Topological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk

Signal transduction networks have only been studied at a small scale because large-scale reconstructions and suitable in silico analysis methods have not been available. Since reconstructions of large signaling networks are progressing well there is now a need to develop a framework for analysing structural properties of signaling networks. One such framework is presented here, one that is based on systemically independent pathways and a mass-balanced representation of signaling events. This approach was applied to a prototypic signaling network and it allowed for: (1) a systemic analysis of all possible input/output relationships, (2) a quantitative evaluation of network crosstalk, or the interconnectivity of systemically independent pathways, (3) a measure of the redundancy in the signaling network, (4) the participation of reactions in signaling pathways, and (5) the calculation of correlated reaction sets. These properties emerge from network structure and can only be derived and studied within a defined mathematical framework. The calculations presented are the first of their kind for a signaling network, while similar analysis has been extensively performed for prototypic and genome-scale metabolic networks. This approach does not yet account for dynamic concentration profiles. Due to the scalability of the stoichiometric formalism used, the results presented for the prototypic signaling network can be obtained for large signaling networks once their reconstruction is completed.     

Modeling cell signaling networks

Cell signaling pathways interact with one another to form networks in mammalian systems. Such networks are complex in their organization and exhibit emergent properties such as bistability and ultrasensitivity. Analysis of signaling networks requires a combination of experimental and theoretical approaches including the development and analysis of models. This review focuses on theoretical approaches to understanding cell signaling networks. Using heterotrimeric G protein pathways an example, we demonstrate how interactions between two pathways can result in a network that contains a positive feedback loop and function as a switch. Different mathematical approaches that are currently used to model signaling networks are described, and future challenges including the need for databases as well as enhanced computing environments are discussed.     

Two approaches for metabolic pathway analysis?

Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in (reconstructed) biochemical reaction networks. Of the two most promising concepts for pathway analysis, one relies on elementary flux modes and the other on extreme pathways. These concepts are closely related because extreme pathways are a subset of elementary modes. Here, the common features, differences and applicability of these concepts are discussed. Assessing metabolic systems by the set of extreme pathways can, in general, give misleading results owing to the exclusion of possibly important routes. However, in certain network topologies, the sets of elementary modes and extreme pathways coincide. This is quite often the case in realistic applications. In our opinion, the unification of both approaches into one common framework for metabolic pathway analysis is necessary and achievable.     

Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems

The elucidation of organism-scale metabolic networks necessitates the development of integrative methods to analyze and interpret the systemic properties of cellular metabolism. A shift in emphasis from single metabolic reactions to systemically defined pathways is one consequence of such an integrative analysis of metabolic systems. The constraints of systemic stoichiometry, and limited thermodynamics have led to the definition of the flux space within the context of convex analysis. The flux space of the metabolic system, containing all allowable flux distributions, is constrained to a convex polyhedral cone in a high-dimensional space. From metabolic pathway analysis, the edges of the high-dimensional flux cone are vectors that correspond to systemically defined "extreme pathways" spanning the capabilities of the system. The addition of maximum flux capacities of individual metabolic reactions serves to further constrain the flux space and has led to the development of flux balance analysis using linear optimization to calculate optimal flux distributions. Here we provide the precise theoretical connections between pathway analysis and flux balance analysis allowing for their combined application to study integrated metabolic function. Shifts in metabolic behavior are calculated using linear optimization and are then interpreted using the extreme pathways to demonstrate the concept of pathway utilization. Changes to the reaction network, such as the removal of a reaction, can lead to the generation of suboptimal phenotypes that can be directly attributed to the loss of pathway function and capabilities. Optimal growth phenotypes are calculated as a function of environmental variables, such as the availability of substrate and oxygen, leading to the definition of phenotypic phase planes. It is illustrated how optimality properties of the computed flux distributions can be interpreted in terms of the extreme pathways. Together these developments are applied to an example network and to core metabolism of Escherichia coli demonstrating the connections between the extreme pathways, optimal flux distributions, and phenotypic phase planes. The consequences of changing environmental and internal conditions of the network are examined for growth on glucose and succinate in the face of a variety of gene deletions. The convergence of the calculation of optimal phenotypes through linear programming and the definition of extreme pathways establishes a different perspective for the understanding of how a defined metabolic network is best used under different environmental and internal conditions or, in other words, a pathway basis for the interpretation of the metabolic reaction norm.     

