolytic activator of TGF-β1, but puzzling results in fibrotic animal have shown that the plasmin pathway causes decreased TGF-β1.We used computational modelling and experimental tests to understand the activation of TGF-β1, by plasmin and by the matricellular glycoprotein thrombospondin1 (TSP1). Ordinary differential equations were used to model the dynamics of activation and feedback. TGF-β1 and TSP1 create a fibrogenic feedback loop, because TGF-β1 causes transcriptional up-regulation of TSP1, while TSP1 activates TGF-β1 through conformational change. Simulations showed that, although plasmin is a direct activator of TGF-β1, increasing doses of plasmin caused a decrease in TGF-β1 activation by cleaving TSP1 and antagonizing the TSP1-TGF-β1 positive feedback loop. Furthermore, the system was capable of bistability, switching between high and low levels of active TGF-β1. Key predictions of the mathematical model were validated in vitro with co-culture models of liver fibrosis, which showed inverse correlation between active TGF-β1 and plasmin. Bistability was demonstrated by showing hysteresis, where the same system achieved two different steady states, after being given the same two input stimuli but in opposite orders.Our model explains how plasmin can decrease or increase TGF-β1 activation, depending on the presence of TSP1. The bistable transition between plasmin-mediated and TSP-mediated TGF-β1 activation pathways may be relevant to the transition between inflammatory and fibrogenic phases of wound healing, suggesting novel mechanisms for addressing TGFβ1-related diseases.

The paper discusses the development of electronic medical record systems (EMRs). Current EMRs are based on earlier PACS systems as well as hospital information systems (HIS) and radiological information systems (RIS). EMR systems of the type described in the paper must be capable of handling a range of information including admission and referral data, images and physiological waveforms. When used in the form of clinical communications systems they must have the ability to store and retrieve data using different types of databases. Design criteria for EMR systems are discussed, including multimedia presentation. The second part of the paper describes the development of a specific EMR system cabled the PIF or patient information folder. The PIF system uses Web technology as a front-end which makes it extremely flexible in terms of its operation. In addition to its display features, the PIF also incorporates the use of knowledge filters; this is a technique which allows the EMR system to automatically deliver the appropriate information to a particular type of health care professional. Another important feature of the system is the use of advanced SQL database technology called Datablades-this is designed to be directly compatible with Informix universal server technology.

Summary: Alzheimer disease (AD) is a neurodegenerative condition leading to progressive, irreversible loss of cognitive and behavioral function. Despite considerable investments in neuroscience research, only four drugs, all cholinesterase inhibitors, have been approved for the symptomatic management of AD in the United States. Although basically safe and modestly effective, these drugs are far from ideal, being neither universally efficacious nor disease modifying. AD exacts a considerable toll in direct medical costs, quality of life, and caregiver burden for persons and society. In addition to the obvious clinical benefit, therapeutic agents for AD and related dementias represent a considerable market opportunity for the pharmaceutical and biotechnology industries. There are currently 8–10 million AD sufferers in the seven major pharmaceutical markets. The market will grow rapidly in coming decades, as the developed world experiences an enormous increase in its elderly population. Given the great need for new therapeutic agents to manage and prevent AD, the Institute for the Study of Aging and the Fidelity Foundation organized a workshop, “Barriers to the Discovery and Development of Drugs for Alzheimer's Disease,” to examine ways to expedite drug discovery and development. The identified barriers and potential solutions will be discussed here and in the accompanying articles in more detail.