Abhinandan Khan

Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

A swarm based global routing optimization scheme

Swarm Intelligence (SI), modelled upon the behaviours of various swarms of animals and insects such as ants, termites, bees, birds, fishes, fireflies, etc. is an emerging area in the field of optimization. SI based algorithms are proclaimed to be robust and efficient optimization tools. This fact is corroborated by a number of practical engineering problems where these algorithms give very satisfactory results. Nowadays VLSI Design has become one of the most intriguing and fervent research field for engineers. Efficient development of a system of a billion chips and blocks on a printed circuit board requires extensive use of optimization in various areas of design such as chip size, separation among components, interconnect length etc. One of the most significant among these is the interconnect wirelength, which determines the overall delay in transmission within the chip. The routing phase in the VLSI Physical Design strives to optimize the interconnect length. Several studies have been and are being conducted to improve the performance of VLSI chips by optimally interconnecting the various components. Various SI based algorithms have already proved their efficiency in this field of routing optimization. In this paper we have proposed a global routing scheme based on contemporary SI algorithms: Firefly Algorithm (FA), and Artificial Bee Colony (ABC) algorithm and have compared the performance of the two. FA produces superior optimization results in comparison to ABC although proving to be quite expensive, computationally.

A Global Routing Optimization Scheme Based on ABC Algorithm

Rapid technological advancements are leading to a continuous reduction of integrated chip sizes. An additional steady increase in the chip density is resulting in device performance improvements as well as severely complicating the fabrication process. The interconnection of all the components on a chip, known as routing, is done in two phases: global routing and detail routing. These phases impact chip performance significantly and hence researched extensively today. This paper deals with the global routing phase which is essentially a case of finding a Minimal Rectilinear Steiner Tree (MRST) by joining all the terminal nodes, known to be an NP-hard problem. There are several algorithms which return near optimal results. Recently algorithms based on Evolutionary Algorithms (such as Genetic Algorithm) and based on Swarm Intelligence (such as PSO, ACO, ABC, etc.) are being increasingly used in the domain of global routing optimization of VLSI Design. Swarm based algorithms are an emerging area in the field of optimization and this paper presents a swarm intelligence algorithm, Artificial Bee Colony(ABC) for solving the routing optimization problem. The proposed algorithm shows noteworthy improvements in reduction of the total interconnect length. The performance of this algorithm has been compared with FLUTE (Fast Look Up Table Estimation) that uses Look Up Table to handle nets with degree up to 9 and net breaking technique for nets with degree up to 100. It is used for VLSI applications in which most of the nets have a degree 30 or less than that.

Gene regulatory networks using bat algorithm inspired particle swarm optimization

Here, we have proposed a statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data. The recurrent neural network formalism has been implemented to extract the underlying dynamics from time series microarray datasets accurately. The proposed swarm intelligence framework has been used for optimising the parameters of the recurrent neural network model. Preliminary research with the proposed methodology has been done on a small, artificial network and the experimental (in vivo) microarray data of the SOS DNA repair network of Escherichia coli. Results obtained suggest that the proposed methodology can infer the underlying network structures with a better degree of success.

An Approach for Reduction of False Predictions in Reverse Engineering of Gene Regulatory Networks

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives.

A swarm intelligence based scheme for reduction of false positives in inferred gene regulatory networks

A gene regulatory network reveals the regulatory relationships among genes at a cellular level. The accurate reconstruction of such networks using computational tools, from time series genetic expression data, is crucial to the understanding of the proper functioning of a living organism. Investigations in this domain focused mainly on the identification of as many true regulations as possible. This has somewhat overshadowed the reduction of false predictions in inferred networks. In the present investigation, we have proposed a novel scheme, based on different swarm intelligence algorithms, to reduce the number of inferred false regulations. We have first applied our proposed methodology on the much studied, benchmark experimental datasets of the DNA SOS repair network of Escherichia Coli. Subsequently, we have experimented upon a larger, in silico network extracted from the GeneNetWeaver database. The obtained results suggest that the proposed methodology can reduce the number of false predictions, significantly, without using any supplementary biological information for larger gene regulatory networks.

A Novel Technique for Reduction of False Positives in Predicted Gene Regulatory Networks

In this paper, we have proposed a novel method for the reduction of the number of inferred false positives in gene regulatory networks, constructed from time-series microarray genetic expression datasets. We have implemented a hybrid statistical/swarm intelligence technique for the purpose of reverse engineering genetic networks from temporal expression data. The theory of combination has been used to reduce the search space of network topologies effectively. Recurrent neural networks have been employed to obtain the underlying dynamics of the expression data accurately. Two swarm intelligence techniques, namely, Particle Swarm Optimisation and a Bat Algorithm inspired variant of the same, have been used to train the corresponding model parameters. Subsequently, we have identified and used their common portions to construct a final network where the incorrect predictions have been filtered out. We have done preliminary investigations on experimental (in vivo) data sets of the real-world SOS DNA repair network in Escherichia coli. Furthermore, we have implemented our proposed algorithm on medium-scale networks, consisting of 10 and 20 genes. Experimental results are quite encouraging, and they suggest that the proposed methodology is capable of reducing the number of false positives, thus, increasing the overall accuracy and the biological plausibility of the predicted genetic networks.

An artificial bee colony optimization based global routing technique

Artificial Bee Colony (ABC) algorithm is a very efficient optimization algorithm that uses the foraging behavior of honeybees. ABC has been quite extensively studied upon and applied to solve many real world engineering problems. Routing is one of the crucial stages in physical design, on which the performance of the entire chip depends. Global routing is the first step of routing where the nets in different blocks of the chip are loosely interconnected. Detailed routing which follows after global routing determines the particular points through which actual routing is to be done. So global routing is the most important part of routing and the proper interconnection between nets with the most optimum wire length depends on the performance of this stage. This paper presents an algorithm based on ABC for calculating the wire length in global routing and the optimized results are compared against the standard router NTHU 2.0. NTHU-Route 2.0 is a quick and robust global router which solves all ISPD benchmarks maintaining very good quality.