细胞信号转导网络的建模

细胞信号转导网络的组织和调控是十分复杂的。目前,结合试验数据,借助计算机建模的方法,进行信号转导网络的研究颤受关注。介绍了信号通路数学建模的步骤、方法,描述了当前信号转导网络建模的不同类型的模型及各自的优缺点。此外,还以G蛋白偶联受体模型为例,详细阐述了模型的层次问题,探讨了信号转导建模所面临的挑战及其未来的发展趋势。     

沉默DNA-PKcs对细胞信号转导相关基因转录的影响

利用RNA干扰技术构建DNA-PKcs表达抑制细胞模型,探讨DNA-PKcs对HeLa细胞信号转导相关基因表达的调控作用.通过观察细胞对辐射及顺铂的敏感性,鉴定细胞表型变化.用寡核苷酸芯片检测细胞信号转导相关基因的转录谱,并用RT-PCR方法和SEAP检测系统进一步验证基因的表达变化.所筛选出的DNA-PKcs表达抑制细胞对辐射及顺铂的敏感性升高,15个与细胞信号转导相关的基因表达升高,其中7个是与干扰素信号转导反应相关的基因.8个表达下降,包括有细胞增殖分化相关基因,如NFAT.RT-PCR检测结果与芯片结果相一致,利用SEAT报告系统检测,进一步证实NFAT转录活性下调.实验结果表明,DNA-PKcs除了参与DNA修复外,还调控细胞信号转导相关基因的表达,而且大多与细胞增殖分化相关.     

基于信号网络的功能细胞设计

细胞信号网络是细胞应对环境变化、调控细胞功能以及决定细胞命运的中央处理器。运用合成生物学方法,人工设计细胞信号网络对于"细胞机器"的构建具有重要作用。信号网络通过编码定量的动力学信号,能够在多个维度对细胞工程中的多个子功能单元进行调控。本文介绍了天然信号网络的动力学功能的研究进展,阐述了基于信号网络的功能蛋白质设计的合成生物学相关的方法和思路,并展望了信号网络在下一代合成生物学中的战略意义。     

G蛋白与植物细胞信号转导

本文概述了植物细胞信号转导的一些情况,并以信号转导中的G蛋白模型为依据,着重介绍植物细胞中存在的G蛋白,指出植物细胞G蛋白对于植物细胞信号转导的重要性。     

植物细胞信号转导网

植物细胞信号转导对植物的生长发育起着重要的作用。本文概述了植物中存在复杂信号网络的生理和基因的证据,主要集中于光、激素、糖信号间的转导相互作用。     

细胞复杂信号转导的调控模式

越来越多的证据表明 ,细胞信号转导途径之间存在着各种各样的相互通讯(ｃｒｏｓｓｔａｌｋ) ,由此形成了复杂的信号转导网络。特定的输入信号在通过这一网络传输信号时 ,信号的强度或 /和时间会被修饰或调控 ,从而引发不同的生理效应[1,2 ] 。通过对细胞信号转导网络的研究 ,能使我们更好的理解信号转导网络改变所引发的病理过程 ,更好的理解肿瘤、糖尿病、精神病等疾病的发病机理。现将存在于复杂细胞信号转导网络中的信号转导调控模式描述如下。1 .双稳调控模式最简单的双稳调控模式由两条相互影响的信号转导途径组成 ,其间存在正反馈循环…     

丙型肝炎病毒蛋白作用于细胞信号转导途径的研究进展

细胞信号转导异常往往与人类疾病的发生、发展密切相关。一些病毒致病和感染机制即为病毒抗原蛋白作用宿主细胞信号转导途径,导致宿主细胞内信号转导发生紊乱。丙型肝炎病毒（HCV）是引发慢性丙型肝炎,导致肝硬化和肝细胞癌发生的主要病原体,但目前HCV的致病机制与宿主内持续感染机制尚不清楚。HCV致病机制可能与HCV表达的蛋白质干扰宿主细胞信号转导途径而导致异常的细胞信号转导有关。研究HCV蛋白对宿主细胞信号转导途径的影响不仅有助于阐明其致病机制,还能为新药设计和寻找新的治疗方法提供新思路和新靶点。本文主要综述了近年来国内外有关HCV蛋白作用细胞信号转导途径的研究进展。     

