當(dāng)不慎傷了手指的時候,，血液會在傷口附近凝成血痂并自動止血,。在這個看似簡單的凝血和止血過程中,，需要80多種不同的化學(xué)反應(yīng)，只要一個反應(yīng)出了問題,，血塊就可能形成于不當(dāng)?shù)奈恢迷斐刹豢霸O(shè)想的后果,。多年來，科學(xué)家始終無法完全明白凝血過程,，也說不清凝血何時會發(fā)生,。目前，芝加哥大學(xué)的研究人員開發(fā)了一種簡易的技術(shù),，能夠預(yù)測凝血發(fā)生的時間和部位,。
該研究的發(fā)起人之一、芝加哥大學(xué)的化學(xué)專業(yè)研究生Christian Kastrup說：“真正精彩之處是：我們實(shí)際上是通過一個人造的凝血模型來進(jìn)行研究預(yù)測的,，僅用三個簡單的化學(xué)反應(yīng)就代表了凝血過程中的80多個反應(yīng),。”這項(xiàng)技術(shù)詳細(xì)地報道在《美國國家科學(xué)院院刊》網(wǎng)絡(luò)版上。
我們血液中的成分——產(chǎn)生于骨髓的圓形血小板,，不斷地隨著血液流動,，一直在監(jiān)視著血管的狀況，一旦發(fā)生破裂需要修補(bǔ),，血小板就會變得黏稠并結(jié)塊,。同時血液中的凝血酶系統(tǒng)開始一系列的反應(yīng)，生成長形的,、有彈性的蛋白質(zhì),。威克森林大學(xué)科學(xué)家們的一項(xiàng)最新研究表明：單就纖維蛋白來說，其纖維是大自然中最具柔韌性的蛋白質(zhì),，可以拉伸至原長度的三倍,。這些蛋白質(zhì)叫做凝血因子,，它們形成一張具有柔韌性的網(wǎng),，攔住血小板，將血小板固定在血管破裂的部位,。作為整個一張網(wǎng),，這些蛋白質(zhì)失去了部分、而不是全部柔韌性,。在肉眼看來,，這張蛋白質(zhì)和血小板構(gòu)成的網(wǎng)就是所結(jié)的痂。
在這項(xiàng)新研究中,，Kastrup和同事發(fā)現(xiàn)止血網(wǎng)中有一種凝血因子叫組織因子,，其分布情況決定著血小板是否會凝結(jié)。Kastrup告訴《生命科學(xué)》的記者說：“將組織因子集中在表面特定的區(qū)域內(nèi),，遇到血液后就會發(fā)生凝結(jié),。相反，將組織因子分散在試驗(yàn)樣品當(dāng)中，凝血就沒有發(fā)生,。”由于只有當(dāng)很多組織因子集中在一定的區(qū)域內(nèi)時凝血才會發(fā)生,，科學(xué)家也許能夠通過監(jiān)視病人的組織因子濃度來預(yù)測凝血的情況。人們甚至設(shè)想有朝一日,，這個方法可用來診斷和防止有害的凝血,。
當(dāng)血小板、凝血因子和其他化學(xué)物質(zhì)不能協(xié)作的時候,，就會導(dǎo)致失血過多,，或相反，由于不必要的凝血造成血栓,。椐美國國家血友病基金會統(tǒng)計(jì),，每年有60萬美國人死于非正常凝血。“凝血對于止血和組織再生有益處,，但凝血也跟許多疾病有關(guān),，如中風(fēng)和出血，”另一個研究人員說,，“心臟或大腦等部位的凝血會造成致命的后果,。假如能預(yù)測何時、何處將會產(chǎn)生凝血,，可防止人們患上凝血不良帶來的各種疾病,。” （胡德良編譯自美國《生命科學(xué)網(wǎng)》）
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.
英文全文鏈接：www.pnas.org/cgi/content/full/103/43/15747
