A New Approach to Link Prediction in Gene Regulatory Networks

2015 ◽  
pp. 404-415 ◽  
Author(s):  
Turki Turki ◽  
Jason T. L. Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Link Prediction ◽  
Regulatory Networks ◽  
New Approach ◽  
Gene Regulatory

scholarly journals An ASP-based Approach for Boolean Networks Representation and Attractor Detection

10.29007/fb4f ◽  
2020 ◽  
Author(s):  
Tarek Khaled ◽  
Belaid Benhamou
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Answer Set Programming ◽  
Boolean Networks ◽  
Stable States ◽  
New Approach ◽  
Significant Step ◽  
The Subject ◽  
Gene Regulatory ◽  
Critical Aspects

In biology, Boolean networks are conventionally used to represent and simulate gene regulatory networks. The attractors are the subject of special attention in analyzing the dynamics of a Boolean network. They correspond to stable states and stable cycles, which play a crucial role in biological systems. In this work, we study a new representation of the dynamics of Boolean networks that are based on a new semantics used in answer set programming (ASP). Our work is based on the enu- meration of all the attractors of asynchronous Boolean networks having interaction graphs which are circuits. We show that the used semantics allows to design a new approach for computing exhaustively both the stable cycles and the stable states of such networks. The enumeration of all the attractors and the distinction between both types of attractors is a significant step to better understand some critical aspects of biology. We applied and evaluated the proposed approach on randomly generated Boolean networks and the obtained results highlight the benefits of this approach, and match with some conjectured results in biology.

Cracking the Egg: Virtual Embryogenesis of Real Robots

2014 ◽  
Vol 20 (3) ◽  
pp. 361-383 ◽  
Author(s):  
Sylvain Cussat-Blanc ◽  
Jordan Pollack
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Developmental Process ◽  
Body Plan ◽  
Living Systems ◽  
New Approach ◽  
Virtual Cells ◽  
Building Process ◽  
Gene Regulatory

All multicellular living beings are created from a single cell. A developmental process, called embryogenesis, takes this first fertilized cell down a complex path of reproduction, migration, and specialization into a complex organism adapted to its environment. In most cases, the first steps of the embryogenesis take place in a protected environment such as in an egg or in utero. Starting from this observation, we propose a new approach to the generation of real robots, strongly inspired by living systems. Our robots are composed of tens of specialized cells, grown from a single cell using a bio-inspired virtual developmental process. Virtual cells, controlled by gene regulatory networks, divide, migrate, and specialize to produce the robot's body plan (morphology), and then the robot is manually built from this plan. Because the robot is as easy to assemble as Lego, the building process could be easily automated.

scholarly journals Dynamical Modularity of the Genotype-Phenotype Map

2021 ◽  
Author(s):  
Johannes Jaeger ◽  
Nick Monk
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Dynamic Behaviour ◽  
New Approach ◽  
Regulatory Systems ◽  
Alternative Approach ◽  
Gene Regulatory ◽  
And Function ◽  
Discrete Traits

An organism’s phenotype can be thought of as consisting of a set of discrete traits, able to evolve relatively independently of each other. This implies that the developmental processes generating these traits—the underlying genotype-phenotype map—must also be functionally organised in a modular manner. The genotype-phenotype map lies at the heart of evolutionary systems biology. Recently, it has become popular to define developmental modules in terms of the structure of gene regulatory networks. This approach is inherently limited: gene networks often do not have structural modularity. More generally, the connection between structure and function is quite loose. In this chapter, we discuss an alternative approach based on the concept of dynamical modularity, which overcomes many of the limitations of structural modules. A dynamical module consists of the activities of a set of genes and their interactions that generate a specific dynamic behaviour. These modules can be identified and characterised by phase-space analysis of data-driven models. We showcase the power and the promise of this new approach using several case studies. Dynamical modularity forms an important component of a general theory of the evolution of regulatory systems and the genotype-phenotype map they define.

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