当不慎伤了手指的时候，血液会在伤口附近凝成血痂并自动止血。在这个看似简单的凝血和止血过程中，需要80多种不同的化学反应，只要一个反应出了问题，血块就可能形成于不当的位置造成不堪设想的后果。多年来，科学家始终无法完全明白凝血过程，也说不清凝血何时会发生。目前，芝加哥大学的研究人员开发了一种简易的技术，能够预测凝血发生的时间和部位。
该研究的发起人之一、芝加哥大学的化学专业研究生Christian Kastrup说：“真正精彩之处是：我们实际上是通过一个人造的凝血模型来进行研究预测的，仅用三个简单的化学反应就代表了凝血过程中的80多个反应。”这项技术详细地报道在《美国国家科学院院刊》网络版上。
我们血液中的成分——产生于骨髓的圆形血小板，不断地随着血液流动，一直在监视着血管的状况，一旦发生破裂需要修补，血小板就会变得黏稠并结块。同时血液中的凝血酶系统开始一系列的反应，生成长形的、有弹性的蛋白质。威克森林大学科学家们的一项最新研究表明：单就纤维蛋白来说，其纤维是大自然中最具柔韧性的蛋白质，可以拉伸至原长度的三倍。这些蛋白质叫做凝血因子，它们形成一张具有柔韧性的网，拦住血小板，将血小板固定在血管破裂的部位。作为整个一张网，这些蛋白质失去了部分、而不是全部柔韧性。在肉眼看来，这张蛋白质和血小板构成的网就是所结的痂。
在这项新研究中，Kastrup和同事发现止血网中有一种凝血因子叫组织因子，其分布情况决定着血小板是否会凝结。Kastrup告诉《生命科学》的记者说：“将组织因子集中在表面特定的区域内，遇到血液后就会发生凝结。相反，将组织因子分散在试验样品当中，凝血就没有发生。”由于只有当很多组织因子集中在一定的区域内时凝血才会发生，科学家也许能够通过监视病人的组织因子浓度来预测凝血的情况。人们甚至设想有朝一日，这个方法可用来诊断和防止有害的凝血。
当血小板、凝血因子和其他化学物质不能协作的时候，就会导致失血过多，或相反，由于不必要的凝血造成血栓。椐美国国家血友病基金会统计，每年有60万美国人死于非正常凝血。“凝血对于止血和组织再生有益处，但凝血也跟许多疾病有关，如中风和出血，”另一个研究人员说，“心脏或大脑等部位的凝血会造成致命的后果。假如能预测何时、何处将会产生凝血，可防止人们患上凝血不良带来的各种疾病。” （胡德良编译自美国《生命科学网》）
部分英文原文：
Published online before print October 16, 2006, 10.1073/pnas.0605560103
PNAS | October 24, 2006 | vol. 103 | no. 43 | 15747-15752
Modular chemical mechanism predicts spatiotemporal dynamics of initiation in the complex network of hemostasis
Christian J. Kastrup, Matthew K. Runyon, Feng Shen, and Rustem F. Ismagilov*
Department of Chemistry and Institute for Biophysical Dynamics, University of Chicago, 929 West 57th Street, Chicago, IL 60637
Edited by George M. Whitesides, Harvard University, Cambridge, MA, and approved August 30, 2006 (received for review July 3, 2006)
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.
complexity | microfluidics | networks | tissue factor | nonlinear
Complex networks of interacting reactions are responsible for the function and self-regulation of biological systems and are the focus of a substantial research effort (1–8). The spatiotemporal dynamics of such networks (2, 4) is especially challenging and interesting to understand, and to reproduce in synthetic model systems (2, 3, 5, 9–11). Simplified physical or chemical model systems are attractive for understanding biological complexity because these models can be made simple to probe, analyze, and understand. These models, even if correct, may be met with skepticism that "there is no model simpler than life itself" (12), unless predictions can be made with the model system and can be tested and validated with the complex network. This testing is often difficult for experimental models of spatiotemporal dynamics because both the model system and the complex network must be perturbed and observed in space and time in a controlled fashion.
In this article, we use soft lithography and microfluidics (13) to control and compare the spatiotemporal dynamics of two networks: the complex network of hemostasis and a simple chemical model system that describes the network. Our main question is whether the qualitative dynamics of the complex network may be predicted by observing the dynamics of this "analogue" model system, and whether semiquantitative scaling predictions can be made to relate the dynamics of the two systems. To control clotting, the network of 80 reactions (14) of hemostasis must be robust: it must initiate blood clotting at a patch of substantial vascular injury but not at patches of smaller damage that are believed to be present throughout the vascular system (15, 16). Although most of the individual reactions in hemostasis have been characterized, its overall spatiotemporal dynamics remains less understood (17) because the system is complex. The function of hemostasis has been postulated to depend on the delicate balance of production, consumption, and transport—by diffusion or by convective flow—of clotting factors (14, 15, 17–19). Modeling all reactions together with transport phenomena is exceptionally challenging. As is typical for complex networks, many models are proposed to describe hemostasis but are not readily accepted (12). Even the most basic aspects of threshold dynamics of hemostasis remain under debate, such as whether blood can be exposed to clot-inducing stimuli, including tissue factor, without initiating clotting (20, 21, 46). In this article, we use microfluidics to expose the two networks to surfaces patterned with patches presenting clotting stimuli. We perform this comparison of the spatiotemporal dynamics of initiation in these two networks in the absence of convective transport (fluid flow) to make the analysis unambiguous and to avoid complicating effects such as eddies and turbulent flow present at high values of the Reynolds number (22).
To model the spatiotemporal dynamics of initiation, we simplified the complexity of the hemostasis network so both reactions and transport could be analyzed intuitively. We represented (18) 80 reactions of hemostasis as three interacting modules (5), with the overall kinetics corresponding to (i) higher-order autocatalytic production of activators, (ii) linear consumption of activators, and (iii) formation of the clot at high concentrations of activators. Concentration of activators, C, acted as a control parameter. Interactions among these modules lead to a threshold concentration, Cthresh, above (but not below) which clotting was initiated. In this representation, hemostasis is normally in the stable steady state at low C. Small increases of C preserve C < Cthresh, such perturbations decay, and the system returns to the stable steady state. Large perturbations increase the concentration above the unstable steady state (C > Cthresh), resulting in amplification of activators and initiation of clotting. This representation does not require knowledge of all of the reactions of clotting, but it is consistent with the known kinetics of hemostasis (e.g., autocatalytic loops are involved in activation of clotting). Here, we did not attempt to map all reactions of hemostasis onto modules. We hypothesized that a functional, but drastically simplified, chemical model of hemostasis may be created by replacing each module with at least one chemical reaction with kinetics matching that of the module. We previously used organic and inorganic reactions (23) to create such a system and to model spreading of clotting through junctions of vessels (18). This system used acid (the hydronium ion H3O+) as the activator of gelling, or "clotting." The clotting reaction was monitored by observing the transition of the reaction mixture from basic to acidic, which caused gelling of alginic acid and changed the color of a pH indicator. Here, we directly compare the spatiotemporal dynamics of the model system and the complex network, testing whether the chemical model can successfully reproduce and predict the dynamics of initiation of clotting.
英文全文链接：www.pnas.org/cgi/content/full/103/43/15747