Mapping of signaling networks through synthetic genetic interaction analysis by RNAi

The analysis of synthetic genetic interaction networks can reveal how biological systems achieve a high level of complexity with a limited repertoire of components. Studies in yeast and bacteria have taken advantage of collections of deletion strains to construct matrices of quantitative interaction profiles and infer gene function. Yet comparable approaches in higher organisms have been difficult to implement in a robust manner. Here we report a method to identify genetic interactions in tissue culture cells through RNAi. By performing more than 70,000 pairwise perturbations of signaling factors, we identified >600 interactions affecting different quantitative phenotypes of Drosophila melanogaster cells. Computational analysis of this interaction matrix allowed us to reconstruct signaling pathways and identify a conserved regulator of Ras-MAPK signaling. Large-scale genetic interaction mapping by RNAi is a versatile, scalable approach for revealing gene function and the connectivity of cellular networks.     

Dynamical and integrative cell signaling: challenges for the new biology

Years of careful experimental analysis have revealed that signaling molecules are organized into complex networks of biochemical reactions exquisitely regulated in time and space to provide a cell with high-fidelity information about an extremely noisy and volatile environment. A new view of signaling networks as systems consisting of multiple complex elements interacting in a multifarious fashion is emerging, a view that conflicts with the single-gene or protein-centric approach common in biological research. The postgenomic era has brought about a different, network-centric methodology of analysis, suddenly forcing researchers toward the opposite extreme of complexity, where the networks being explored are, to a certain extent, intractable and uninterpretable. Both the cartoons of simple pathways and the very large "hair-ball" diagrams of large intracellular networks are also representations of static worlds, superficially devoid of dynamics and chemistry. These representations are often viewed as being analogous to stably linked computer and neural networks rather than dynamically changing networks of chemical interactions, where the notions of concentration, compartmentalization, and diffusion may be the primary determinants of connectivity. Arguably, the systems biology approach, relying on computational modeling coupled with various experimental techniques and methodologies, will be an essential component of analysis of the behavior of signal transduction pathways. Combining the dynamical view of rapidly evolving responses and the structural view arising from high-throughput analyses of the interacting species will be the best approach toward efforts toward greater understanding of intracellular signaling processes.     

Mathematical models of specificity in cell signaling

Cellular signaling pathways transduce extracellular signals into appropriate responses. These pathways are typically interconnected to form networks, often with different pathways sharing similar or identical components. A consequence of this connectedness is the potential for cross talk, some of which may be undesirable. Indeed, experimental evidence indicates that cells have evolved insulating mechanisms to partially suppress "leaking" between pathways. Here we characterize mathematical models of simple signaling networks and obtain exact analytical expressions for two measures of cross talk called specificity and fidelity. The performance of several insulating mechanisms--combinatorial signaling, compartmentalization, the inhibition of one pathway by another, and the selective activation of scaffold proteins--is evaluated with respect to the trade-off between the specificity they provide and the constraints they place on the network. The effects of noise are also examined. The insights gained from this analysis are applied to understanding specificity in the yeast mating and invasive growth MAP kinase signaling network.     

Extreme pathway analysis of human red blood cell metabolism

The development of high-throughput technologies and the resulting large-scale data sets have necessitated a systems approach to the analysis of metabolic networks. One way to approach the issue of complex metabolic function is through the calculation and interpretation of extreme pathways. Extreme pathways are a mathematically defined set of generating vectors that describe the conical steady-state solution space for flux distributions through an entire metabolic network. Herein, the extreme pathways of the well-characterized human red blood cell metabolic network were calculated and interpreted in a biochemical and physiological context. These extreme pathways were divided into groups based on such criteria as their cofactor and by-product production, and carbon inputs including those that 1) convert glucose to pyruvate; 2) interchange pyruvate and lactate; 3) produce 2,3-diphosphoglycerate that binds to hemoglobin; 4) convert inosine to pyruvate; 5) induce a change in the total adenosine pool; and 6) dissipate ATP. Additionally, results from a full kinetic model of red blood cell metabolism were predicted based solely on an interpretation of the extreme pathway structure. The extreme pathways for the red blood cell thus give a concise representation of red blood cell metabolism and a way to interpret its metabolic physiology.     

