Modeling specificity in the yeast MAPK signaling networks

Cells sense several kinds of stimuli and trigger corresponding responses through signaling pathways. As a result, cells must process and integrate multiple signals in parallel to maintain specificity and avoid erroneous cross-talk. In this study, we focus our theoretical effort on understanding specificity of a model network system in yeast, Saccharomyces cerevisiae, which contains three mitogen-activated protein kinase (MAPK) signal transduction cascades that share multiple signaling components. The cellular response to the pheromone, the filamentous growth and osmotic pressure stimuli in yeast is described and an integrative mathematical model for the three MAPK cascades is developed using available literature and experimental data. The theoretical framework for analyzing the specificity of signaling networks [Bardwell, L., Zou, X.F., Nie, Q., Komarova, N.L., 2007. Mathematical models of specificity in cell signaling. Biophys. J. 92, 3425-3441] is extended to include multiple interacting pathways with shared components. Simulations are also performed with any one stimulus, with any two simultaneous stimuli, and with the simultaneous application of the three stimuli. The interactions between the three pathways are systematically investigated. Moreover, the specificity and fidelity of this model system are calculated using our newly developed concept under different stimuli or with specific mutants. Our simulated and calculated results demonstrate that the yeast MAPK signaling network can achieve specificity and fidelity by filtering out spurious cross-talk between the relevant pathways through different mechanisms, such as scaffolding, cross-inhibiting, and feedback control. Proof that Pbs2 and Hog1 are essential for the maintenance of signaling specificity is presented. Our studies provide novel insights into integration of relevant signaling pathways in a biological system and the mechanisms conferring specificity in cellular signaling networks.     

Cell signaling networks ontology

Although databases for cell signaling pathways include numbers of reaction data of the pathways, the reaction data cannot be used yet to deduce biological functions from them. For the deduction, we need systematic and consistent interpretation of biological functions of reactions in cell signaling pathways in the context of "information transmission". To address this issue, we have developed a functional ontology for cell signaling pathways, Cell Signaling Network Ontology (CSN-Ontology), which provides framework for the functional interpretation presenting some important concepts as information, selectivity, movability, and signaling rules including passage of time.     

Fluorescence imaging of signaling networks

Receptor-triggered signaling processes exhibit complex cross-talk and feedback interactions, with many signaling proteins and second messengers acting locally within the cell. The flow of information in this input-output system can only be understood by tracking where and when local signaling activities are induced. Systematic strategies are therefore needed to measure the localization and translocation of all signaling proteins, and to develop fluorescent biosensors that can track local signaling activities in individual cells. Such a biosensor tool chest can be based on two types of green fluorescent protein constructs that either translocate or undergo fluorescence-resonance-energy transfer when local signaling occurs. Broad strategies to measure quantitative, dynamic parameters in signaling networks, together with perturbation approaches, are needed to develop comprehensive models of signaling networks*.     

Quantitative analysis of signaling networks

The response of biological cells to environmental change is coordinated by protein-based signaling networks. These networks are to be found in both prokaryotes and eukaryotes. In eukaryotes, the signaling networks can be highly complex, some networks comprising of 60 or more proteins. The fundamental motif that has been found in all signaling networks is the protein phosphorylation/dephosphorylation cycle--the cascade cycle. At this time, the computational function of many of the signaling networks is poorly understood. However, it is clear that it is possible to construct a huge variety of control and computational circuits, both analog and digital from combinations of the cascade cycle. In this review, we will summarize the great versatility of the simple cascade cycle as a computational unit and towards the end give two examples, one prokaryotic chemotaxis circuit and the other, the eukaryotic MAPK cascade.     

Advancing signaling networks through proteomics

Healthful physiology can be distinguished from unhealthful physiology by focusing upon how a given signal transduction pathway is shifted as a function of disease. In order to distinguish between pathways that contribute to normal versus disease biology, it is necessary to identify components that comprise a protein module. The development of methods that target such differences is essential for the identification, development and validation of biomarkers that can improve the quality of diagnoses and treatment of disease. This review discusses the use of proteomic methods that integrate cell biology, mass spectrometry and bioinformatics, in relation to the analyses of protein signaling modules that are subject to differential phosphorylation. We examine how these methods can be used to distinguish abnormal from normal physiology.     

Applying proteomics to signaling networks

The information from genome sequencing provides a new framework for a systems-wide understanding of protein networks and cellular function. Whereas microarray technologies provide information about global gene expression within cells, complementary proteomic strategies monitor expression of proteins and their posttranslational modifications. Improved technologies that have emerged for comprehensive and high-throughput protein analysis yield novel insights into cell regulation.     

Advancing signaling networks through proteomics

Healthful physiology can be distinguished from unhealthful physiology by focusing upon how a given signal transduction pathway is shifted as a function of disease. In order to distinguish between pathways that contribute to normal versus disease biology, it is necessary to identify components that comprise a protein module. The development of methods that target such differences is essential for the identification, development and validation of biomarkers that can improve the quality of diagnoses and treatment of disease. This review discusses the use of proteomic methods that integrate cell biology, mass spectrometry and bioinformatics, in relation to the analyses of protein signaling modules that are subject to differential phosphorylation. We examine how these methods can be used to distinguish abnormal from normal physiology.     

