Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm.

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.

Conversion of dried Aspergillus candidus mycelia grown on waste whey to biodiesel by in situ acid transesterification.

This study reports optimization of the transesterification reaction step on dried biomass of an oleaginous fungus Aspergillus candidus grown on agro-dairy waste, whey. Acid catalyzed transesterification was performed and variables affecting esterification, viz., catalyst methanol and chloroform concentrations, temperature, time, and biomass were investigated. Statistical optimization of the transesterification reaction using Plackett-Burman Design showed biomass to be the predominant factor with a 12.5-fold increase in total FAME from 25.6 to 320mg. Studies indicate that the transesterification efficiency in terms of conversion is favored by employing lower biomass loadings. A. candidus exhibited FAME profiles containing desirable saturated (30.2%), monounsaturated (31.5%) and polyunsaturated methyl esters (38.3%). The predicted and experimentally determined biodiesel properties (density, kinematic viscosity, iodine value, cetane number, TAN, water content, total and free glycerol) were in accordance with international (ASTM D6751, EN 14214) and national (IS 15607) standards.

Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence.

Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

Immobilization of Candida rugosa lipase on poly(allyl glycidyl ether-co-ethylene glycol dimethacrylate) macroporous polymer particles.

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.

Arterial pulse system: modern methods for traditional Indian medicine.

Ayurveda is one of the most comprehensive healing systems in the world and has classified the body system according to the theory of Tridosha to overcome ailments. Diagnosis similar to the traditional pulse-based method requires a system of clean input signals, and extensive experiments for obtaining classification features. In this paper we briefly describe our system of generating pulse waveforms and use various feature detecting methods to show that an arterial pulse contains typical physiological properties. The beat-to-beat variability is captured using a complex B-spline mother wavelet based peak detection algorithm. We also capture--to our knowledge for the first time--the self-similarity in the physiological signal, and quantifiable chaotic behavior using recurrence plot structures.

Nadi Tarangini: a pulse based diagnostic system.

Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini1 contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.

Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines.

In this work, we integrate a non-linear signal analysis method, recurrence quantification analysis (RQA), with the well-known machine-learning algorithm, support vector machines for the binary classification of protein sequences. Two different classification problems were selected, discriminating between aggregating and non-aggregating proteins and mostly disordered and completely ordered proteins, respectively. It has also been shown that classification performance of SVM models improve on selection of the most informative RQA descriptors as SVM input features.

Exhaustive analysis of frontal copolymerization of functionalized monovinyl and divinyl monomers.

A series of copolymers of 2-hydroxyethyl methacrylate (HEMA)/glycidyl methacrylate (GMA) and ethylene dimethacrylate (EGDM) were synthesized by frontal polymerization (FP). This study was conducted to investigate the effect of crosslink density, type and concentration of initiator, the use of a complex initiator system, porogen, and diluent on the most relevant parameters of FP, such as sustainability of the front, temperature profile, front velocity, and yield. The products were also characterized for intruded pore volume, pore-size distribution, epoxy-functionality number, and surface morphology. Higher crosslink densities (CLDs) and initiator concentration produced higher front velocities, whereas no trend in front temperature was noted. A complex initiation system was effective in stabilizing and increasing the polymerization yield. Relative to suspension polymerization (SP), FP products synthesized without a solvent were microporous, whereas micro-to-macroporous products were obtained in the presence of a solvent (for HEMA-EGDM polymers). We also present, explain, and discuss the exotic patterns observed under a microscope. We observed two basic types of spatial patterns, namely, planar and nonplanar patterns. The type of planar pattern observed under scanning electron microscopy (SEM) has a spatial impulse that appears as a loop followed by regular periodic motion in the radial and axial directions. This behavior gives rise to a repeating pattern that is a few microns thick. Also, nonplanar patterns, namely, layered concentric rings and winding staircase patterns, were observed under SEM.

Isometric graphing and multidimensional scaling for reaction-diffusion modeling on regular and fractal surfaces with spatiotemporal pattern recognition.

