First Passage and Cooperativity of Queuing Kinetics

We model the kinetics of ligand-receptor systems, where multiple ligands may bind and unbind to the receptor, either randomly or in a specific order. Equilibrium occupation and first occurrence of complete filling of the receptor are determined and compared. At equilibrium, receptors that bind ligands sequentially are more likely to be saturated than those that bind in random order. Surprisingly however, for low cooperativity, the random process first reaches full occupancy faster than the sequential one. This is true {\it except} near a critical binding energy where a 'kinetic trap' arises and the random process dramatically slows down when the number of binding sites $N\geq 8$. These results demonstrate the subtle interplay between cooperativity and sequentiality for a wide class of kinetic phenomena, including chemical binding, nucleation, and assembly line strategies.Comment: 5pp, 5 figure

Integration of a Phosphatase Cascade with the MAP Kinase Pathway provides for a Novel Signal Processing Function

We mathematically modeled the receptor-activated MAP kinase signaling by incorporating the regulation through cellular phosphatases. Activation induced the alignment of a phosphatase cascade in parallel with the MAP kinase pathway. A novel regulatory motif was thus generated, providing for the combinatorial control of each MAPK intermediate. This ensured a non-linear mode of signal transmission with the output being shaped by the balance between the strength of input signal, and the activity gradient along the phosphatase axis. Shifts in this balance yielded modulations in topology of the motif, thereby expanding the repertoire of output responses. Thus we identify an added dimension to signal processing, wherein the output response to an external stimulus is additionally filtered through indicators that define the phenotypic status of the cell.Comment: Whole Manuscript 33 pages inclduing Main text, 7 Figures and Supporting Informatio

Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

<p>Abstract</p> <p>Background</p> <p>In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior.</p> <p>Results</p> <p>We introduce <it>PathwayOracle</it>, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates <it>PathwayOracle </it>from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, <it>PathwayOracle </it>includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model.</p> <p>Conclusion</p> <p><it>PathwayOracle </it>provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models. The application has been developed in Python and is designed to be easily extensible by groups interested in adding new or extending existing features. <it>PathwayOracle </it>is freely available for download and use.</p

Dynamics of growth factor production in monolayers of cancer cells and evolution of resistance to anticancer therapies

Tumor heterogeneity is well documented for many characters, including the production of growth factors, which improve tumor proliferation and promote resistance against apoptosis and against immune reaction. What maintains heterogeneity remains an open question that has implications for diagnosis and treatment. While it has been suggested that therapies targeting growth factors are robust against evolved resistance, current therapies against growth factors, like antiangiogenic drugs, are not effective in the long term, as resistant mutants can evolve and lead to relapse. We use evolutionary game theory to study the dynamics of the production of growth factors by monolayers of cancer cells and to understand the effect of therapies that target growth factors. The dynamics depend on the production cost of the growth factor, on its diffusion range and on the type of benefit it confers to the cells. Stable heterogeneity is a typical outcome of the dynamics, while a pure equilibrium of nonproducer cells is possible under certain conditions. Such pure equilibrium can be the goal of new anticancer therapies. We show that current therapies, instead, can be effective only if growth factors are almost completely eliminated and if the reduction is almost immediate

Information flow during gene activation by signaling molecules: ethylene transduction in Arabidopsis cells as a study system

