Model-based inference of biochemical parameters and dynamic properties of microbial signal transduction networks

Because of the inherent uncertainty about quantitative aspects of signalling networks it is of substantial interest to use computational methods that allow inferring non-measurable quantities such as rate constants, from measurable quantities such as changes in protein abundances. We argue that true biochemical parameters like rate constants can generally not be inferred using models due to their non-identifiability. Recent advances, however, facilitate the analysis of parameter identifiability of a given model and automated discrimination of candidate models, both being important techniques to still extract quantitative biological information from experimental data.     

Bioinformatics analyses for signal transduction networks

Research in signaling networks contributes to a deeper understanding of organism living activities. With the development of experimental methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. This paper introduces such popular bioin-formatics analysis methods for signaling networks as the common mechanism of signaling pathways and database resource on the Internet, summerizes the methods of analyzing the structural properties of networks, including structural Motif finding and automated pathways generation, and discusses the modeling and simulation of signaling networks in detail, as well as the research situation and tendency in this area. Now the investigation of signal transduction is developing from small-scale experiments to large-scale network analysis, and dynamic simulation of networks is closer to the real system. With the investigation going deeper than ever, the bioinformatics analysis of signal transduction would have immense space for development and application.     

Expansion of signal transduction networks

Signal transduction networks of different cell types show a large variety in their structural design. In this paper, basic structural properties of signal transduction networks are investigated. For this, such networks with the recently developed method of network expansion are analysed. This method allows for a structural analysis of networks by calculating signal expansion profiles when provided with certain compounds, for example growth factors, inactive kinases and so on (seed compounds), to initiate such an expansion. The presented results may put forth valuable hints on the evolution of signalling networks.     

Chemical approaches for investigating phosphorylation in signal transduction networks

The power and scope of chemical synthesis offer considerable opportunities to broaden the lexicon of chemical tools that can be implemented for the study of complex biological systems. To investigate individual signaling proteins and pathways, chemical tools provide a powerful complement to existing genetic, chemical genetic and immunologic methods. In particular, understanding phosphorylation-mediated signaling in real time yields important information about the regulation of cellular function and insights into the origin of disease. Recent advances in the development of photolabile caged analogs of bioactive species and fluorescence-based sensors of protein kinase activities are useful for investigating protein phosphorylation and the roles of phosphoproteins. Photolabile caged analogs allow spatial and temporal control over the release of a compound, while fluorescence-based sensors allow the real-time visualization of kinase activity. Here, we discuss recent advances that have increased the specificity and availability of these tools.     

Is autoinducer-2 a universal signal for interspecies communication: a comparative genomic and phylogenetic analysis of the synthesis and signal transduction pathways

Background  

Quorum sensing is a process of bacterial cell-to-cell communication involving the production and detection of extracellular signaling molecules called autoinducers. Recently, it has been proposed that autoinducer-2 (AI-2), a furanosyl borate diester derived from the recycling of S-adenosyl-homocysteine (SAH) to homocysteine, serves as a universal signal for interspecies communication.     

PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

Background  

A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.     

Nonparametric simulation of signal transduction networks with semi-synchronized update

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.     

An investigation of spatial signal transduction in cellular networks

ABSTRACT: BACKGROUND: Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation, cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary. RESULTS: In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling. CONCLUSIONS: Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.     

Towards an understating of signal transduction protein interaction networks

Protein network analysis has witnessed a number of advancements in the past for understanding molecular characteristics for important network topologies in biological systems. The signaling pathway regulates cell cycle progression and anti-apoptotic molecules. This pathway is also involved in maintaining cell survival by modulating the activity of apoptosis through RAS, P13K, AKT and BAD activities. The importance of protein-protein interactions to improve usability of the interactome by scoring and ranking interaction data for proteins in signal transduction networks is illustrated using available data and resources.     

Continuous recycling: a mechanism for modulatory signal transduction

Modulatory signal transduction commonly requires efficient "on demand" assembly of specific multicomponent cellular machines that convert signals to cellular actions. This article suggests that for these signaling machines to detect and respond to fluctuations in signal strength, they must be continuously disassembled in an energy-dependent process that probably involves molecular chaperones.     

BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains

BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction.     

Funneled landscape leads to robustness of cellular networks: MAPK signal transduction

We uncover the underlying potential energy landscape for a cellular network. We find that the potential energy landscape of the mitogen-activated protein-kinase signal transduction network is funneled toward the global minimum. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. Funneled landscape is a realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.     

Rhodopsin: structure, signal transduction and oligomerisation

Rhodopsin was the first G protein-coupled receptor (GPCR) for which a high-resolution crystal structure was obtained. Several crystal structures have now been solved representing different activation states of the receptor. These structures, together with those from lower resolution techniques (e.g. electron microscopy), shed light on the stepwise process by which energy from an extracellular photon is transduced across the membrane to the intracellular compartment thereby activating signalling mechanisms responsible for very low-level light detection. Controversy remains in several areas including: (i) transmembrane helix movements responsible for the transduction process, (ii) the stoichiometry of coupling to G proteins and their mode of activation, (iii) the role, if any, of receptor oligomerisation and (iv) the suitability of using structures of this GPCR as templates for modelling the structures of other GPCRs, and their mechanisms of activation.     

SENTRA, a database of signal transduction proteins

SENTRA, available via URL http://wit.mcs.anl.gov/WIT2/Sentra/, is a database of proteins associated with microbial signal transduction. The database currently includes the classical two-component signal transduction pathway proteins and methyl-accepting chemotaxis proteins, but will be expanded to also include other classes of signal transduction systems that are modulated by phosphorylation or methylation reactions. Although the majority of database entries are from prokaryotic systems, eukaroytic proteins with bacterial-like signal transduction domains are also included. Currently SENTRA contains signal transduction proteins in 34 complete and almost completely sequenced prokaryotic genomes, as well as sequences from 243 organisms available in public databases (SWISS-PROT and EMBL). The analysis was carried out within the framework of the WIT2 system, which is designed and implemented to support genetic sequence analysis and comparative analysis of sequenced genomes.