Heterogeneous surface reactions exhibiting complex spatiotemporal dynamics and patterns can be studied as processes involving reaction-diffusion mechanisms. In many realistic situations, the surface has fractal characteristics. This situation is studied by isometric graphing and multidimensional scaling (IGMDS) of fractal surfaces for extracting geodesic distances (i.e., shortest scaled distances that obtain edges of neighboring surface nodes and their interconnections) and the results obtained used to model effects of surface diffusion with nonlinear reactions. Further analysis of evolved spatiotemporal patterns may be carried out by IGMDS because high-dimensional snapshot data can be efficiently projected to a transformed subspace with reduced dimensions. Validation of the IGMDS methodology is carried out by comparing results with reduction capabilities of conventional principal component analysis for simple situations of reaction and diffusion on surfaces. The usefulness of the IGMDS methodology is shown for analysis of complex patterns formed on both regular and fractal surfaces, and using generic nonlinear reaction-diffusion systems following FitzHugh Nagumo and cubic reaction kinetics. The studies of these systems with nonlinear kinetics and noise show that effects of surface disorder due to fractality can become very relevant. The relevance is shown by studying properties of dynamical invariants in IGMDS component space, viz., the Lyapunov exponents and the KS entropy for interesting situations of spiral formation and turbulent patterns.

Barium hexaaluminate nanowhiskers synthesized by novel sol-gel process in reverse micellar media.

Barium hexaaluminate (BHA) synthesized by coupling of sol-gel process in reverse microemulsions shows a unique nanowhisker morphology and high surface area, which are retained after calcination at 1300 degrees C.

Optimization of media by evolutionary algorithms for production of polyols.

Biotransformation of sucrose-based medium to polyols has been reported for the first time using osmophilic yeast, Hansenula anomala. A new, real coded evolutionary algorithm was developed for optimization of fermentation medium in parallel shake-flask experiments. By iteratively employing the nature-inspired techniques of selection, crossover, and mutation for a fixed number of generations, the algorithm obtains the optimal values of important process variables, namely, inoculum size and sugar, yeast extract, urea, and MgSO4 concentrations. Maximum polyols yield of 76.43% has been achieved. The method is useful for reducing the overall development time to obtain an efficient fermentation process.

Analysis of DNA curvature distribution in mycobacterial promoters using theoretical models.

In this paper, 125 different mycobacterial promoters are analyzed for their DNA curvature distribution using several di- and tri-nucleotide dependent models of DNA curvature. Different models give similar behavior and therefore qualitative validation of the results. Mycobacterial promoters resembling the E. coli sigma(70) type have almost 81% (85%) sequences having medium and high curvature profiles using dinucleotide-dependent models. Non-E. coli sigma(70) type mycobacterial promoters have comparatively higher percent of low curvature profiles. Very few extended -10 promoters have low curvature profiles. Mycobacterial promoters having A(n)T(m) (n+m > or =3) tract in the upstream region of -35 box and repeated in phase with each other have high curvature profiles. M. smegmatis promoters have high curvature profiles compared to M. tuberculosis promoters.

Parameter estimation in spatially extended systems: the Karhunen-Lóeve and Galerkin multiple shooting approach.

Parameter estimation for spatiotemporal dynamics for coupled map lattices and continuous time domain systems is shown using a combination of multiple shooting, Karhunen-Loéve decomposition and Galerkin's projection methodologies. The resulting advantages in estimating parameters have been studied and discussed for chaotic and turbulent dynamics using small amounts of data from subsystems, availability of only scalar and noisy time series data, effects of space-time parameter variations, and in the presence of multiple time scales.

Synthesis and Characterization of Ferrierite-Type Zeolite in the Presence of Nonionic Surfactants.

