scholarly journals GeneVis: Simulation and Visualization of Genetic Networks

2003 ◽  
Vol 2 (4) ◽  
pp. 201-217 ◽  
Author(s):  
Charles Baker ◽  
Sheelagh Carpendale ◽  
Przemyslaw Prusinkiewicz ◽  
Michael Surette
Keyword(s):  
Network Structure ◽  
Base Pair ◽  
Regulatory Networks ◽  
Protein Concentration ◽  
Genetic Network ◽  
Genetic Regulatory Networks ◽  
Genetic Networks ◽  
Structure Representation ◽  
Individual Protein ◽  
The Individual

GeneVis simulates genetic networks and visualizes the process of this simulation interactively, providing a visual environment for exploring the dynamics of genetic regulatory networks. The visualization environment supports several representational modes, which include: an individual protein representation, a protein concentration representation, and a network structure representation. The individual protein representation shows the activities of the individual proteins. The protein concentration representation illustrates the relative spread and concentrations of the different proteins in the simulation. The network structure representation depicts the genetic network dependencies that are present in the simulation. GeneVis includes several interactive viewing tools. These include animated transitions from the individual protein representation to the protein concentration representation and from the individual protein representation to the network structure representation. Three types of lenses are used to provide different views within a representation: fuzzy lenses, base pair lenses, and the network structure ring lens. With a fuzzy lens an alternate representation can be viewed in a selected region. The base pair lenses allow users to reposition genes for better viewing or to minimize interference during the simulation. The ring lens provides detail-in-context viewing of individual levels in the genetic network structure representation.

scholarly journals A High-Level Petri Net Framework for Genetic Regulatory Networks

2007 ◽  
Vol 4 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Richard Banks ◽  
L. Jason Steggles
Keyword(s):  
Regulatory Networks ◽  
Formal Analysis ◽  
Boolean Model ◽  
Genetic Regulatory Networks ◽  
Cellular Systems ◽  
Logic Minimization ◽  
Formal Framework ◽  
The Individual ◽  
High Level ◽  
Compare And Contrast

Summary To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.

scholarly journals Analyzing the miRNA-Gene Networks to Mine the Important miRNAs under Skin of Human and Mouse

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jianghong Wu ◽  
Husile Gong ◽  
Yongsheng Bai ◽  
Wenguang Zhang
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
Mirna Gene ◽  
Genetic Regulatory Networks ◽  
Host Immunity ◽  
Genetic Networks ◽  
Target Sites ◽  
Enormous Number ◽  
Human And Mouse

Genetic networks provide new mechanistic insights into the diversity of species morphology. In this study, we have integrated the MGI, GEO, and miRNA database to analyze the genetic regulatory networks under morphology difference of integument of humans and mice. We found that the gene expression network in the skin is highly divergent between human and mouse. The GO term of secretion was highly enriched, and this category was specific in human compared to mouse. These secretion genes might be involved in eccrine system evolution in human. In addition, total 62,637 miRNA binding target sites were predicted in human integument genes (IGs), while 26,280 miRNA binding target sites were predicted in mouse IGs. The interactions between miRNAs and IGs in human are more complex than those in mouse. Furthermore,hsa-miR-548,mmu-miR-466, andmmu-miR-467have an enormous number of targets on IGs, which both have the role of inhibition of host immunity response. The pattern of distribution on the chromosome of these three miRNAs families is very different. The interaction of miRNA/IGs has added the new dimension in traditional gene regulation networks of skin. Our results are generating new insights into the gene networks basis of skin difference between human and mouse.

