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A mathematical model of biological signaling networks and network characteristics correlated with signaling behavior.

機(jī)譯：生物信號網(wǎng)絡(luò)和網(wǎng)絡(luò)特征與信號行為相關(guān)的數(shù)學(xué)模型。

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Traditionally, molecular biology has focused on the role of individual genes. More recently, systems biology has shifted the focus to interactions among many genes; the field emphasizes that the behavior of genetic networks is important and difficult to predict from the knowledge of a single gene. This work studies interacting biochemical networks. In particular, we focus on the characteristics of signaling networks. Biological signaling occurs when a chemical outside the cell (the signal) binds to a receptor on the surface of the cell. This causes a signaling cascade of chemical reactions in the cell, leading to a change in cellular behavior. When a cell does not properly respond to its signals, cancer or other diseases can result.; We developed a simplified dynamical systems model to describe cellular signaling. The model is based on a model of interacting genetic networks (developed by Wagner and extended by Siegal and Bergman). One element of the system's state vector is identified as the signal. The influence of the signal on other elements of the network allows the system to switch between different stable steady states depending on the state of the signal. Using our model and mathematical definition of signaling, we studied the network characteristics associated with signaling behavior in small networks (2, 3, or 4 elements). We find that the most important parameters associated with signaling behavior are the structure of the network and the number and placement of non-zero connections between elements. The more connections there are from the signal to the subnetwork (the network with the signal and connections to/from the signal removed), the more likely the network is to signal. Networks that signal are not likely to be full rank. In addition, self connections, particularly negative self connections, are suppressed in signaling networks, compared to the full population of networks. Finally, we use our model to study an example biological signaling system (a phosphotransfer signaling pathway). This work gives insight into the network structure that would most readily allow cells to evolve signaling behavior.
機(jī)譯：傳統(tǒng)上，分子生物學(xué)專注于單個(gè)基因的作用。最近，系統(tǒng)生物學(xué)已將重點(diǎn)轉(zhuǎn)移到許多基因之間的相互作用上。該領(lǐng)域強(qiáng)調(diào)，遺傳網(wǎng)絡(luò)的行為很重要，而且很難從單個(gè)基因的知識進(jìn)行預(yù)測。這項(xiàng)工作研究相互作用的生化網(wǎng)絡(luò)。特別地，我們關(guān)注信令網(wǎng)絡(luò)的特征。當(dāng)細(xì)胞外的化學(xué)物質(zhì)（信號）與細(xì)胞表面的受體結(jié)合時(shí)，就會(huì)發(fā)生生物信號傳遞。這導(dǎo)致細(xì)胞中化學(xué)反應(yīng)的信號級聯(lián)，導(dǎo)致細(xì)胞行為發(fā)生變化。當(dāng)細(xì)胞不能正確響應(yīng)其信號時(shí)，會(huì)導(dǎo)致癌癥或其他疾病。我們開發(fā)了一種簡化的動(dòng)力學(xué)系統(tǒng)模型來描述細(xì)胞信號傳導(dǎo)。該模型基于相互作用的遺傳網(wǎng)絡(luò)模型（由Wagner開發(fā)，由Siegal和Bergman擴(kuò)展）。系統(tǒng)狀態(tài)向量的一個(gè)元素被識別為信號。信號對網(wǎng)絡(luò)其他元素的影響使系統(tǒng)可以根據(jù)信號狀態(tài)在不同的穩(wěn)定穩(wěn)態(tài)之間進(jìn)行切換。使用我們的信令模型和數(shù)學(xué)定義，我們研究了與小型網(wǎng)絡(luò)（2、3或4個(gè)元素）中的信令行為相關(guān)的網(wǎng)絡(luò)特性。我們發(fā)現(xiàn)與信令行為相關(guān)的最重要的參數(shù)是網(wǎng)絡(luò)的結(jié)構(gòu)以及元素之間非零連接的數(shù)量和位置。從信號到子網(wǎng)（帶有信號的網(wǎng)絡(luò)以及到/從信號刪除的連接）之間的連接越多，網(wǎng)絡(luò)發(fā)出信號的可能性就越大。發(fā)出信號的網(wǎng)絡(luò)不太可能排名很高。另外，與全部網(wǎng)絡(luò)相比，信令網(wǎng)絡(luò)中的自連接，尤其是負(fù)自連接，受到抑制。最后，我們使用我們的模型研究示例生物信號系統(tǒng)（磷酸轉(zhuǎn)移信號通路）。這項(xiàng)工作深入了解了網(wǎng)絡(luò)結(jié)構(gòu)，該結(jié)構(gòu)最容易允許細(xì)胞演化信號傳導(dǎo)行為。

著錄項(xiàng)

  • 作者

    Waterbury, L. A.;

  • 作者單位

    University of Colorado at Boulder.$bApplied Mathematics.;

  • 授予單位 University of Colorado at Boulder.$bApplied Mathematics.;
  • 學(xué)科 Applied Mechanics.
  • 學(xué)位 M.S.
  • 年度 2007
  • 頁碼 114 p.
  • 總頁數(shù) 114
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類 應(yīng)用力學(xué);
  • 關(guān)鍵詞

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