Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network

To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Coaffinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified nine DAF-7/TGF-beta signaling modifiers, seven of which are conserved in humans. We show that one of these has functional homology to human SNO/SKI oncoproteins and that mutations at the corresponding genetic locus daf-5 confer defects in DAF-7/TGF-beta signaling. Our results reveal substantial molecular complexity in DAF-7/TGF-beta signal transduction. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.     

Receptor cross-talk in angiogenesis: Mapping environmental cues to cell phenotype using a stochastic, Boolean signaling network model

Cancer invasion and metastasis depend on tumor-induced angiogenesis, the means by which cancer cells attract and maintain a blood supply. During angiogenesis, cellular processes are tightly coordinated by signaling molecules and their receptors. Understanding how endothelial cells synthesize multiple biochemical signals can catalyze the development of novel therapeutic strategies to combat cancer. This study is the first to propose a signal transduction model highlighting the cross-talk between key receptors involved in angiogenesis, namely the VEGF, integrin, and cadherin receptors. From experimental data, we construct a network model of receptor cross-talk and analyze its dynamics. We identify relationships between receptor activation combinations and cellular function, and show that cross-talk is crucial to phenotype determination. The network converges to a unique set of output states that correspond to known cell phenotypes: migratory, proliferating, quiescent, apoptotic, and it predicts one phenotype that challenges the “go or grow” hypothesis. Finally, we use the model to study protein inhibition and to suggest molecular targets for anti-angiogenic therapies.     

Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

Background

Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.

Results

Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G₁ fixed point was found for deterministic updating schemes.

Conclusions

In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.     

Protein evolution on a human signaling network

Background  

The architectural structure of cellular networks provides a framework for innovations as well as constraints for protein evolution. This issue has previously been studied extensively by analyzing protein interaction networks. However, it is unclear how signaling networks influence and constrain protein evolution and conversely, how protein evolution modifies and shapes the functional consequences of signaling networks. In this study, we constructed a human signaling network containing more than 1,600 nodes and 5,000 links through manual curation of signaling pathways, and analyzed the d _N/d _S values of human-mouse orthologues on the network.     

细胞粘附分子群相互作用网络模型研究

通过相互作用网络可视化软件Cytoscape对整联蛋白粘合体156种成份及相互间的690个反应实现网络可视化,并通过网络分析软件network analyser得到该网络节点间的平均连接并不复杂是一个不可分割的整体网络,网络较稳定,信息传递很快等。直接可视化的网络通过节点的形状改变视图有助于我们在原始数据的基础上更好的认识这一复杂的作用网络的相关信息。     

Boolean network analysis of a neurotransmitter signaling pathway

BACKGROUND: A Boolean network is a simple computational model that may provide insight into the overall behavior of genetic networks and is represented by variables with two possible states (on/off), of the individual nodes/genes of the network. In this study, a Boolean network model has been used to simulate a molecular pathway between two neurotransmitter receptor, dopamine and glutamate receptor, systems in order to understand the consequence of using logic gate rules between nodes, which have two possible states (active and inactive). RESULTS: The dynamical properties of this Boolean network model of the biochemical pathway shows that, the pathway is stable and that, deletion/knockout of certain biologically important nodes cause significant perturbation to this network. The analysis clearly shows that in addition to the expected components dopamine and dopamine receptor 2 (DRD2), Ca(2+) ions play a critical role in maintaining stability of the pathway. CONCLUSION: So this method may be useful for the identification of potential genetic targets, whose loss of function in biochemical pathways may be responsible for disease onset. The molecular pathway considered in this study has been implicated with a complex disorder like schizophrenia, which has a complex multifactorial etiology.     

Mean-field Boolean network model of a signal transduction network

In this paper we provide a mean-field Boolean network model for a signal transduction network of a generic fibroblast cell. The network consists of several main signaling pathways, including the receptor tyrosine kinase, the G-protein coupled receptor, and the Integrin signaling pathway. The network consists of 130 nodes, each representing a signaling molecule (mainly proteins). Nodes are governed by Boolean dynamics including canalizing functions as well as totalistic Boolean functions that depend only on the overall fraction of active nodes. We categorize the Boolean functions into several different classes. Using a mean-field approach we generate a mathematical formula for the probability of a node becoming active at any time step. The model is shown to be a good match for the actual network. This is done by iterating both the actual network and the model and comparing the results numerically. Using the Boolean model it is shown that the system is stable under a variety of parameter combinations. It is also shown that this model is suitable for assessing the dynamics of the network under protein mutations. Analytical results support the numerical observations that in the long-run at most half of the nodes of the network are active.     

