Identification of coding and non-coding sequences using local holder exponent formalism - Kulkarni, O. C.; Vigneshwar, R.; Jayaraman, Valadi K.; Kulkarni, B. D. (Bioinformatics, 2005)
Motivation: Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. Results: The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences.

Improved time series prediction with a new method for selection of model parameters - Jade, A. M.; Jayaraman, Valadi K.; Kulkarni, B. D. (Journal of Physics A-Mathematical and General, 2006)
A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Holder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data.

Improve polyethylene process control and product quality - using artificial intelligence-based sensors can improve costs - Badhe, Yogesh P.; Lonari, J.; Tambe, S. S.; Kulkarni, B. D.; Valecha, N. K.; Deshmukh, S. V.; Ravichandran, S. (Hydrocarbon Processing, 2007)

Identification and characterization of flow structures in chemical process equipment using multiresolution techniques - Deshpande, Sagar S.; Joshi, Jyeshtharaj B.; Kumar, V. Ravi; Kulkarni, B. D. (Chemical Engineering Science, 2008)
Planar information of velocity from 2D particle image velocimetry (PIV) and large eddy simulation (LES) data have been studied using multiresolution wavelet transform (WT) formalisms, i.e., discrete and continuous WT. Identification of dominant energy containing structures with their characterization in terms of fractal spectra have been carried out for industrially important equipment exhibiting turbulent behavior. These include annular centrifugal contactor, jet loop reactor, ultrasound reactor, channel flow, stirred tank and bubble column reactor. The characterization of their dynamics based on denoising the data and studying the local energy along the WT scales show sensitive variation and this helps in identifying the size and shape of structures. A dependency is seen between mixing time and the higher order moments of length scale distribution, viz., skewness and kurtosis and a generalized correlation has been built up for important types of equipment and associated flow parameters. The correlation is not only based on the knowledge of reactor geometry and operating conditions but also on the flow structures via their statistical parameters. Wavelet transform modulus maxima (WTMM) methodology has been used to study the evolution of structures and their interaction in a reduced dimensionality by evaluating the fractal spectra. Classification studies have been carried out using principal component analysis (PCA) of the fractal spectra. The results obtained show clear classes for the six types of equipments and delineate regimes to obtain benchmark patterns of flow hydrodynamics based on PCA co-ordinates. This methodology offers a generalized way for the optimal design and operation of different types of reactors. (c) 2008 Published by Elsevier Ltd .
Keywords: Chemical reactors; hydrodynamics; Length scale distribution; mixing; Model reduction; Turbulence; Wavelet transform

Immobilization of Candida rugosa lipase on poly(allyl glycidyl ether-co-thylene glycol dimethacrylate) macroporous polymer particles - Vaidya, Bhalchandra K.; Ingavle, Ganesh C.; Ponrathnam, S.; Kulkarni, B. D.; Nene, Sanjay N. (Bioresource Technology, 2008)
Macroporous polymer particles containing surface epoxy groups were synthesized for immobilization of Candida rugosa lipase (CRL). The effect of incorporation of two different sets of monomers [allyl glycidyl ether (AGE) and glycidyl methacrylate (GMA)] and the effect of crosslinking density on immobilization of lipase were studied. AGE-co-EGDM polymers gave higher binding and expression of lipase than GMA-co-EGDM polymers. Optimization of immobilization parameters was done with respect to immobilization time and enzyme loading. Amongst AGE-co-EGDM polymer series, AGE-150 polymer found to give maximum lipase activity yield and therefore evaluated for temperature, pH and storage stability. Under optimum conditions, AGE-150 polymer gave 78.40% of activity yield. Immobilized lipase on AGE-150 showed a broader pH, higher temperature and excellent storage stability. (C) 2007 Published by Elsevier Ltd.
Keywords: Candida rugosa lipase; Enzyme immobilization; Epoxy-activated support

Identification of N-glycosylation sites with sequence and structural features employing random forests - Karnik, Shreyas; Mitra, Joydeep; Singh, Arunima; Kulkarni, B. D.; Sundarajan, V.; Jayaraman, Valadi K. (Pattern Recognition and Machine Intelligence, Proceedings, 2009)
N-Glycosylation plays a very important role in various processes like quality control of proteins produced in ER, transport of proteins and in disease control. The experimental elucidation of N-Glycosylation sites is expensive and laborious process. In this work we build models for identification of potential N-Glycosylation sites in proteins based on sequence and structural features. The best model has cross validation accuracy rate of 72.81%.
3rd International Conference on Pattern Recognition and Machine Intelligence, IIT Delhi, New Delhi, INDIA, DEC 16-20, 2009

Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm - Sarode, Ketan Dinkar; Kumar, V. Ravi; Kulkarni, B. D. (Mathematical Biosciences, 2016)
An efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. (C) 2016 Elsevier Inc. All rights reserved.