Faculty Publications
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Item Efficient shape descriptors for feature extraction in 3D protein structures(2007) Ranganath, A.; Shet, K.C.; Vidyavathi, N.Structural Genomics initiatives are generating an increasing number of protein structures with very limited biochemical characterization. Characterization of a protein's function and understanding the specific nature of a protein's binding is a critical part of both protein engineering and structure-based drug discovery. The accurate detection of binding site in these protein structures can be valuable in determining its function. As shape plays a crucial role in bimolecular recognition and function, the development of shape analysis techniques is important for understanding protein structure-function relationships. This paper describes the use of the continuous wavelet transforms (CWT) for characterizing shape features of 3D protein structures. The goal is to explore the CWT as a multiscale tool to generate rotation- and translation-invariant shape features. © 2007 IOS Press. All rights reserved.Item Modelling and simulation of steady-state phenol degradation in a pulsed plate bioreactor with immobilised cells of Nocardia hydrocarbonoxydans(2011) Shetty K, V.S.; Verma, D.K.; Srinikethan, G.A novel bioreactor called pulsed plate bioreactor (PPBR) with cell immobilised glass particles in the interplate spaces was used for continuous aerobic biodegradation of phenol present in wastewater. A mathematical model consisting of mass balance equations and accounting for simultaneous external film mass transfer, internal diffusion and reaction is presented to describe the steady-state degradation of phenol by Nocardia hydrocarbonoxydans (Nch.) in this bioreactor. The growth of Nch. on phenol was found to follow Haldane substrate inhibition model. The biokinetic parameters at a temperature of 30 ± 1 °C and pH at 7.0 ± 0.1 are ? m = 0.5397 h -1, K S = 6.445 mg/L and K I = 855.7 mg/L. The mathematical model was able to predict the reactor performance, with a maximum error of 2% between the predicted and experimental percentage degradations of phenol. The biofilm internal diffusion rate was found to be the slowest step in biodegradation of phenol in a PPBR. © 2010 Springer-Verlag.Item Computational investigations on the hemodynamic performance of a new swirl generator in bifurcated arteries(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2019) Prashantha, B.; Anish, S.Hemodynamic behaviour of blood in the bifurcated arteries are closely related to the development of cardiovascular disease. The secondary flows generated at the bifurcation zone promotes the deposition of atherogenic particles on the outer walls. The present study aims at suppressing the development of atherosclerosis plaque by inducing helical flow structure in the arterial passage. To realize this objective a novel swirl generator (stent like structure with an internal groove) has been developed to induce helicity in the bifurcated passage. The functional requirement of the swirl generator is to minimize the relative residence time (RRT) of the fluid layer near the endothelial wall without generating any additional pressure drop. Different configurations of the swirl generator have been tested computationally using large eddy simulation (LES) model. It is observed that the induced helical flow redistributes the kinetic energy from the centre to the periphery. A single rib swirl flow generator proximal to the stent treated passage can generate sufficient helicity to bring down the RRT by 36% without generating any additional pressure drop. The swirl flow adds azimuthal instability which increase vortex formations in the passage. The induced helical flow in the domain provokes more linked vortices, which may act as self-cleaning mechanism to the arterial wall. © 2018, © 2019 Informa UK Limited, trading as Taylor & Francis Group.Item Clumped-MCEM: Inference for multistep transcriptional processes(Elsevier Ltd, 2019) Shetty, K.S.; B, A.Many biochemical events involve multistep reactions. Among them, an important biological process that involves multistep reaction is the transcriptional process. A widely used approach for simplifying multistep reactions is the delayed reaction method. In this work, we devise a model reduction strategy that represents several OFF states by a single state, accompanied by specifying a time delay for burst frequency. Using this model reduction, we develop Clumped-MCEM which enables simulation and parameter inference. We apply this method to time-series data of endogenous mouse glutaminase promoter, to validate the model assumptions and infer the kinetic parameters. Further, we compare efficiency of Clumped-MCEM with state-of-the-art methods – Bursty MCEM2 and delay Bursty MCEM. Simulation results show that Clumped-MCEM inference is more efficient for time-series data and is able to produce similar numerical accuracy as state-of-the-art methods – Bursty MCEM2 and delay Bursty MCEM in less time. Clumped-MCEM reduces computational cost by 57.58% when compared with Bursty MCEM2 and 32.19% when compared with delay Bursty MCEM. © 2019 Elsevier LtdItem Multi-ENPS simulator support tool with automatic file inter-conversion and multi-membrane execution(Elsevier Ireland Ltd, 2020) Raghavan, S.; Gangadhar, Y.; Pattar, V.; Chandrasekaran, K.P System or Membrane Computing is an unconventional and natural computing model inspired by the functioning of a living cell. This model has an inherently parallel structure. There are several variants of P System developed, each of which has a different application. One of the variants, Enzymatic Numerical P System (ENPS), has primarily been developed to be used with numerical values (as in economics) and thus has vast applications. For realizing ENPS there are several tools available, primarily based on Java and Python, each of which has