Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.

@article{AngelinBonnet2019GeneRN,
  title={Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.},
  author={Olivia Angelin-Bonnet and Patrick J. Biggs and Matthieu Vignes},
  journal={Methods in molecular biology},
  year={2019},
  volume={1883},
  pages={
          347-383
        }
}
Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein… 
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References

SHOWING 1-10 OF 152 REFERENCES
Modeling and Simulation of Genetic Regulatory Systems: A Literature Review
TLDR
This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equation, stochastic equations, and so on.
Stochastic modelling of gene regulatory networks
TLDR
This article motivates the stochastic modelling of genetic networks and demonstrates the approach using several examples, and discusses the mathematics of molecular noise models including the chemical master equation, the chemical Langevin equation, and the reaction rate equation.
A Gene Network Simulator to Assess Reverse Engineering Algorithms
TLDR
A novel gene‐network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics, and provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data.
Reverse Engineering of Gene Regulatory Networks: A Comparative Study
TLDR
A comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks, highlights the neural network approach as best performing method among those under study.
Topological and causal structure of the yeast transcriptional regulatory network
TLDR
A graph of 909 genetically or biochemically established interactions among 491 yeast genes is created, showing a deviation from randomness probably reflects functional constraints that include biosynthetic cost, response delay and differentiative and homeostatic regulation.
Modelling and analysis of gene regulatory networks
Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these
A system for generating transcription regulatory networks with combinatorial control of transcription
TLDR
A new software system, REgulatory Network generator with COmbinatorial control (RENCO), for automatic generation of differential equations describing pre-transcriptional combinatorics in artificial regulatory networks.
Transcriptional regulation by the numbers: models.
SGN Sim, a Stochastic Genetic Networks Simulator
UNLABELLED We present SGNSim, 'Stochastic Gene Networks Simulator', a tool to model gene regulatory networks (GRN) where transcription and translation are modeled as multiple time delayed events and
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