Endostatin's antiangiogenic signaling network

It is here demonstrated that the set of gene expressions underlying the angiogenic balance in tissues can be molecularly reset en masse by a single protein. Using genome-wide expression profiling, coupled with RT-PCR and phosphorylation analysis, we show that the endogenous angiogenesis inhibitor endostatin downregulates many signaling pathways in human microvascular endothelium associated with proangiogenic activity. Simultaneously, endostatin is found to upregulate many antiangiogenic genes. The result is a unique alignment between the direction of gene regulation and angiogenic status. Profiling further reveals the regulation of genes not heretofore associated with angiogenesis. Our analysis of coregulated genes shows complex interpathway communications in an intricate signaling network that both recapitulates and extends on current understanding of the angiogenic process. More generally, insights into the nature of genetic networking from the cell biologic and therapeutic perspectives are revealed.     

Cardiac Z-disc signaling network

During the last 15 years, the perception of the cardiac z-disc has undergone substantial changes. Initially viewed as a structural component at the lateral boundaries of the sarcomere, the cardiac z-disc has increasingly become recognized as a nodal point in cardiomyocyte signal transduction and disease. This minireview thus focuses on novel components and recent developments in z-disc biology and their role in cardiac signaling and disease.     

Capacity of a model immune network

The capacity of a model immune network in terms of the number of different antigens that can be vaccinated against without any memory lost is computed and tested by numerical simulations. We also investigate memory loss and failure to vaccinate due to overcrowding the network with too many antigens. The computations are done for two different strategies for proliferation, one implying all the antigen specific clones and the second one being more thrifty.     

Toward a network model of dystonia

Dystonia has generally been considered a basal ganglia (BG) disorder. Early models hypothesized that dystonia occurred as the result of reduced mean discharge rates in the internal segment of the globus pallidus (GPi). Increasing evidence suggests a more systemwide disruption of the basal ganglia thalamic circuit (BGTC) resulting in altered firing patterns, synchronized oscillations, and widened receptive fields. A model of dystonia incorporating these changes within the BGTC is presented in which we postulate that this pathophysiology arises from disruptions within the striatum. Alterations in the cerebellothalamocortical (CBTC) pathway to the development of dystonia may also play a role. However, the contribution of CBTC abnormalities to dystonia remains unclear and may vary with different etiologies of dystonia. Finally, the relevance of established and emerging theories related to the pathophysiology of dystonia is addressed within the context of improving conventional approaches for deep brain stimulation (DBS) treatment strategies.     

A protein interaction network and cell signaling pathways activated by muramyl peptides

Review is devoted to studying the interaction muramyl peptides with protein components of immune system cells. Systems analysis of published results may be useful to select not only the strategy to further explore the function of this class of glycopeptides, but their use in clinical practice.     

A logical model provides insights into T cell receptor signaling

Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.     

Granulosa cell apoptosis: conservation of cell signaling in an avian ovarian model system

Ovarian follicle atresia in all vertebrates studied to date is mediated via apoptosis, a process that is often initiated within the granulosa cell layer. While follicle atresia is considered a normal physiological process to insure the greatest chance for ovulation of fertilizable oocytes, abnormally high rates of atresia result in chronic infertility and/or premature termination of fertility (e.g., menopause). Although the vast majority of research to elucidate the molecular ordering of cell signaling during the process of granulosa cell apoptosis has been conducted in mammalian model systems, there is ample evidence to demonstrate that many of the proteins, enzymes and cell-signaling pathways are common to ovarian follicles from avian species. The following review will discuss evidence for the conservation of cellular processes that regulate the fate of granulosa cells from the avian, versus mammalian, ovary during follicle development.     

Boolean network model predicts cell cycle sequence of fission yeast

A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe) is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer faithfully reproduces the known activity sequence of regulatory proteins along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.     

Cell adhesion in development: a complex signaling network

Cell-adhesion molecules play a major role in morphogenesis and organogenesis. In vertebrates, a significant fraction of genes encode cell-adhesion molecules. Multiple signal-transduction pathways have been described that modulate the adhesion process. These pathways have been studied in great detail for cadherins and integrins - two major adhesion systems controlling cell-cell and cell-substrate interactions. Recent findings confirm that a given cell-adhesion molecule can be implicated at different stages of development in processes as diverse as cell positioning, tissue patterning and compartmentalization, axon guidance and synaptogenesis. Clearly, a wide variety of new biophysical techniques and genomic approaches will permit analysis of the roles of adhesive interactions in development to be addressed with far greater precision.