Towards large scale genetic network modeling
- Authors: Khan, Rubaiya Rahtin , Chetty, Madhu
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: Reverse Engineering Gene Regulatory Networks (GRNs) is an important and challenging problem of Systems Biology. For its superiority in both structure and parameter learning, the S-system model framework is often chosen for GRN reconstruction. The biggest challenge in reconstructing GRNs is the data having large number of genes and only a small number of samples. This "curse of dimensionality", along with the large number of model parameters to be learnt, makes it extremely difficult to reverse engineer even a small network. For a medium or large network, the complexity becomes enormous. In this paper, we propose a method for managing large scale GRN modeling. As first step, we propose an Affinity Propagation Based Clustering to identify appropriate clusters by grouping the genes based on their time expression profiles. In the second step, the largest cluster consisting of majority of the relevant genes is considered in full detail to act as the core of the network while the other remaining clusters, which are not so significant, are each represented by their single representative gene to obtain a reduced order GRN. In the third step, we optimize the entire network by initializing the model parameters of the genes of the largest cluster with the values obtained in the second step (which are near optimal) and proceed to optimize the entire network. The initial investigations are carried out using previously reported 20-gene synthetic network. The superiority of performance is evaluated not only using the standard metrics, namely, sensitivity, specificity, precision and F-score, but also by average mean error and by comparing the time responses with those of the actual network parameters. The results obtained are promising. © 2015 IEEE.
Improving gene regulatory network inference using network topology information
- Authors: Nair, Ajay , Chetty, Madhu , Wangikar, Pramod
- Date: 2015
- Type: Text , Journal article
- Relation: Molecular BioSystems Vol. 11, no. 9 (2015), p. 2449-2463
- Full Text: false
- Reviewed:
- Description: Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.
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.