Signaling networks

Signaling Networks

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Just like with metabolism, signalling pathways have multiple cross-connections and intersections. This page focusses on the principles of signalling networks, on their discovery and definition from high-throughput data, on their analysis and on applications that aim to understand dysregulations that underlie disease. An important concept that sets signalling apart from metabolism - at least in part - is the need to consider space: many signalling components are regulated not through changing their expression levels, or their post-translational modification state, but through sequestering or compartmentalizing them. Another importnat aspect is that of concentration: since the number of e.g. binding sites and repressors can be very small (eg. the average lac-repressor concentration in E. coli is 0.7/cell), stochastic effects may play a large role since physical molecules obviously always appear in integral numbers.

 

Introductory reading

Kim et al. (2011) Reduction of complex signaling networks to a representative kernel. Sci Signal 4:ra35. (pmid: 21632468)

[ PubMed ] [ DOI ] The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a "kernel" that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network's kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.

 

Contents

• Principles
• Network discovery
• Applications

   

Further reading and resources

Principles

Kolch et al. (2005) When kinases meet mathematics: the systems biology of MAPK signalling. FEBS Lett 579:1891-5. (pmid: 15763569)

[ PubMed ] [ DOI ] The mitogen activated protein kinase/extracellular signal regulated kinase pathway regulates fundamental cellular function such as cell proliferation, survival, differentiation and motility, raising the question how these diverse functions are specified and coordinated. They are encoded through the activation kinetics of the pathway, a multitude of feedback loops, scaffold proteins, subcellular compartmentalisation, and crosstalk with other pathways. These regulatory motifs alone or in combination can generate a multitude of complex behaviour. Systems biology tries to decode this complexity through mathematical modelling and prediction in order to gain a deeper insight into the inner works of signalling networks.

Lipshtat et al. (2009) Specification of spatial relationships in directed graphs of cell signaling networks. Ann N Y Acad Sci 1158:44-56. (pmid: 19348631)

[ PubMed ] [ DOI ] Graph theory provides a useful and powerful tool for the analysis of cellular signaling networks. Intracellular components such as cytoplasmic signaling proteins, transcription factors, and genes are connected by links, representing various types of chemical interactions that result in functional consequences. However, these graphs lack important information regarding the spatial distribution of cellular components. The ability of two cellular components to interact depends not only on their mutual chemical affinity but also on colocalization to the same subcellular region. Localization of components is often used as a regulatory mechanism to achieve specific effects in response to different receptor signals. Here we describe an approach for incorporating spatial distribution into graphs and for the development of mixed graphs where links are specified by mutual chemical affinity as well as colocalization. We suggest that such mixed graphs will provide more accurate descriptions of functional cellular networks and their regulatory capabilities and aid in the development of large-scale predictive models of cellular behavior.

Chatterjee & Kumar (2011) Unraveling the design principle for motif organization in signaling networks. PLoS ONE 6:e28606. (pmid: 22164309)

[ PubMed ] [ DOI ] Cellular signaling networks display complex architecture. Defining the design principle of this architecture is crucial for our understanding of various biological processes. Using a mathematical model for three-node feed-forward loops, we identify that the organization of motifs in specific manner within the network serves as an important regulator of signal processing. Further, incorporating a systemic stochastic perturbation to the model we could propose a possible design principle, for higher-order organization of motifs into larger networks in order to achieve specific biological output. The design principle was then verified in a large, complex human cancer signaling network. Further analysis permitted us to classify signaling nodes of the network into robust and vulnerable nodes as a result of higher order motif organization. We show that distribution of these nodes within the network at strategic locations then provides for the range of features displayed by the signaling network.

Network discovery and analysis

Pe'er (2005) Bayesian network analysis of signaling networks: a primer. Sci STKE 2005:pl4. (pmid: 15855409)

[ PubMed ] [ DOI ] High-throughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We discuss ways to automatically derive a Bayesian network model from proteomic data and to interpret the resulting model.

Shimoni et al. (2010) Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows. PLoS Comput Biol 6:e1000828. (pmid: 20585619)

[ PubMed ] [ DOI ] Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.

Wang & Albert (2011) Elementary signaling modes predict the essentiality of signal transduction network components. BMC Syst Biol 5:44. (pmid: 21426566)

[ PubMed ] [ DOI ] BACKGROUND: Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. RESULTS: In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. CONCLUSIONS: This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The source codes for the algorithms developed in this study can be found at http://www.phys.psu.edu/~ralbert/ESM.

Schulthess & Blüthgen (2011) From reaction networks to information flow--using modular response analysis to track information in signaling networks. Meth Enzymol 500:397-409. (pmid: 21943908)

[ PubMed ] [ DOI ] Even if the biochemical details of signaling networks are known, it is often hard to track how information flows through the network. In combination with experimental techniques, modular response analysis has proven useful in analyzing the quantitative information transfer in signal transduction networks. The sensitivity of a target (e.g., transcription factor, protein) to an upstream stimulus (e.g., growth factor) can be determined by a so-called response coefficient. We have used this methodology to analyze how information flows in networks where the details of the mechanisms in the networks are known, but parameters are lacking. Using a Monte Carlo approach, we apply this method to track the routes of information flow. More specifically, we determine whether a given species has no, positive or negative influence on any other species in the network. Surprisingly, one can uniquely determine whether a molecule activates or inhibits another one in more than 99% of the interactions solely from the topology of the reaction network. To exemplify the methodology, we briefly discuss three signaling networks of different complexity: (i) a Wnt signaling pathway model with 15 species, (ii) a MAPK signaling pathway model with 200 species, and (iii) a large-scale signaling network of the entire cell with over 6000 species.

Applications

Schramm et al. (2010) Regulation patterns in signaling networks of cancer. BMC Syst Biol 4:162. (pmid: 21110851)

[ PubMed ] [ DOI ] BACKGROUND: Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample. RESULTS: We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells. CONCLUSION: Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed.

Prasasya et al. (2011) Analysis of cancer signaling networks by systems biology to develop therapies. Semin Cancer Biol 21:200-6. (pmid: 21511035)

[ PubMed ] [ DOI ] Cancer is a complex and heterogeneous disease, demonstrating variations with respect to tumor types and between individual tumors. This heterogeneity has complicated the search for 'magic bullets'-individual genes or pathways that could be targeted and have beneficial effects for large numbers of patients. Instead, recent studies suggest that cancer can be more effectively analyzed through the use of systems biology techniques that examine multiple pathways and account for interactions between these pathways. In this review, we outline the various ways in which systems biology can be utilized to translate high-throughput data into a signaling network and then computationally analyze how cells make decisions based on the information flow through this network. We then discuss recent studies utilizing network-level analysis to reveal therapeutic targets, predict which tumors will be sensitive to existing drugs, and develop combinatorial therapies that target multiple pathways, demonstrating the potential for systems biology to revolutionize cancer therapy.

Hughey et al. (2010) Computational modeling of mammalian signaling networks. Wiley Interdiscip Rev Syst Biol Med 2:194-209. (pmid: 20836022)

[ PubMed ] [ DOI ] One of the most exciting developments in signal transduction research has been the proliferation of studies in which a biological discovery was initiated by computational modeling. In this study, we review the major efforts that enable such studies. First, we describe the experimental technologies that are generally used to identify the molecular components and interactions in, and dynamic behavior exhibited by, a network of interest. Next, we review the mathematical approaches that are used to model signaling network behavior. Finally, we focus on three specific instances of 'model-driven discovery': cases in which computational modeling of a signaling network has led to new insights that have been verified experimentally.