scholarly journals ANALYSIS OF PREFERENTIAL NETWORK MOTIF GENERATION IN AN ARTIFICIAL REGULATORY NETWORK MODEL CREATED BY DUPLICATION AND DIVERGENCE

2007 ◽  
Vol 10 (02) ◽  
pp. 155-172 ◽  
Author(s):  
ANDRÉ LEIER ◽  
P. DWIGHT KUO ◽  
WOLFGANG BANZHAF
Keyword(s):  
Gene Regulatory Networks ◽  
Network Topology ◽  
Regulatory Networks ◽  
Genome Duplication ◽  
Network Motif ◽  
Network Motifs ◽  
Gene Regulatory ◽  
Artificial Gene ◽  
Artificial Model ◽  
Regulatory Sites

Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplication and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.

scholarly journals Network Inference from Gene Expression Data with Distance Correlation and Network Topology Centrality

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 61
Author(s):  
Kuan Liu ◽  
Haiyuan Liu ◽  
Dongyan Sun ◽  
Lei Zhang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Topology ◽  
Regulatory Networks ◽  
Network Inference ◽  
Expression Data ◽  
Distance Correlation ◽  
Non Linear ◽  
Gene Regulatory

The reconstruction of gene regulatory networks based on gene expression data can effectively uncover regulatory relationships between genes and provide a deeper understanding of biological control processes. Non-linear dependence is a common problem in the regulatory mechanisms of gene regulatory networks. Various methods based on information theory have been developed to infer networks. However, the methods have introduced many redundant regulatory relationships in the network inference process. A recent measurement method called distance correlation has, in many cases, shown strong and computationally efficient non-linear correlations. In this paper, we propose a novel regulatory network inference method called the distance-correlation and network topology centrality network (DCNTC) method. The method is based on and extends the Local Density Measurement of Network Node Centrality (LDCNET) algorithm, which has the same choice of network centrality ranking as the LDCNET algorithm, but uses a simpler and more efficient distance correlation measure of association between genes. In this work, we integrate distance correlation and network topological centrality into the reasoning about the structure of gene regulatory networks. We will select optimal thresholds based on the characteristics of the distribution of each gene pair in relation to distance correlation. Experiments were carried out on four network datasets and their performance was compared.

scholarly journals Evolutionary strategies applied to artificial gene regulatory networks

2021 ◽  
Author(s):  
André Luiz de Lucena Moreira ◽  
César Rennó-Costa
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Task Complexity ◽  
Single Cells ◽  
Single Gene ◽  
Evolutionary Strategy ◽  
Evolutionary Strategies ◽  
Gene Regulatory ◽  
Artificial Gene ◽  
The Impact

Evolution optimizes cellular behavior throughout sequential generations by selecting the successful individual cells in a given context. As gene regulatory networks (GRNs) determine the behavior of single cells by ruling the activation of different processes - such as cell differentiation and death - how GRNs change from one generation to the other might have a relevant impact on the course of evolution. It is not clear, however, which mechanisms that affect GRNs effectively favor evolution and how. Here, we use a population of computational robotic models controlled by artificial gene regulatory networks (AGRNs) to evaluate the impact of different genetic modification strategies in the course of evolution. The virtual agent senses the ambient and acts on it as a bacteria in different phototaxis-like tasks - orientation to light, phototaxis, and phototaxis with obstacles. We studied how the strategies of gradual and abrupt changes on the AGRNs impact evolution considering multiple levels of task complexity. The results indicated that a gradual increase in the complexity of the performed tasks is beneficial for the evolution of the model. Furthermore, we have seen that larger gene regulatory networks are needed for more complex tasks, with single-gene duplication being an excellent evolutionary strategy for growing these networks, as opposed to full-genome duplication. Studying how GRNs evolved in a biological environment allows us to improve the computational models produced and provide insights into aspects and events that influenced the development of life on earth.

scholarly journals Form and function in gene regulatory networks: the structure of network motifs determines fundamental properties of their dynamical state space

2016 ◽  
Vol 13 (120) ◽  
pp. 20160179 ◽  
Author(s):  
S. E. Ahnert ◽  
T. M. A. Fink
Keyword(s):  
State Space ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Boolean Network ◽  
Network Motifs ◽  
Form And Function ◽  
Fundamental Properties ◽  
Dynamical State ◽  
Gene Regulatory ◽  
And Function

Network motifs have been studied extensively over the past decade, and certain motifs, such as the feed-forward loop, play an important role in regulatory networks. Recent studies have used Boolean network motifs to explore the link between form and function in gene regulatory networks and have found that the structure of a motif does not strongly determine its function, if this is defined in terms of the gene expression patterns the motif can produce. Here, we offer a different, higher-level definition of the ‘function’ of a motif, in terms of two fundamental properties of its dynamical state space as a Boolean network. One is the basin entropy, which is a complexity measure of the dynamics of Boolean networks. The other is the diversity of cyclic attractor lengths that a given motif can produce. Using these two measures, we examine all 104 topologically distinct three-node motifs and show that the structural properties of a motif, such as the presence of feedback loops and feed-forward loops, predict fundamental characteristics of its dynamical state space, which in turn determine aspects of its functional versatility. We also show that these higher-level properties have a direct bearing on real regulatory networks, as both basin entropy and cycle length diversity show a close correspondence with the prevalence, in neural and genetic regulatory networks, of the 13 connected motifs without self-interactions that have been studied extensively in the literature.

Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks

2010 ◽  
pp. 498-522
Author(s):  
T. Steiner ◽  
Y. Jin ◽  
L. Schramm ◽  
B. Sendhoff
Keyword(s):  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Evolutionary Process ◽  
Computational Method ◽  
Cellular Growth ◽  
Small Scale ◽  
Mutational Robustness ◽  
Gene Regulatory ◽  
Artificial Gene

In this chapter, we describe the use of evolutionary methods for the in silico generation of artificial gene regulatory networks (GRNs). These usually serve as models for biological networks and can be used for enhancing analysis methods in biology. We clarify our motivation in adopting this strategy by showing the importance of detailed knowledge of all processes, especially the regulatory dynamics of interactions undertaken during gene expression. To illustrate how such a methodology works, two different approaches to the evolution of small-scale GRNs with specified functions, are briefly reviewed and discussed. Thereafter, we present an approach to evolve medium sized GRNs with the ability to produce stable multi-cellular growth. The computational method employed allows for a detailed analysis of the dynamics of the GRNs as well as their evolution. We have observed the emergence of negative feedback during the evolutionary process, and we suggest its implication to the mutational robustness of the regulatory network which is further supported by evidence observed in additional experiments.

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