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Proceedings ArticleDOI

Stochasticity in reactions: a probabilistic Boolean modeling approach

TLDR
This paper model and analyze the cytokinin response network of Arabidopsis thaliana with a focus on clarifying the character of an important feedback mechanism and presents a formalism that augments Boolean models with stochastic aspects.
Abstract
Boolean modeling frameworks have long since proved their worth for capturing and analyzing essential characteristics of complex systems. Hybrid approaches aim at exploiting the advantages of Boolean formalisms while refining expressiveness. In this paper, we present a formalism that augments Boolean models with stochastic aspects. More specifically, biological reactions effecting a system in a given state are associated with probabilities, resulting in dynamical behavior represented as a Markov chain. Using this approach, we model and analyze the cytokinin response network of Arabidopsis thaliana with a focus on clarifying the character of an important feedback mechanism.

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Citations
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Journal ArticleDOI

A Stochastic Model of Epigenetic Dynamics in Somatic Cell Reprogramming

TL;DR: An abstract mechanistic model of a subset of the known regulatory processes during cell differentiation and production of induced pluripotent stem cells is developed, which describes the interplay between gene expression, chromatin modifications, and DNA methylation.
Journal ArticleDOI

Static Analysis of Boolean Networks Based on Interaction Graphs: A Survey

TL;DR: This paper presents results in this topic, mainly by focusing on the influence of positive and negative feedbacks inlean networks, by deducing from the interaction graph of the network, which only contains n vertices.
Book ChapterDOI

Concretizing the process hitting into biological regulatory networks

TL;DR: This work emphasizes the ability of PH to deal with large BRNs with incomplete knowledge on cooperations, where Thomas' approach fails because of the combinatorics of parameters.
Journal ArticleDOI

Identification of biological regulatory networks from Process Hitting models

TL;DR: The inference of the Interaction Graph from a PH model summarizes the signed influences between the components that are effective for the dynamics, and provides the inference of all Rene Thomas models of BRNs that are compatible with a given PH.
DissertationDOI

Systems biology approaches to somatic cell reprogramming reveal new insights into the order of events, transcriptional and epigenetic control of the process

Till Scharp
TL;DR: An intermediate state in which transcriptional activity of genes playing an important role in iPSCs is strongly down-regulated is postulated, in which the aforementioned transcriptionally inactive intermediate state accumulates during reprogramming simulations.
References
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Book

The origins of order

Journal ArticleDOI

Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

TL;DR: Probabilistic Boolean Networks (PBN) are introduced that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.
Journal ArticleDOI

Cytokinin metabolism and action

TL;DR: This review centers on cytokinin metabolism with connecting discussions on biosynthesis and signal transduction, and important findings are summarized with emphasis on metabolic enzymes and genes.
Journal ArticleDOI

Two-component circuitry in Arabidopsis cytokinin signal transduction

Ildoo Hwang, +1 more
- 27 Sep 2001 - 
TL;DR: A eukaryotic two-component signalling circuit that initiates cytokinin signalling through distinct hybrid histidine protein kinase activities at the plasma membrane is identified.
Journal Article

Cytokinin metabolism and action

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