Logic-Based Models for the Analysis of Cell Signaling Networks

Morris, Melody K.; Sáez-Rodríguez, Julio; Sorger, Peter K.; Lauffenburger, Douglas A.

doi:10.1021/bi902202q

Cited by 294 publications

(243 citation statements)

References 61 publications

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“…Among the modeling techniques, logic(al) modeling has proven to be very versatile and able to provide useful biological insights 25, 26, 27. It has been applied for studying several biological phenomena (e.g., developmental processes,28 hematopoiesis,29 or cell fate decision30), but primarily used in signaling and gene regulation 26.…”

Section: Basics Of Logic Modelingmentioning

confidence: 99%

Logic Modeling in Quantitative Systems Pharmacology

Traynard

Tobalina

Eduati

et al. 2017

CPT Pharmacometrics Syst. Pharmacol.

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Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

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Section: Basics Of Logic Modelingmentioning

confidence: 99%

Logic Modeling in Quantitative Systems Pharmacology

Traynard

Tobalina

Eduati

et al. 2017

CPT Pharmacometrics Syst. Pharmacol.

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“…Metabolic networks have been analysed using flux balance analysis 53 , metabolic flux analysis, pathway analysis by elementary modes, or extreme pathways 54,55 . Signalling networks can likewise be addressed via a variety of techniques, ranging from ODEs 56,57 to Boolean networks in global cellular models 58,59 .…”

Section: Contemporary Approaches: Microscopic Modelsmentioning

confidence: 99%

Mathematical Models of Microbial Growth and Metabolism: A Whole-Organism Perspective

Nev

Berg

2017

Science Progress

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warwick.ac.uk/lib-publicationsOriginal citation: Nev, Olga and Berg, Hugo van den. (2017) Mathematical models of microbial growth and metabolism : a whole-organism perspective. Science Progress, 100 (4). pp. 343-362. Permanent WRAP URL:http://wrap.warwick.ac.uk/89064 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:This is an Accepted Manuscript of an article published by Science Reviews 2000 Ltd in Science Progresson 1 November 2017, available online: https://doi.org/10.3184/003685017X15063357842583 A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. ABSTRACTWe review the principles underpinning the development of mathematical models of the metabolic activities of micro-organisms. Such models are important to understand and chart the substantial contributions made by micro-organisms to geochemical cycles, and also to optimise the performance of bio-reactors that exploit the biochemical capabilities of these organisms. We advocate an approach based on the principle of dynamic allocation. We survey the biological background that motivates this approach, including nutrient assimilation, the regulation of gene expression, and the principles of microbial growth. In addition, we discuss the classic models of microbial growth as well as contemporary approaches. The dynamic allocation theory generalises these classic models in a natural manner and is readily amenable to the additional information provided by transcriptomics and proteomics approaches. Finally, we touch upon these organising principles in the context of the transition from the free-living unicellular mode of life to multicellularity.

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“…The model consists of 22 nodes representing the principal components involved, or likely participating, in the signaling cascade: ion channel activities, intracellular ion and molecular concentrations and the membrane potential, amongst others. To analyze the dynamics of the network, we implemented a discrete formulation that is a generalization of the Boolean approach and that has proven to be revealing for the gene regulation dynamics of many systems (Kauffman, 1969;Espinosa-Soto et al, 2004;Albert and Othmer, 2003;Huang and Ingber, 2000;Li et al, 2004), as well as other cell signaling networks (Morris et al, 2010). In this approach, the dynamic state of the network consists of a set of N discrete variables {s 1 , s 2 ,... s n }, each representing the state of a node.…”

Section: Mathematical Modelmentioning

confidence: 99%

Niflumic acid disrupts marine spermatozoan chemotaxis without impairing the spatiotemporal detection of chemoattractant gradients

Guerrero

Espinal

Wood

et al. 2013

Journal of Cell Science

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SummaryIn many broadcast-spawning marine organisms, oocytes release chemicals that guide conspecific spermatozoa towards them through chemotaxis. In the sea urchin Lytechinus pictus, the chemoattractant peptide speract triggers a train of fluctuations of intracellular Ca 2+ concentration in the sperm flagella. Each transient Ca 2+ elevation leads to a momentary increase in flagellar bending asymmetry, known as a chemotactic turn. Furthermore, chemotaxis requires a precise spatiotemporal coordination between the Ca 2+ -dependent turns and the form of chemoattractant gradient. Spermatozoa that perform Ca 2+ -dependent turns while swimming down the chemoattractant gradient, and conversely suppress turning events while swimming up the gradient, successfully approach the center of the gradient. Previous experiments in Strongylocentrotus purpuratus sea urchin spermatozoa showed that niflumic acid (NFA), an inhibitor of several ion channels, drastically altered the speract-induced Ca 2+ fluctuations and swimming patterns. In this study, mathematical modeling of the speract-dependent Ca 2+ signaling pathway suggests that NFA, by potentially affecting hyperpolarization-activated and cyclic nucleotidegated channels, Ca 2+ -regulated Cl 2 channels and/or Ca 2+ -regulated K + channels, may alter the temporal organization of Ca 2+ fluctuations, and therefore disrupt chemotaxis. We used a novel automated method for analyzing sperm behavior and we identified that NFA does indeed disrupt chemotactic responses of L. pictus spermatozoa, although the temporal coordination between the Ca 2+ -dependent turns and the form of chemoattractant gradient is unaltered. Instead, NFA disrupts sperm chemotaxis by altering the arc length traveled during each chemotactic turning event. This alteration in the chemotactic turn trajectory disorientates spermatozoa at the termination of the turning event. We conclude that NFA disrupts chemotaxis without affecting how the spermatozoa decode environmental cues.

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Logic-Based Models for the Analysis of Cell Signaling Networks

Cited by 294 publications

References 61 publications

Logic Modeling in Quantitative Systems Pharmacology

Logic Modeling in Quantitative Systems Pharmacology

Mathematical Models of Microbial Growth and Metabolism: A Whole-Organism Perspective

Niflumic acid disrupts marine spermatozoan chemotaxis without impairing the spatiotemporal detection of chemoattractant gradients

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