Pure-phase ferrierite (FER) zeolite has been synthesized in the presence of small amounts of nonionic surfactants, Tween 20 and Tween 80. The input SiO(2)/Al(2)O(3) molar ratio has been varied over a range of 25 to 150. The results confirm that a small amount of pyrrolidine acts as a template and is necessary. The effects of varying concentration of surfactant on ferrierite crystallization and output SiO(2)/Al(2)O(3) molar ratio are reported. The results also show that in the absence of pyrrolidine, ZSM-5 cocrystallized with FER, whereas in the absence of a nonionic surfactant the yield and crystallization of FER are very poor. The XRF analysis shows the changes in SiO(2)/Al(2)O(3) output molar ratios as the surfactant concentration changes. The XRD data show that the samples are fully crystalline and pure. The TG/DTA of the sample shows its structural stability. The scanning electron microscopy results show changes in particle size with varying concentration of nonionic surfactant. Copyright 2001 Academic Press.

Synthesis of Ferrierite-Type Zeolite in the Presence of a Catalytic Amount of Pyrrolidine and Sodium Bis(2-ethyhlhexyl) Sulfosuccinate.

Synthesis of ferrierite (FER) type zeolite with varying Si to Al ratios in the presence of a catalytic amount of pyrrolidine and sodium bis(2-ethylhexyl) sulfosuccinate (AOT) in aqueous media is reported. The surfactant moieties direct pyrrolidine molecules in a particular fashion, resulting in a sixfold decrease in the required amount of template as compared to conventional procedure, for FER crystallization. The product obtained was highly crystalline and pure. In the presence of AOT alone, ferrierite co-crystallized with the ZSM-5 phase, indicating AOT is not acting as a structure-directing agent and a small concentration of pyrrolidine ( approximately 2 wt%) template is essential for the ferrierite crystallization. A scanning electron micrograph showed uniformity in crystals (average 2-3 µm) consisting of broad plate type morphology. The crystal structure of FER (AOT/Py) maintains structural integrity until about 1000 degrees C. FER (AOT/Py) has been further characterized by employing XRD, XRF, IR, and TG/DTA techniques. Copyright 2001 Academic Press.

Dynamic optimization of fed-batch bioreactors using the ant algorithm.

The ant colony algorithm, mimicking the cooperative search behavior of ants in real life, has been employed for the dynamic optimization of fed-batch bioreactors. To test the capability of this new heuristic algorithm, two well-known and extensively studied systems have been chosen. The algorithm rapidly converges to optimal feed rate profiles, which maximize the overall production of the desired product and the profits in a computationally efficient and robust manner. The optimal profiles evolved are easy to implement in plant operation. The algorithm compares favorably with the other known techniques.

Dynamic optimization of chemical processes using ant colony framework.

Ant colony framework is illustrated by considering dynamic optimization of six important bench marking examples. This new computational tool is simple to implement and can tackle problems with state as well as terminal constraints in a straightforward fashion. It requires fewer grid points to reach the global optimum at relatively very low computational effort. The examples with varying degree of complexities, analyzed here, illustrate its potential for solving a large class of process optimization problems in chemical engineering.

ANN modeling of DNA sequences: new strategies using DNA shape code.

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models.

Optimum DNA curvature using a hybrid approach involving an artificial neural network and genetic algorithm.

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the RL value. Using the ANN-GA methodology, a number of sequences possessing high RL values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high RL values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.

Obtaining functional form for chaotic time series evolution using genetic algorithm.

A genetic algorithm (GA) based strategy is presented for deducing an exact or near-exact functional form from a time series. The GA formalism proposed here utilizes (i) the "postfix" representation with a view to reduce the procedural complexities and (ii) the "elitist mating" scheme to produce fitter offspring strings. The GA procedure is exemplified by considering chaotic time series of the well-known logistic, Henon and universal maps. The GA correctly recovers the underlying functional forms for the respective time series. Measurements from a number of finite-dimensional physical, biological, and other systems often give rise to complex time series and the presented methodology should prove useful in obtaining functional forms describing accurately the evolution of the time series. (c) 1999 American Institute of Physics.