71 research outputs found

    Flowchart describing the CPSS method to determine and analyze network topologies underlying a given dynamic property.

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    <p>Flowchart describing the CPSS method to determine and analyze network topologies underlying a given dynamic property.</p

    CPSS software implementation.

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    <p>The major functional modules (above the gray box) are application-independent objects and will not need to be changed for a new application. The modules within the gray box are application-specific objects; they inherit all properties of their “parent” objects, which help minimize the amount of new coding needed. For example, the present study uses a bistability evaluator (under “network feature evaluator”) and a 3-node system (under “ODE solver”). A different feature evaluator (e.g. adaptation) and a network with larger number of nodes can be similarly implemented for a new study.</p

    Systematic Reverse Engineering of Network Topologies: A Case Study of Resettable Bistable Cellular Responses

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    <div><p>A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the <i>continuous</i> parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.</p></div

    Pairwise correlation between links 3 and 9: 2D correlation heat map.

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    <p>The x axis denotes the link from EE to MD (link 3); the y axis denotes the link from EE to itself (link 9). The value on each axis denotes the link strength, with the positive and negative segments indicating activation and repression links, respectively. Color bar on the right: the fraction of ‘good’ parameter sets (supporting resettable bistability).</p

    Mean network motif for resettable bistability under the Constrained situation.

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    <p>(a) Mean value matrix consisting all six network links. (b) Topology Matrix after discretization of the Mean value matrix. (c) Mean network topology, which is responsible for resettable bistability under the Constrained situation.</p

    Backbone motif underlying resettable bistability under the Constrained situation.

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    <p>(a) Coefficient Variation matrix. (b) Backbone motifs obtained from the Coefficient Variation matrix. See text for details.</p

    Mean network topology underlying the resettable bistability.

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    <p>(a) Mean value matrix consisting all six network links. (b) Topology matrix after discretization of Mean value matrix. (c) Mean network topology obtained from the topology matrix.</p

    Pairwise correlation between links 3 and 9: Diagrams of link combinations that correspond to the heat map in <b>Figure 6</b>.

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    <p>Pairwise correlation between links 3 and 9: Diagrams of link combinations that correspond to the heat map in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105833#pone-0105833-g006" target="_blank"><b>Figure 6</b></a>.</p

    An <i>N</i>,<i>N</i>‑Bis(benzimidazolylpicolinoyl)piperazine (BT-11): A Novel Lanthionine Synthetase C‑Like 2‑Based Therapeutic for Inflammatory Bowel Disease

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    Lanthionine synthetase C-like 2 (LANCL2), a novel therapeutic target for inflammatory and autoimmune diseases and diabetes, exerts anti-inflammatory and insulin-sensitizing effects. This study reports the first LANCL2-based therapeutics for inflammatory bowel disease (IBD). Analogues of <b>1</b> (ABA) and <b>2</b> (NSC61610) were screened by molecular docking, then synthesized and analyzed for binding to LANCL2 by surface plasmon resonance. Piperazine-1,4-diylbis­(6-benzo­[d]­imidazole-2-yl)­pyridine-2-yl)­methanone, <b>7</b>, was identified as the lead LANCL2-binding compound for treating IBD. The oral treatment with <b>7</b> (8 mg/kg/d) in a mouse model of IBD resulted in lowering the disease activity index, decreasing colonic inflammatory lesions by 4-fold, and suppressing inflammatory markers (e.g., TNF-α, and interferon-Îł) in the gut. Furthermore, studies in LANCL2–/– mice demonstrated that loss of LANCL2 abrogated beneficial actions of <b>7</b>, suggesting high selectivity for the target. In conclusion, <b>7</b> merits continued development as a LANCL2-based, first-in-class orally active therapeutic for IBD
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