Hypothesis testing in animal social networks

Behavioural ecologists are increasingly using social network analysis to describe the social organisation of animal populations and to test hypotheses. However, the statistical analysis of network data presents a number of challenges. In particular the non-independent nature of the data violates the assumptions of many common statistical approaches. In our opinion there is currently confusion and uncertainty amongst behavioural ecologists concerning the potential pitfalls when hypotheses testing using social network data. Here we review what we consider to be key considerations associated with the analysis of animal social networks and provide a practical guide to the use of null models based on randomisation to control for structure and non-independence in the data.     

A database for cell signaling networks.

We developed a data and knowledge base for cellular signal transduction in human cells, to make this rapidly growing information available. The database includes all the biological properties of cellular signal transduction, including biological reactions that transfer cellular signals and molecular attributes characterized by sequences, structures, and functions. Since the database is based on the object-oriented technique, highly flexible methods of data definition and modification are necessary to handle this diverse and complex biological information. The database includes attractive graphical representations of signaling cascades and the three-dimensional structure of molecules. The database is a novel application of ACEDB, which was the database originally developed to store the C. elegans genome. The database can be accessed through the Internet at http://geo.nihs.go.jp/csndb.html.     

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.     

Modeling of signaling networks

Biochemical networks, including those containing signaling pathways, display a wide range of regulatory properties. These include the ability to propagate information across different time scales and to function as switches and oscillators. The mechanisms underlying these complex behaviors involve many interacting components and cannot be understood by experiments alone. The development of computational models and the integration of these models with experiments provide valuable insight into these complex systems-level behaviors. Here we review current approaches to the development of computational models of biochemical networks and describe the insights gained from models that integrate experimental data, using three examples that deal with ultrasensitivity, flexible bistability and oscillatory behavior. These types of complex behavior from relatively simple networks highlight the necessity of using theoretical approaches in understanding higher order biological functions.     

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.     

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.     

Protein kinase signaling networks in cancer

Protein kinases orchestrate the activation of signaling cascades in response to extracellular and intracellular stimuli to control cell growth, proliferation, and survival. The complexity of numerous intracellular signaling pathways is highlighted by the number of kinases encoded by the human genome (539) and the plethora of phosphorylation sites identified in phosphoproteomic studies. Perturbation of these signaling networks by mutations or abnormal protein expression underlies the cause of many diseases including cancer. Recent RNAi screens and cancer genomic sequencing studies have revealed that many more kinases than anticipated contribute to tumorigenesis and are potential targets for inhibitor drug development intervention. This review will highlight recent insights into known pathways essential for tumorigenesis and discuss exciting new pathways for therapeutic intervention.     

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.     

Efficient algorithms for detecting signaling pathways in protein interaction networks.

The interpretation of large-scale protein network data depends on our ability to identify significant substructures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths and trees in graphs to the problem of identifying pathways in protein interaction networks. We present linear-time algorithms for finding paths and trees in networks under several biologically motivated constraints. We apply our methodology to search for protein pathways in the yeast protein-protein interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths and trees in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in about three hours.     

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.     

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*.     

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.     

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.     

Regulation patterns in signaling networks of cancer

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.     

Extending signaling pathways with protein-interaction networks. Application to apoptosis

Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.