Model-based analysis of interferon-beta induced signaling pathway

MOTIVATION: Interferon-beta induced JAK-STAT signaling pathways contribute to mucosal immune recognition and an anti-viral state. Though the main molecular mechanisms constituting these pathways are known, neither the detailed structure of the regulatory network, nor its dynamics has yet been investigated. The objective of this work is to build a mathematical model for the pathway that would serve two purposes: (1) to reproduce experimental results in simulation of both early and late response to Interferon-beta stimulation and (2) to explain experimental phenomena generating new hypotheses about regulatory mechanisms that cannot yet be tested experimentally. RESULTS: Experimentally determined time dependent changes in the major components of this pathway were used to build a mathematical model describing pathway dynamics in the form of ordinary differential equations. The experimental results suggested existence of unknown negative control mechanisms that were tested numerically using the model. Together, experimental and numerical data show that the epithelial JAK-STAT pathway might be subjected to previously unknown dynamic negative control mechanisms: (1) activation of dormant phosphatases and (2) inhibition of nuclear import of IRF1.     

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.     

干扰素信号传导通路与其基因组多态性网络模型的建立

通过检索Pubmed、Embase数据库, 整合文献资料, 运用Teranode Design Suite(TDS)生物软件, 建立干扰素(IFN)发挥生物学作用的信号传导通路。应用SNP Trawler软件搜索通路中相关基因的SNP信息, 并以属性形式在通路模型中给以显示。结果构建了融合基因的遗传信息尤其是SNP相关信息的IFN发挥生物学作用的信号传导通路的网络模型, 此模型共包含JAK-STAT, MAPK-p38和PI3K 3条主要传导通路, 干扰素通过不同的信号传导通路发挥不同的生物活性, 其中Ⅰ型干扰素通过这3条通路发挥抗病毒、抗细胞增殖及免疫调节等重要作用, 而Ⅱ型干扰素则通过JAK-STAT和MAPK-p38路径发挥生物学作用, Ⅲ型干扰素则仅通过PI3K通路发挥生物学作用; 这3条传导通路包含98个基因和19 693个SNPs, 组成了复杂的基因间相互作用网络。此通路模型的成功建立, 一方面为研究SNP对IFN生物学作用的影响而预测IFN疗效提供理论基础, 另一方面为实现个体化诊疗、发现新的药物作用靶点及开发新的药物奠定基础。     

The immune signaling pathways of Manduca sexta

Signal transduction pathways and their coordination are critically important for proper functioning of animal immune systems. Our knowledge of the constituents of the intracellular signaling network in insects mainly comes from genetic analyses in Drosophila melanogaster. To facilitate future studies of similar systems in the tobacco hornworm and other lepidopteran insects, we have identified and examined the homologous genes in the genome of Manduca sexta. Based on 1:1 orthologous relationships in most cases, we hypothesize that the Toll, Imd, MAPK-JNK-p38 and JAK-STAT pathways are intact and operative in this species, as are most of the regulatory mechanisms. Similarly, cellular processes such as autophagy, apoptosis and RNA interference probably function in similar ways, because their mediators and modulators are mostly conserved in this lepidopteran species. We have annotated a total of 186 genes encoding 199 proteins, studied their domain structures and evolution, and examined their mRNA levels in tissues at different life stages. Such information provides a genomic perspective of the intricate signaling system in a non-drosophiline insect.     

FluxAnalyzer: exploring structure,pathways, and flux distributions in metabolic networks on interactive flux maps

MOTIVATION: The analysis of structure, pathways and flux distributions in metabolic networks has become an important approach for understanding the functionality of metabolic systems. The need of a user-friendly platform for stoichiometric modeling of metabolic networks in silico is evident. RESULTS: The FluxAnalyzer is a package for MATLAB and facilitates integrated pathway and flux analysis for metabolic networks within a graphical user interface. Arbitrary metabolic network models can be composed by instances of four types of network elements. The abstract network model is linked with network graphics leading to interactive flux maps which allow for user input and display of calculation results within a network visualization. Therein, a large and powerful collection of tools and algorithms can be applied interactively including metabolic flux analysis, flux optimization, detection of topological features and pathway analysis by elementary flux modes or extreme pathways. The FluxAnalyzer has been applied and tested for complex networks with more than 500,000 elementary modes. Some aspects of the combinatorial complexity of pathway analysis in metabolic networks are discussed. AVAILABILITY: Upon request from the corresponding author. Free for academic users (license agreement). Special contracts are available for industrial corporations. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer.