Decoupled modeling of gene regulatory networks using Michaelis-Menten kinetics
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed. © Springer International Publishing Switzerland 2015.
Gene regulatory network inference using Michaelis-Menten kinetics
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 2015 IEEE Congress on Evolutionary Computation (Cec); Sendai, Japan; 25th-28th May 2015 p. 2392-2397
- Full Text: false
- Reviewed:
- Description: A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.
Exploiting temporal genetic correlations for enhancing regulatory network optimization
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 23rd International Conference on Neural Information Processing, ICONIP 2016; Kyoto, Japan; 16th-21st October 2016; published in Neural Information Processing (Lecture Notes in Computer Science series) Vol. 9947 LNCS, p. 479-487
- Full Text: false
- Reviewed:
- Description: Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference. © Springer International Publishing AG 2016.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Large scale modeling of genetic networks using gene knockout data
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 Australasian Computer Science Week Multiconference, ACSW 2018; Brisbane, Australia; 29th January-2nd February 2018; published in ACM International Conference Proceedings Series
- Full Text: false
- Reviewed:
- Description: Gene regulatory network (GRN) represents a set of genes and their regulatory interactions. The inference of the regulatory interactions between genes is usually carried out as an optimization problem using an appropriate mathematical model and the time-series gene expression data. Among the various models proposed for GRN inference, our recently proposed Michaelis-Menten kinetics based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Since the search space for large networks is huge, leading to a low accuracy of inference, it is important to reduce the search region for improved performance of the optimization algorithm. In this paper, we propose a classification method using gene knockout data to eliminate a large infeasible region from the optimization search area. We also propose a method for partial inference of regulations when all the regulators of a given regulated gene are unregulated genes. The proposed method is evaluated by reconstructing in silico networks of large sizes. © 2018 ACM.
PCA based population generation for genetic network optimization
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2018
- Type: Text , Journal article
- Relation: Cognitive Neurodynamics Vol. 12, no. 4 (2018), p. 417-429
- Full Text:
- Reviewed:
- Description: A gene regulatory network (GRN) represents a set of genes and its regulatory interactions. The inference of the regulatory interactions between genes is usually carried out using an appropriate mathematical model and the available gene expression profile. Among the various models proposed for GRN inference, our recently proposed Michaelis–Menten based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Considering various issues associated with such population based stochastic optimization approaches (e.g. diversity, premature convergence due to local optima, accuracy, etc.), it becomes important to seed the initial population with good individuals which are closer to the optimal solution. In this paper, we exploit the inherent strength of principal component analysis (PCA) in a novel manner to initialize the population for GRN optimization. The benefit of the proposed method is validated by reconstructing in silico and in vivo networks of various sizes. For the same level of accuracy, the approach with PCA based initialization shows improved convergence speed.
Reverse engineering genetic networks using nonlinear saturation kinetics
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2019
- Type: Text , Journal article
- Relation: BioSystems Vol. 182, no. (2019), p. 30-41
- Full Text:
- Reviewed:
- 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.