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Modular chemical mechanism predicts spatiotemporal dynamics of initiation in the complex network of hemostasis.

ABSTRACT: This article demonstrates that a simple chemical model system, built by using a modular approach, may be used to predict the spatiotemporal dynamics of initiation of blood clotting in the complex network of hemostasis. Microfluidics was used to create in vitro environments that expose both the complex network and the model system to surfaces patterned with patches presenting clotting stimuli. Both systems displayed a threshold response, with clotting initiating only on isolated patches larger than a threshold size. The magnitude of the threshold patch size for both systems was described by the Damköhler number, measuring competition of reaction and diffusion. Reaction produces activators at the patch, and diffusion removes activators from the patch. The chemical model made additional predictions that were validated experimentally with human blood plasma. These experiments show that blood can be exposed to significant amounts of clot-inducing stimuli, such as tissue factor, without initiating clotting. Overall, these results demonstrate that such chemical model systems, implemented with microfluidics, may be used to predict spatiotemporal dynamics of complex biochemical networks.

SUBMITTER: Kastrup CJ

PROVIDER: S-EPMC1635074 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature

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Modular chemical mechanism predicts spatiotemporal dynamics of initiation in the complex network of hemostasis.

Kastrup Christian J CJ Runyon Matthew K MK Shen Feng F Ismagilov Rustem F RF

Proceedings of the National Academy of Sciences of the United States of America 20061016 43

This article demonstrates that a simple chemical model system, built by using a modular approach, may be used to predict the spatiotemporal dynamics of initiation of blood clotting in the complex network of hemostasis. Microfluidics was used to create in vitro environments that expose both the complex network and the model system to surfaces patterned with patches presenting clotting stimuli. Both systems displayed a threshold response, with clotting initiating only on isolated patches larger th ...[more]

PMID: 17043240

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Project description:MotivationPrecision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction.ResultsWe study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies.Availability and implementationCode and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Modular chemical mechanism predicts spatiotemporal dynamics of initiation in the complex network of hemostasis.

Publications

Modular chemical mechanism predicts spatiotemporal dynamics of initiation in the complex network of hemostasis.

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