Homologous control of protein signaling networks

In a previous paper we introduced a method called augmented sparse reconstruction (ASR) that identifies links among nodes of ordinary differential equation networks, given a small set of observed trajectories with various initial conditions. The main purpose of that technique was to reconstruct intracellular protein signaling networks.In this paper we show that a recursive augmented sparse reconstruction generates artificial networks that are homologous to a large, reference network, in the sense that kinase inhibition of several reactions in the network alters the trajectories of a sizable number of proteins in comparable ways for reference and reconstructed networks. We show this result using a large in-silico model of the epidermal growth factor receptor (EGF-R) driven signaling cascade to generate the data used in the reconstruction algorithm.The most significant consequence of this observed homology is that a nearly optimal combinatorial dosage of kinase inhibitors can be inferred, for many nodes, from the reconstructed network, a result potentially useful for a variety of applications in personalized medicine.     

Non-linear dimensionality reduction of signaling networks

Background  

Systems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling.     

Dose-to-duration encoding and signaling beyond saturation in intracellular signaling networks

The cellular response elicited by an environmental cue typically varies with the strength of the stimulus. For example, in the yeast Saccharomyces cerevisiae, the concentration of mating pheromone determines whether cells undergo vegetative growth, chemotropic growth, or mating. This implies that the signaling pathways responsible for detecting the stimulus and initiating a response must transmit quantitative information about the intensity of the signal. Our previous experimental results suggest that yeast encode pheromone concentration as the duration of the transmitted signal. Here we use mathematical modeling to analyze possible biochemical mechanisms for performing this “dose-to-duration” conversion. We demonstrate that modulation of signal duration increases the range of stimulus concentrations for which dose-dependent responses are possible; this increased dynamic range produces the counterintuitive result of “signaling beyond saturation” in which dose-dependent responses are still possible after apparent saturation of the receptors. We propose a mechanism for dose-to-duration encoding in the yeast pheromone pathway that is consistent with current experimental observations. Most previous investigations of information processing by signaling pathways have focused on amplitude encoding without considering temporal aspects of signal transduction. Here we demonstrate that dose-to-duration encoding provides cells with an alternative mechanism for processing and transmitting quantitative information about their surrounding environment. The ability of signaling pathways to convert stimulus strength into signal duration results directly from the nonlinear nature of these systems and emphasizes the importance of considering the dynamic properties of signaling pathways when characterizing their behavior. Understanding how signaling pathways encode and transmit quantitative information about the external environment will not only deepen our understanding of these systems but also provide insight into how to reestablish proper function of pathways that have become dysregulated by disease.     

Quantitative phosphoproteomics to characterize signaling networks

Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform for analyzing protein phosphorylation at a system-wide scale and has become the intuitive strategy for comprehensive characterization of signaling networks. Contemporary phosphoproteomics use highly optimized procedures for sample preparation, mass spectrometry and data analysis algorithms to identify and quantify thousands of phosphorylations, thus providing extensive overviews of the cellular signaling networks. As a result of these developments quantitative phosphoproteomics have been applied to study processes as diverse as immunology, stem cell biology and DNA damage. Here we review the developments in phosphoproteomics technology that have facilitated the application of phosphoproteomics to signaling networks and introduce examples of recent system-wide applications of quantitative phosphoproteomics. Despite the great advances in phosphoproteomics technology there are still several outstanding issues and we provide here our outlook on the current limitations and challenges in the field.     

MicroRNA-modulated autophagic signaling networks in cancer

MicroRNAs (miRNAs) are small, non-coding endogenous RNAs ～22 nucleotides (nt) in length that may play the essential roles for regulation of programed cell death, referring to apoptosis and autophagy. Of note, autophagy is an evolutionarily conserved, multi-step lysosomal degradation process in which a cell degrades long-lived proteins and damaged organelles. Accumulating evidence has recently revealed that miRNAs can modulate the autophagic pathways in many pathological processes, most notably cancer. In this review, we focus on highlighting the dual functions of miRNAs as either oncogenes (e.g., miRNA-183, miRNA-376b, miRNA-106a, miRNA-221/222, miRNA-31 and miRNA-34c) or tumor suppressors (e.g., miRNA-30a, miRNA-101 and miRNA-9*) via mediating several autophagic signaling pathways in cancer pathogenesis. Taken together, these findings may uncover the regulatory mechanisms of oncogenic and tumor suppressive miRNAs in autophagy, which would provide a better understanding of miRNA-modulated autophagic signaling networks for future cancer therapeutics.     

Cold stress signaling networks in Arabidopsis

Cold is one of the critical environmental conditions that negatively affects plant growth and development and determines the geographic distribution of plants. Cold stress signaling is dynamic and interacts with many other signal transduction pathways to efficiently cope with adverse stress effects in plants. The cold signal is primarily perceived via Ca²⁺ channel proteins, membrane histidine kinases, or unknown sensors, which then activate the sophisticated cold-responsive signaling pathways in concert with phytohormone signaling, the circadian clock, and the developmental transition to flowering, as a part of the stress adaptation response. In this review, we focus on crosstalk between cold signaling and other signal transduction pathways in Arabidopsis.