Positive and Negative Circuits in Discrete Neural Networks

Aracena, Julio; Demongeot, Jacques; Goles, Éric

doi:10.1109/tnn.2003.821555

Cited by 73 publications

(50 citation statements)

References 14 publications

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“…For the MRN to be able to reproduce the observed patterns, (1) positive circuits must be present for multiple cell fates to exist (Thomas and Kaufman, 2001;Soulé et al, 2006), each stable steady state of the network corresponding to one cell fate; (2) at least one positive circuit is required for two steady stable states and several positive circuits are needed to generate more stable steady states, the maximum number of stable steady states depending on the number of positive circuits and on their intersections (Aracena et al, 2004;Aracena, 2008); and (3) each element differentially expressed among regions must be included in a positive circuit with other elements that are also differentially expressed among those regions or regulated by such a circuit.…”

Section: Mrn Requirements For Patterning and Multistabilitymentioning

confidence: 99%

A Data-Driven Integrative Model of Sepal Primordium Polarity in Arabidopsis

et al. 2011

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Flower patterning is determined by a complex molecular network but how this network functions remains to be elucidated. Here, we develop an integrative modeling approach that assembles heterogeneous data into a biologically coherent model to allow predictions to be made and inconsistencies among the data to be found. We use this approach to study the network underlying sepal development in the young flower of Arabidopsis thaliana. We constructed a digital atlas of gene expression and used it to build a dynamical molecular regulatory network model of sepal primordium development. This led to the construction of a coherent molecular network model for lateral organ polarity that fully recapitulates expression and interaction data. Our model predicts the existence of three novel pathways involving the HD-ZIP III genes and both cytokinin and ARGONAUTE family members. In addition, our model provides predictions on molecular interactions. In a broader context, this approach allows the extraction of biological knowledge from diverse types of data and can be used to study developmental processes in any multicellular organism.

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Section: Mrn Requirements For Patterning and Multistabilitymentioning

confidence: 99%

A Data-Driven Integrative Model of Sepal Primordium Polarity in Arabidopsis

et al. 2011

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“…Under these hypotheses, the authors of [45,73,74] proved the following results: Theorem 1. If all cycles of the undirected version of G are positive then there exists a vector x = (x 1 , .…”

Section: Appendix B Biological Regulation Network Modelingmentioning

confidence: 96%

“…First, as it has been explained in Appendix D, in certain applications [27,45,67,73,74], the state 0 is replaced by −1 by a simple change of variable.…”

Section: Appendix E Relationships Between the Different Formalismsmentioning

confidence: 99%

“Immunetworks”, intersecting circuits and dynamics

Demongeot

Elena

Noual

et al. 2011

Journal of Theoretical Biology

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This paper proposes a study of biological regulatory networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system ("immunetworks"), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that "inter-lock".

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“…The five phases of the cell cycle can be further decomposed into different states, by taking into account critical control points sensitive to the regulation in the frame of genetic networks or to external or internal metabolite, morphogen or antibody fluctuations (e.g. Daszynkiewiez et al 1980;Bertuzzi et al 1981;Cohen et al 2001;Cinquin & Demongeot 2002a,b, 2005Aracena et al 2003Aracena et al , 2004aBasse et al 2003;Demongeot et al 2003a-c;Aracena & Demongeot 2004;Baum et al 2004Baum et al , 2006Laforge et al 2005;Guardavaccaro & Pagano 2006) accelerating the progress to the mitosis or provoking either cell death (apoptosis) or cell differentiation (a dramatic change of cell lineage).…”

Section: Cell Cycle Dynamics: Proliferation Growth and Apoptosis (A)mentioning

confidence: 99%

Synchrony in reaction–diffusion models of morphogenesis: applications to curvature-dependent proliferation and zero-diffusion front waves

Abbas

Demongeot

Glade

2009

Phil. Trans. R. Soc. A.

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The paper presents the classical age-dependent approach of the morphogenesis in the framework of the von Foerster equation, in which we introduce a new constraint and study a new feature: (i) the new constraint concerns cell proliferation along the contour lines of the cell density, depending on the local curvature such as it favours the amplification of the concavities (like in the gastrulation process) and (ii) the new feature consists of considering, on the cell density surface, a remarkable line (the null mean Gaussian curvature line), on which the normal diffusion vanishes, favouring local coexistence of diffusing morphogens, metabolites or cells, and hence the auto-assemblages of these entities. Two applications to biological multi-agents systems are studied, gastrulation and feather morphogenesis.

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Positive and Negative Circuits in Discrete Neural Networks

Cited by 73 publications

References 14 publications

A Data-Driven Integrative Model of Sepal Primordium Polarity in Arabidopsis

A Data-Driven Integrative Model of Sepal Primordium Polarity in Arabidopsis

“Immunetworks”, intersecting circuits and dynamics

Synchrony in reaction–diffusion models of morphogenesis: applications to curvature-dependent proliferation and zero-diffusion front waves

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