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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 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/

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.

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

Given a signaling network, the target combination identification problem aims to predict efficacious and safe target combinations for treatment of a disease. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects which directly affect the solution quality. In this paper, we present Steroid, a novel method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is generally associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. Our empirical study on the cancer-related mapk-pi3k network demonstrates the superiority of Steroid in comparison to mcsa-based approaches. Specifically, Steroid is an order of magnitude faster and yet yields biologically relevant synergistic target combinations with significantly lower off-target effects.

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

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.

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.

Cycling of actin subunits between monomeric and filamentous phases is essential for cell crawling behavior. We investigated actin filament turnover rates, length, number, barbed end exposure, and binding of cofilin in bovine arterial endothelial cells moving at different speeds depending on their position in a confluent monolayer. Fast-translocating cells near the wound edge have short filament lifetimes compared with turnover values that proportionately increase in slower moving cells situated at increasing distances from the wound border. Contrasted with slow cells exhibiting slow actin filament turnover speeds, fast cells have less polymerized actin, shorter actin filaments, more free barbed ends, and less actin-associated cofilin. Cultured primary fibroblasts manifest identical relationships between speed and actin turnover as the endothelial cells, and fast fibroblasts expressing gelsolin have higher actin turnover rates than slow fibroblasts that lack this actin-severing protein. These results implicate actin filament severing as an important control mechanism for actin cycling in cells.

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.

Phonoangiography, the quantitative analysis of sounds produced by blood flow, has been used to investigate local fluid motion in arteries narrowed by atherosclerosis. The sound spectra measured in vivo were found to be identical to those obtained in laboratory investigations of turbulent pipe flow. A theoretical model is proposed which: ( a ) quantitatively relates the in vivo and in vitro data, and ( b ) allows estimation of the clinically important parameters of arterial diameter, flow velocity, local turbulence intensity, and wall pressure fluctuations. The results of calculations using data from human subjects agree with known arterial diameters and blood flow velocities.

A correlation is made of heat-transfer data for circular cylinders of high thermal conductivity in the Reynolds number range 2 × 10-2 0.2. The Nusselt number is found to vary monotonically from the continuum heat-transfer law Nu ∼ Re 1 2 at high Reynolds numbers to Nu ∼ Re in free-molecule flow. The normalized recovery temperature is found to be a function only of the free-stream Knudsen number for Mach numbers greater than 0.6, and a method of estimating the recovery temperature for all Mach numbers is proposed. Empirical formulas are presented for the Nusselt number, recovery factor, and slope of the Nu-Re correlation. The results indicate a free-molecule flow energy accommodation coefficient near unity.

The local hemodynamic shear stress waveforms present in an artery dictate the endothelial cell phenotype. The observed decrease of the apical glycocalyx layer on the endothelium in atheroprone regions of the circulation suggests that the glycocalyx may have a central role in determining atherosclerotic plaque formation. However, the kinetics for the cells’ ability to adapt its glycocalyx to the environment have not been quantitatively resolved. Here we report that the heparan sulfate component of the glycocalyx of HUVECs increases by 1.4-fold following the onset of high shear stress, compared to static cultured cells, with a time constant of 19 h. Cell morphology experiments show that 12 h are required for the cells to elongate, but only after 36 h have the cells reached maximal alignment to the flow vector. Our findings demonstrate that following enzymatic degradation, heparan sulfate is restored to the cell surface within 12 h under flow whereas the time required is 20 h under static conditions. We also propose a model describing the contribution of endocytosis and exocytosis to apical heparan sulfate expression. The change in HS regrowth kinetics from static to high-shear EC phenotype implies a differential in the rate of endocytic and exocytic membrane turnover., National Heart, Lung, and Blood Institute (Grant HL090856-01), Singapore-MIT Alliance