a different input format. Currently, there is no tool which allows the user to execute ENPS using both the simulators on the same platform, the issue being inter-conversion between input formats, namely, XML and PeP (specific format designed for Python based ENPS). Another major issue with existing simulators is their inability to allow multiple membrane systems to be executed and there is no facility for interconnection between two membrane systems. A tool developed here solves both problems namely, file inter-conversion and multiple membrane support by transferring dependent variable values automatically according to users’ choice. The tool is developed using Python 3.0 and has only a few dependencies. The tool is tested under different scenarios and the results confirm the correctness of the tool. © 2019 Elsevier B.V.Item GPUPeP: Parallel Enzymatic Numerical P System simulator with a Python-based interface(Elsevier Ireland Ltd, 2020) Raghavan, S.; Rai, S.S.; Rohit, M.P.; Chandrasekaran, K.Membrane computing is a computational paradigm inspired by the structure and behavior of a living cell. P Systems are the computing devices that are used to realize membrane computing models. Numerous theoretical studies on many variants of P Systems have shown them to be computationally universal. There is a wide range of applications of P Systems from modeling of biological processes to image processing. Among many variants of P Systems, one of the most important is Enzymatic Numerical P System (ENPS). ENPS is a class of P System in which membranes operate on numerical values. To realize the power of ENPS there are a few simulators developed. Each and every simulator has some advantages as well as some disadvantages. Here, a GPU based simulator using Python as a user interaction language is developed. This tool is a completely parallel variant, compatible with a Python based sequential simulator (PeP) which was the first Python based work for ENPS. The developed simulator uses CUDA to interact with GPU and gives the desired speed up, while processing the membranes. There are two important case studies which show the performance of the developed tool to be far better than the other serial simulators. © 2020 Elsevier B.V.Item Estimation of tumor parameters using neural networks for inverse bioheat problem(Elsevier Ireland Ltd, 2021) Majdoubi, J.; Iyer, A.S.; Ashique, A.M.; Arumuga Perumal, D.A.; Mahrous, Y.M.; Rahimi-Gorji, M.; Issakhov, A.Background and objective: Some types of cancer cause rapid cell growth, while others cause cells to grow and divide at a slower rate. Certain forms of cancer result in visible growths called tumors. This work proposes an inverse estimation of the size and location of the tumor using a feedforward Neural Network (FFNN) model. Methods: The forward model is a 3D model of the breast induced with a tumor of various sizes at different locations within the breast, and it is solved using the Pennes equation. The data obtained from the simulation of the bioheat transfer is used for training the neural network. In order to optimize the neural network architecture, the work proposes varying the number of neurons in the hidden layer and thus finding the best fit to create a relationship between the temperature profile and tumor parameters which can be used to estimate the tumor parameters given the temperature profile. Results: These simulations resulted in a temperature distribution profile that could thus be used to locate and determine the parameters of the cancerous tumor within the breast. The prediction accuracy showed the capacity of the trained Feed Forward Neural Network to estimate the unknown parameters within an acceptable range of error. The model validations use the Root Mean Square Error method to quantify and minimize the prediction error. Conclusions: In this work, a non-intrusive method for the diagnosis of breast cancer was modelled, which yields conclusive results for the estimation of the tumor parameters. © 2021Item CFD modelling of an immobilised photocatalytic reactor for phenol degradation(IWA Publishing, 2023) Devipriya, B.; Mohanan, S.; Surenjan, A.Photocatalysis is an advanced oxidation process, which has been gaining attention as a sustainable technology for tackling pollution. Optimum design, fabrication and scaling up of novel photocatalytic reactors are faced with problems such as fabrication cost and numerous experimental trials for optimisation. Computational fluid dynamics (CFD), a computer simulation technique can ease the process of scaling up photocatalytic reactors. The current study focuses on CFD modelling of a serpentine flow path photocatalytic reactor with curved baffles for phenol degradation. The investigation compared different reactor configurations to finalise the optimum design with maximum removal efficiency. Initially, a simple cuboidal reactor was chosen with an efficiency of 27%. However, with a serpentine flow path being introduced, the reactor displayed an improved efficiency of 42%. The addition of baffles improved flow homogeneity and degradation efficiency. The investigation showed that serpentine flow increased the residence time and fluid mixing, while the curved baffles prevented flow channelisation, which enhanced the degradation efficiency. Efficiencies corresponding to different baffle types and geometry were also compared and the final reactor design chosen was a horizontal curved baffled serpentine flow reactor with a flow rate of 0.3 L/s and improved efficiency of 43.1% for a residence time of 18.44 s. © 2023 The Authors.Item An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs(Springer Nature, 2025) Etikyala, U.; Reddyrajula, R.; Vani, T.; Kuchana, V.; Udayakumar, U.; Vijjulatha, V.Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1–D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein–ligand complex. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.