基于质谱的蛋白质组学信号网络研究进展

细胞信号转导网络调控着所有细胞和器官的生物学过程。以往信号转导网络的研究主要采用一些生物化学方法开展,如抗体技术。目前,基于质谱的大规模蛋白质组学研究可以在翻译后修饰、蛋白质互作及蛋白质表达水平上,系统地研究信号转导事件。基于蛋白质组学的大规模信号转导的研究将改变我们对信号转导网络的理解。从蛋白质组翻译后修饰、蛋白质互作及蛋白质表达3个方面综述了质谱在信号转导方面的研究。     

芸苔属自交不亲和细胞信号转导的研究进展

在芸苔属植物的自交不亲和细胞信号转导过程中,信号分子-SCR配体是由花粉粒产生的,被柱头乳突细胞SRK受体识别后,进行细胞内信号转导。这对受体-配体是两个由S位点编码的且高度多态的蛋白质,它们决定着自交不亲和反应。配体是位于花粉粒表面的一个小的胞被蛋白,由SCR基因编码;受体是位于柱头乳突细胞原生质膜上的跨膜的蛋白质激酶,由SRK基因编码。在自交授粉过程中,配体SCR和受体SRK的相互作用激活了受体SRK,被激活的SRK通过其下游组分ARC1介导底物的泛肽化,然后泛肽化的底物在蛋白酶体/CSN中被降解,从而导致了自交不亲和性反应。这些降解的底物可能是促进花粉水合、萌发和花粉管生长的雌蕊亲和因子。主要针对芸苔属自交不亲和细胞信号转导作一综述。     

基于Unscented卡尔曼滤波器的信号转导通路参数与不可观测状态估计

系统生物学的一个主要目的是建立细胞信号转导通路的数学模型。然而细胞信号转导通路多具有较强非线性、参与生化反应物质多等特点,同时测量数据往往不完备,并且混有来自实验各个阶段的各种噪声,这些都给模型参数估计带来很大困难。该文采用Unscented卡尔曼滤波器估计信号转导通路未知参数与模型不可观测状态。以肿瘤坏死因子诱导的核转录因子κB信号转导通路为例进行了仿真,结果表明,采用该方法可以在噪声干扰下较准确地的估计系统未知参数和不能观测状态。     

A Minimal Model of Signaling Network Elucidates Cell-to-Cell Stochastic Variability in Apoptosis

Background

Signaling networks are designed to sense an environmental stimulus and adapt to it. We propose and study a minimal model of signaling network that can sense and respond to external stimuli of varying strength in an adaptive manner. The structure of this minimal network is derived based on some simple assumptions on its differential response to external stimuli.

Methodology

We employ stochastic differential equations and probability distributions obtained from stochastic simulations to characterize differential signaling response in our minimal network model. Gillespie''s stochastic simulation algorithm (SSA) is used in this study.

Conclusions/Significance

We show that the proposed minimal signaling network displays two distinct types of response as the strength of the stimulus is decreased. The signaling network has a deterministic part that undergoes rapid activation by a strong stimulus in which case cell-to-cell fluctuations can be ignored. As the strength of the stimulus decreases, the stochastic part of the network begins dominating the signaling response where slow activation is observed with characteristic large cell-to-cell stochastic variability. Interestingly, this proposed stochastic signaling network can capture some of the essential signaling behaviors of a complex apoptotic cell death signaling network that has been studied through experiments and large-scale computer simulations. Thus we claim that the proposed signaling network is an appropriate minimal model of apoptosis signaling. Elucidating the fundamental design principles of complex cellular signaling pathways such as apoptosis signaling remains a challenging task. We demonstrate how our proposed minimal model can help elucidate the effect of a specific apoptotic inhibitor Bcl-2 on apoptotic signaling in a cell-type independent manner. We also discuss the implications of our study in elucidating the adaptive strategy of cell death signaling pathways.     

Network quantification of EGFR signaling unveils potential for targeted combination therapy

The epidermal growth factor receptor (EGFR) signaling network is activated in most solid tumors, and small‐molecule drugs targeting this network are increasingly available. However, often only specific combinations of inhibitors are effective. Therefore, the prediction of potent combinatorial treatments is a major challenge in targeted cancer therapy. In this study, we demonstrate how a model‐based evaluation of signaling data can assist in finding the most suitable treatment combination. We generated a perturbation data set by monitoring the response of RAS/PI3K signaling to combined stimulations and inhibitions in a panel of colorectal cancer cell lines, which we analyzed using mathematical models. We detected that a negative feedback involving EGFR mediates strong cross talk from ERK to AKT. Consequently, when inhibiting MAPK, AKT activity is increased in an EGFR‐dependent manner. Using the model, we predict that in contrast to single inhibition, combined inactivation of MEK and EGFR could inactivate both endpoints of RAS, ERK and AKT. We further could demonstrate that this combination blocked cell growth in BRAF‐ as well as KRAS‐mutated tumor cells, which we confirmed using a xenograft model.     

Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a “pan-cell-state” strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer.     

Minimal Network Topologies for Signal Processing during Collective Cell Chemotaxis

Cell-cell communication plays an important role in collective cell migration. However, it remains unclear how cells in a group cooperatively process external signals to determine the group’s direction of motion. Although the topology of signaling pathways is vitally important in single-cell chemotaxis, the signaling topology for collective chemotaxis has not been systematically studied. Here, we combine mathematical analysis and simulations to find minimal network topologies for multicellular signal processing in collective chemotaxis. We focus on border cell cluster chemotaxis in the Drosophila egg chamber, in which responses to several experimental perturbations of the signaling network are known. Our minimal signaling network includes only four elements: a chemoattractant, the protein Rac (indicating cell activation), cell protrusion, and a hypothesized global factor responsible for cell-cell interaction. Experimental data on cell protrusion statistics allows us to systematically narrow the number of possible topologies from more than 40,000,000 to only six minimal topologies with six interactions between the four elements. This analysis does not require a specific functional form of the interactions, and only qualitative features are needed; it is thus robust to many modeling choices. Simulations of a stochastic biochemical model of border cell chemotaxis show that the qualitative selection procedure accurately determines which topologies are consistent with the experiment. We fit our model for all six proposed topologies; each produces results that are consistent with all experimentally available data. Finally, we suggest experiments to further discriminate possible pathway topologies.     

The Actin Cytoskeleton and Signaling Network during Pollen Tube Tip Growth

The organization and dynamics of the actin cytoskeleton play key roles in many aspects of plant cell development. The actin cytoskeleton responds to internal developmental cues and en-vironmental signals and is involved in cell division, subcellular organelle movement, cell polarity and polar cell growth. The tip-growing pollen tubes provide an ideal model system to investigate fundamental mechanisms of underlying polarized cell growth. In this system, most signaling cascades required for tip growth, such as Ca~(2+)-, small GTPases- and lipid-mediated signaling have been found to be involved in transmitting signals to a large group of actin-binding proteins. These actin-binding proteins subsequently regulate the structure of the actin network, as well as the rapid turnover of actin filaments (F-actin), thereby eventually controlling tip growth. The actin cytoskeleton acts as an integrator in which multiple signaling pathways converge, providing a general growth and regulatory mechanism that applies not only for tip growth but also for polarized diffuse growth in plants.     

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.     

The Actin Cytoskeleton and Signaling Network during Pollen Tube Tip Growth

The organization and dynamics of the actin cytoskeleton play key roles in many aspects of plant cell development. The actin cytoskeleton responds to internal developmental cues and environmental signals and is involved in cell division, subcellular organelle movement, cell polarity and polar cell growth. The tipgrowing pollen tubes provide an ideal model system to investigate fundamental mechanisms of underlying polarized cell growth. In this system, most signaling cascades required for tip growth...     

Cell-sorting at the A/P boundary in the Drosophila wing primordium: a computational model to consolidate observed non-local effects of Hh signaling

Non-intermingling, adjacent populations of cells define compartment boundaries; such boundaries are often essential for the positioning and the maintenance of tissue-organizers during growth. In the developing wing primordium of Drosophila melanogaster, signaling by the secreted protein Hedgehog (Hh) is required for compartment boundary maintenance. However, the precise mechanism of Hh input remains poorly understood. Here, we combine experimental observations of perturbed Hh signaling with computer simulations of cellular behavior, and connect physical properties of cells to their Hh signaling status. We find that experimental disruption of Hh signaling has observable effects on cell sorting surprisingly far from the compartment boundary, which is in contrast to a previous model that confines Hh influence to the compartment boundary itself. We have recapitulated our experimental observations by simulations of Hh diffusion and transduction coupled to mechanical tension along cell-to-cell contact surfaces. Intriguingly, the best results were obtained under the assumption that Hh signaling cannot alter the overall tension force of the cell, but will merely re-distribute it locally inside the cell, relative to the signaling status of neighboring cells. Our results suggest a scenario in which homotypic interactions of a putative Hh target molecule at the cell surface are converted into a mechanical force. Such a scenario could explain why the mechanical output of Hh signaling appears to be confined to the compartment boundary, despite the longer range of the Hh molecule itself. Our study is the first to couple a cellular vertex model describing mechanical properties of cells in a growing tissue, to an explicit model of an entire signaling pathway, including a freely diffusible component. We discuss potential applications and challenges of such an approach.