Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network

Department of Biological Sciences, Columbia University, New York, New York, United States
Science (Impact Factor: 33.61). 09/2005; 309(5737):1078-83. DOI: 10.1126/science.1108876
Source: PubMed


We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines
in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification
of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs,
such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were
highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs
in determining cellular choices between homeostasis and plasticity.

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Available from: Gustavo Stolovitzky
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    • "The upper bound on the number of edge modifications that are required in response to the deregulation of a single node is equal to the node's out-degree. In contrast to the peaked out-degree distribution of random Boolean networks, large-scale biological networks have long-tailed out-degree distributions [53,54]. Clearly, the loss of a high-degree node may require many compensatory edge modifications. "
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    ABSTRACT: Background Understanding and ameliorating the effects of network damage are of significant interest, due in part to the variety of applications in which network damage is relevant. For example, the effects of genetic mutations can cascade through within-cell signaling and regulatory networks and alter the behavior of cells, possibly leading to a wide variety of diseases. The typical approach to mitigating network perturbations is to consider the compensatory activation or deactivation of system components. Here, we propose a complementary approach wherein interactions are instead modified to alter key regulatory functions and prevent the network damage from triggering a deregulatory cascade. Results We implement this approach in a Boolean dynamic framework, which has been shown to effectively model the behavior of biological regulatory and signaling networks. We show that the method can stabilize any single state (e.g., fixed point attractors or time-averaged representations of multi-state attractors) to be an attractor of the repaired network. We show that the approach is minimalistic in that few modifications are required to provide stability to a chosen attractor and specific in that interventions do not have undesired effects on the attractor. We apply the approach to random Boolean networks, and further show that the method can in some cases successfully repair synchronous limit cycles. We also apply the methodology to case studies from drought-induced signaling in plants and T-LGL leukemia and find that it is successful in both stabilizing desired behavior and in eliminating undesired outcomes. Code is made freely available through the software package BooleanNet. Conclusions The methodology introduced in this report offers a complementary way to manipulating node expression levels. A comprehensive approach to evaluating network manipulation should take an "all of the above" perspective; we anticipate that theoretical studies of interaction modification, coupled with empirical advances, will ultimately provide researchers with greater flexibility in influencing system behavior.
    Full-text · Article · May 2014 · BMC Systems Biology
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    • "Network id reference Average degree δ + ave (G) δ + worst (G) D δ + worst (G) D /2 1. E. coli transcriptional [25] 1.45 0.132 2 10 0.400 2. Mammalian Signaling [26] 2.04 0.013 3 11 0.545 3. E. Coli transcriptional ♯ ♯ ♯ 1.30 0.043 2 13 0.308 4. T LGL signaling [27] 2.32 0.297 2 7 0.571 5. S. cerevisiae transcriptional [28] 1.56 0.004 3 15 0.400 6. C. elegans Metabolic [29] 4.50 0.010 1.5 7 0.429 7. Drosophila segment polarity [30] 1.69 0.676 4 9 0.889 8. ABA signaling [31] 1.60 0.302 2 7 0.571 9. Immune Response Network [32] 2.33 0.286 1.5 4 0.750 10. T Cell Receptor Signalling [33] 1.46 0.323 3 13 0.462 11. "
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    ABSTRACT: Network measures that reflect the most salient properties of complex large-scale networks are in high demand in the network research community. In this paper we adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parametrized finite networks, and show that a variety of biological and social networks are hyperbolic. This hyperbolicity property has strong implications on the higher-order connectivity and other topological properties of these networks. Specifically, we derive and prove bounds on the distance among shortest or approximately shortest paths in hyperbolic networks. We describe two implications of these bounds to crosstalk in biological networks, and to the existence of central, influential neighborhoods in both biological and social networks.
    Full-text · Article · Mar 2014 · Physical Review E
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    • "The input (source) nodes of signaling networks represent the ligands or their receptors, the intermediate nodes consist of various kinases and second messengers, and the output (sink) nodes represent cellular responses (e.g. transcription factors) [4]. During simulation, we used normalized similarity index (NSI) to weight the edges and calibrated their efficiency for signal transduction in network [18] [19]. "
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    ABSTRACT: A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises four main steps which include weighting the edges, simulating signal transduction in the network (weighting the nodes), finding paths between initial and target nodes, and assigning a significance score to each path. We applied the proposed model to eighty-three signaling networks by using biologically derived source and sink molecules. The recovered dominant paths matched many known signaling pathways and suggesting a promising index to analyze the phenotype essentiality of molecule encoding paths. We also modeled the stimulus–response relations in long and short-term synaptic plasticity based on the dominant signaling pathway concept. We showed that the proposed method not only accurately determines dominant signaling pathways, but also identifies effective points of intervention in signal transduction.
    Full-text · Article · Oct 2013 · Genomics
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