INFERENCE OF GENE REGULATORY NETWORKS USING BOOLEAN-NETWORK INFERENCE METHODS

2009 ◽  
Vol 07 (06) ◽  
pp. 1013-1029 ◽  
Author(s):  
GRAHAM J. HICKMAN ◽  
T. CHARLIE HODGMAN
Keyword(s):  
Regulatory Networks ◽  
Boolean Network ◽  
Network Inference ◽  
Genetic Network ◽  
Boolean Networks ◽  
Genetic Networks ◽  
Related Data ◽  
Boolean Network Model ◽  
Gene Regulatory ◽  
Inference Methods

The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks. The review outlines the various different types of Boolean network developed to date, from the original Random Boolean Network to the current Probabilistic Boolean Network. In addition, some of the different inference methods available to infer these genetic networks are also examined. Where possible, particular attention is paid to input requirements as well as the efficiency, advantages and drawbacks of each method. Though the Boolean network model is one of many models available for network inference today, it is well established and remains a topic of considerable interest in the field of genetic network inference. Hybrids of Boolean networks with other approaches may well be the way forward in inferring the most informative networks.

scholarly journals Complex and unexpected dynamics in simple genetic regulatory networks

2014 ◽  
Vol 28 (14) ◽  
pp. 1430006 ◽  
Author(s):  
Yanika Borg ◽  
Ekkehard Ullner ◽  
Afnan Alagha ◽  
Ahmed Alsaedi ◽  
Darren Nesbeth ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Complex Dynamics ◽  
Dynamical Behavior ◽  
Genetic Regulatory Networks ◽  
Genetic Networks ◽  
Emergent Properties ◽  
Coupling Mechanism ◽  
Bistable Systems ◽  
Large Populations ◽  
External Signals

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

scholarly journals Genetic Network Analyzer: qualitative simulation of genetic regulatory networks

2003 ◽  
Vol 19 (3) ◽  
pp. 336-344 ◽  
Author(s):  
H. de Jong ◽  
J. Geiselmann ◽  
C. Hernandez ◽  
M. Page
Keyword(s):  
Regulatory Networks ◽  
Genetic Network ◽  
Genetic Regulatory Networks ◽  
Network Analyzer ◽  
Qualitative Simulation

scholarly journals Entropy as a Robustness Marker in Genetic Regulatory Networks

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 260 ◽  
Author(s):  
Mustapha Rachdi ◽  
Jules Waku ◽  
Hana Hazgui ◽  
Jacques Demongeot
Keyword(s):  
Invariant Measure ◽  
Regulatory Networks ◽  
Practical Interest ◽  
Genetic Network ◽  
Genetic Regulatory Networks ◽  
Connected Components ◽  
Circular Rnas ◽  
Stability Robustness ◽  
The Stability ◽  
Mathematical Relationships

Genetic regulatory networks have evolved by complexifying their control systems with numerous effectors (inhibitors and activators). That is, for example, the case for the double inhibition by microRNAs and circular RNAs, which introduce a ubiquitous double brake control reducing in general the number of attractors of the complex genetic networks (e.g., by destroying positive regulation circuits), in which complexity indices are the number of nodes, their connectivity, the number of strong connected components and the size of their interaction graph. The stability and robustness of the networks correspond to their ability to respectively recover from dynamical and structural disturbances the same asymptotic trajectories, and hence the same number and nature of their attractors. The complexity of the dynamics is quantified here using the notion of attractor entropy: it describes the way the invariant measure of the dynamics is spread over the state space. The stability (robustness) is characterized by the rate at which the system returns to its equilibrium trajectories (invariant measure) after a dynamical (structural) perturbation. The mathematical relationships between the indices of complexity, stability and robustness are presented in case of Markov chains related to threshold Boolean random regulatory networks updated with a Hopfield-like rule. The entropy of the invariant measure of a network as well as the Kolmogorov-Sinaï entropy of the Markov transition matrix ruling its random dynamics can be considered complexity, stability and robustness indices; and it is possible to exploit the links between these notions to characterize the resilience of a biological system with respect to endogenous or exogenous perturbations. The example of the genetic network controlling the kinin-kallikrein system involved in a pathology called angioedema shows the practical interest of the present approach of the complexity and robustness in two cases, its physiological normal and pathological, abnormal, dynamical behaviors.

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