Description:
A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model.
Description:
A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model.
Description:
This work was supported in part by the Australian Research Council ( LE150100015 ), as well as by a SMAH Small Project Grant ( University of Wollongong ).
Description:
In simple terms, copy number variations or CNVs are replications or deletions in the DNA which, in humans, changes it from the normal number of two gene copies. These CNVs are caused by inherited or de novo structural changes such as duplications, insertions or deletions of repeated portions of genetic material (Fig. 1). These duplications can vary from one to ten or more copies and range in size from 50 DNA base pairs to several million [1]. Since their discovery in 1987 by Nakamura et al. [2], when they were initially named variable number tandem repeats, many studies have investigated their association with rare and common human diseases. Throughout evolution, some of these changes in copy number were beneficial such as the globin gene number duplication, while others such as the CNVs that cause Huntington's disease were not. In 2004, two landmark studies by Iafrate et al. [3] and Sebat et al. [4] found that large-scale copy-number variations, ranging in size from 100 kb to 2 Mb are common throughout the human genome, and that a high proportion of them are in known genes. These findings roused several association studies between CNVs and disease
Description:
In simple terms, copy number variations or CNVs are replications or deletions in the DNA which, in humans, changes it from the normal number of two gene copies. These CNVs are caused by inherited or de novo structural changes such as duplications, insertions or deletions of repeated portions of genetic material (Fig. 1). These duplications can vary from one to ten or more copies and range in size from 50 DNA base pairs to several million [1]. Since their discovery in 1987 by Nakamura et al. [2], when they were initially named variable number tandem repeats, many studies have investigated their association with rare and common human diseases. Throughout evolution, some of these changes in copy number were beneficial such as the globin gene number duplication, while others such as the CNVs that cause Huntington's disease were not. In 2004, two landmark studies by Iafrate et al. [3] and Sebat et al. [4] found that large-scale copy-number variations, ranging in size from 100 kb to 2 Mb are common throughout the human genome, and that a high proportion of them are in known genes. These findings roused several association studies between CNVs and disease
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