<p>Abstract</p> <p>Background</p> <p>We study root cells from the model plant <it>Arabidopsis thaliana </it>and the communication channel conformed by the ethylene signal transduction pathway. A basic equation taken from our previous work relates the probability of expression of the gene <it>ERF</it>1 to the concentration of ethylene.</p> <p>Results</p> <p>The above equation is used to compute the Shannon entropy (<it>H</it>) or degree of uncertainty that the genetic machinery has during the decoding of the message encoded by the ethylene specific receptors embedded in the endoplasmic reticulum membrane and transmitted into the nucleus by the ethylene signaling pathway. We show that the amount of information associated with the expression of the master gene <it>ERF</it>1 (Ethylene Response Factor 1) can be computed. Then we examine the system response to sinusoidal input signals with varying frequencies to determine if the cell can distinguish between different regimes of information flow from the environment. Our results demonstrate that the amount of information managed by the root cell can be correlated with the frequency of the input signal.</p> <p>Conclusion</p> <p>The ethylene signaling pathway cuts off very low and very high frequencies, allowing a window of frequency response in which the nucleus reads the incoming message as a sinusoidal input. Out of this window the nucleus reads the input message as an approximately non-varying one. From this frequency response analysis we estimate: a) the gain of the system during the synthesis of the protein ERF1 (~-5.6 dB); b) the rate of information transfer (0.003 bits) during the transport of each new ERF1 molecule into the nucleus and c) the time of synthesis of each new ERF1 molecule (~21.3 s). Finally, we demonstrate that in the case of the system of a single master gene (<it>ERF</it>1) and a single slave gene (<it>HLS</it>1), the total Shannon entropy is completely determined by the uncertainty associated with the expression of the master gene. A second proposition shows that the Shannon entropy associated with the expression of the <it>HLS</it>1 gene determines the information content of the system that is related to the interaction of the antagonistic genes <it>ARF</it>1, 2 and <it>HLS</it>1.</p

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods

RegPhos: a system to explore the protein kinase–substrate phosphorylation network in humans

Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. With the increasing number of experimental phosphorylation sites that has been identified by mass spectrometry-based proteomics, the desire to explore the networks of protein kinases and substrates is motivated. Manning et al. have identified 518 human kinase genes, which provide a starting point for comprehensive analysis of protein phosphorylation networks. In this study, a knowledgebase is developed to integrate experimentally verified protein phosphorylation data and protein–protein interaction data for constructing the protein kinase–substrate phosphorylation networks in human. A total of 21 110 experimental verified phosphorylation sites within 5092 human proteins are collected. However, only 4138 phosphorylation sites (∼20%) have the annotation of catalytic kinases from public domain. In order to fully investigate how protein kinases regulate the intracellular processes, a published kinase-specific phosphorylation site prediction tool, named KinasePhos is incorporated for assigning the potential kinase. The web-based system, RegPhos, can let users input a group of human proteins; consequently, the phosphorylation network associated with the protein subcellular localization can be explored. Additionally, time-coursed microarray expression data is subsequently used to represent the degree of similarity in the expression profiles of network members. A case study demonstrates that the proposed scheme not only identify the correct network of insulin signaling but also detect a novel signaling pathway that may cross-talk with insulin signaling network. This effective system is now freely available at http://RegPhos.mbc.nctu.edu.tw

Unraveling the Design Principle for Motif Organization in Signaling Networks

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

Swimming Upstream: Identifying Proteomic Signals that Drive Transcriptional Changes using the Interactome and Multiple “-Omics” Datasets

available in PMC 2013 December 23Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, “-omics” methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments “constraints” using previously reported protein–protein and protein–DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions “optimization”. A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.National Cancer Institute (U.S.) ((NCI) Grant U54-CA112967)Natural Sciences and Engineering Research Council of Canada (Postgraduate scholarship)Massachusetts Institute of Technology (Eugene Bell Career Development Chair)National Cancer Institute (U.S.) (NCI integrative cancer biology program graduate fellowship

The mEPN scheme: an intuitive and flexible graphical system for rendering biological pathways

<p>Abstract</p> <p>Background</p> <p>There is general agreement amongst biologists about the need for good pathway diagrams and a need to formalize the way biological pathways are depicted. However, implementing and agreeing how best to do this is currently the subject of some debate.</p> <p>Results</p> <p>The modified Edinburgh Pathway Notation (mEPN) scheme is founded on a notation system originally devised a number of years ago and through use has now been refined extensively. This process has been primarily driven by the author's attempts to produce process diagrams for a diverse range of biological pathways, particularly with respect to immune signaling in mammals. Here we provide a specification of the mEPN notation, its symbols, rules for its use and a comparison to the proposed Systems Biology Graphical Notation (SBGN) scheme.</p> <p>Conclusions</p> <p>We hope this work will contribute to the on-going community effort to develop a standard for depicting pathways and will provide a coherent guide to those planning to construct pathway diagrams of their biological systems of interest.</p