The Existence and the Economic Implications of Genetic Networks : a Meta-Analysis (original) (raw)
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The Existence and the Socio-Economic Implications of Genetic Networks: A Meta-Analysis
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In the recent past, the scientific community experienced a renewed interest in the study of complex networks. Numerous scientists from different fields were interested in studying the topological features and the interactions among the components of complex networks. Intense research activity was directed towards biological networks, where network theory finds its natural application and genes are considered nodes with links as the interconnections among them. These studies have produced remarkable progress, not only in understanding the topological and chemical structures of the genes (which, today, can be described and determined precisely), but also on improving agricultural crops. Thanks to the deep genetic knowledge acquired, genes can be modified and recombined into the cells of living organisms. For this reason, scientists started to use the recombinant DNA technique to improve crop productivity or to make crops more resistant to stress, diseases, and chemical treatments. In ...
Common network structure in the regulatory core of trait development
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Gene regulatory networks (GRN) play an important role in controlling the development and maintenance of traits such as body parts, organs, tissues or cell types. Small recurring sub-graphs, so-called network motifs, have been found in GRN at higher frequencies than in random networks, suggesting they could implement biologically relevant functions. Additionally, recent studies have implicated that network topology may be involved in the regulation of traits. Based on these findings, we investigate whether specific topological patterns can be associated with trait regulation. We characterize the global and local structure of twelve human cell type-specific GRN and six mouse tissue-specific GRN. We also compare their structure and interaction repertoire and examine if network motifs co-occur with trait-determining regulators / transcription factors (TFs). For this purpose we have developed a new method to reconstruct cell type- and tissue-specific GRN from predicted TF binding sites i...
Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework
Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways with principles of quantitative and population genetics to characterize genetic networks underlying complex traits, using a model founded upon one-way functional dependency of downstream genes on upstream regulators (the principle of hierarchy) and mutual functional dependency among related genes (functional genetic units, FGU). Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many 'downstream' loci in pathways. The mathematical relationships between the main effects and epistatic effects of genes acting at different levels of signaling pathways were established using the quantitative and population genetic parameters. Both loss of function and ''coadapted'' gene complexes formed by multiple alleles with differentiated functions (effects) are predicted to be frequent types of allelic diversity at loci that contribute to the genetic variation of complex traits in populations. Downstream FGUs appear to be more vulnerable to loss of function than their upstream regulators, but this vulnerability is apparently compensated by different FGUs of similar functions. Other predictions from the model may account for puzzling results regarding responses to selection, genotype by environment interaction, and the genetic basis of heterosis.
Genetic network models: a comparative study
Microarrays: Optical Technologies and Informatics, 2001
Currently, the need arises for tools capable of unraveling the functionality of genes based on the analysis of microarray measurements. Modeling genetic interactions by means of genetic network models provides a methodology to infer functional relationships between genes. Although a wide variety of di erent models have been introduced so far, it remains, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and di er. This paper compares di erent genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: (1) inferential power;
A Comparison of Genetic Network Models
Pacific Symposium on Biocomputing, 2001
With the completion of the sequencing of the human genome, the need for tools capable of unraveling the interaction and functionality of genes becomes extremely urgent. In answer to this quest, the advent of microarray technology provides the opportunity to perform large scale gene expression analyses. Recently, genetic networks were proposed as a possible methodology for modeling genetic interactions. Since then, a wide variety of di erent models have been introduced. However, it is, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and di er. This paper compares di erent genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important c haracteristics are suggested and employed to compare the models: 1 inferential power; 2 predictive p o w er; 3 robustness; 4 consistency; 5 stability and 6 computational cost. Where possible, synthetic time series data are employed to investigate some of these properties.
Journal of Molecular Evolution, 2004
Population genetics, the mathematical theory of modern evolutionary biology, defines evolution as the alteration of the frequency of distinct gene variants (alleles) differing in fitness over the time. The major problem with this view is that in gene and protein sequences we can find little evidence concerning the molecular basis of phenotypic variance, especially those that would confer adaptive benefit to the bearers. Some novel data, however, suggest that a large amount of genetic variation exists in the regulatory region of genes within populations. In addition, comparison of homologous DNA sequences of various species shows that evolution appears to depend more strongly on gene expression than on the genes themselves. Furthermore, it has been demonstrated in several systems that genes form functional networks, whose products exhibit interrelated expression profiles. Finally, it has been found that regulatory circuits of development behave as evolutionary units. These data demonstrate that our view of evolution calls for a new synthesis. In this article I propose a novel concept, termed the selfish gene network hypothesis, which is based on an overall consideration of the above findings. The major statements of this hypothesis are as follows. (1) Instead of individual genes, gene networks (GNs) are responsible for the determination of traits and behaviors. (2) The primary source of microevolution is the intraspecific polymorphism in GNs and not the allelic variation in either the coding or the regulatory sequences of individual genes. (3) GN polymorphism is generated by the variation in the regulatory regions of the component genes and not by the variance in their coding sequences. (4) Evolution proceeds through continuous restructuring of the composition of GNs rather than fixing of specific alleles or GN variants.
Identification of Genetic Networks
Genetics, 2004
In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T 2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets.
Frontiers in cell and developmental biology, 2014
In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine.
High participation ratio genes in the interaction network structure
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Genes have specific functional roles, however, since they are dependent on each other, they can play a structural role within a network structure of their interactions. In this study, we analyze the structure of the gene interaction network and detect the most contributing genes through the random matrix theory. Specifically, we compare the interaction network of essential and nonessential genes of the yeast Saccharomyces cerevisiae. Most remarkably, this well-established combined framework by measuring the node participation ratio (NPR) index helps detect important genes, which control the insightful structural patterns in the underlying networks. Results indicate that the essential genes have higher values of NPR rather than the nonessential ones which means that they have the most contribution to the network structure. It is worth mentioning that among all essential genes, the NPR value of 5 significant ones is considerably higher than the other essential genes, and also the same...