Scientific Misconduct in India: Causes and Perpetuation.

Along with economic strength, space technology and software expertise, India is also a leading nation in fraudulent scientific research. The problem is worsened by vested interests working in concert for their own benefits. These self-promoting cartels, together with biased evaluation methods and weak penal systems, combine to perpetuate scientific misconduct. Some of these issues are discussed in this commentary, with supporting examples and possible solutions.

Transient response analysis of the eukaryotic chemosensory system to intra-cellular fluctuations.

Like prokaryotic cells, those of eukaryotes are also subjected to noise from within the cells. While the cells have a built-in mechanism to attenuate the noise, conditions may arise where this is beyond the cell's ability to regulate. Start-up perturbations and those induced by metabolic shifts are examples of such situations. Then, it becomes useful to understand how the cells respond. For a eukaryotic chemosensory system, this has been studied by applying response coefficient analysis to a recent model. With even three dependent variables - an activator, an inhibitor, and a response element - the response coefficients differ widely with time and from one variable to another. These differences are interpreted in terms of the chemosensory mechanism and its robustness. The results complement similar recent studies of Escherichia coli chemotaxis, thus supporting their credibility and versatility.

Cognitive optimization of microbial PHB production in an optimally dispersed bioreactor by single and mixed cultures.

Cognitive (or intelligent) models are often superior to mechanistic models for nonideal bioreactors. Two kinds of cognitive models--cybernetic and neural--were applied recently to fed-batch fermentation by Ralstonia eutropha in a bioreactor with optimum finite dispersion. In the present work, these models have been applied in simulation studies of co-cultures of R. eutropha and Lactobacillus delbrueckii. The results for both cognitive and mechanistic models have been compared with single cultures. Neural models were the most effective for both types of cultures and mechanistic models the least effective. Simulations with co-culture fermentations predicted more PHB than single cultures with all three types of models. Significantly, the predicted enhancements in PHB concentration by cognitive methods for mixed cultures were four to five times larger than the corresponding increases in biomass concentration. Further improvements are possible through a hybrid combination of all three types of models.

Response coefficient analysis of a fed-batch bioreactor to dissolved oxygen perturbation in complementary cultures during PHB production.

BACKGROUND: Although the production of poly-beta-hydroxybutyrate (PHB) has many biological, energetic and environmental advantages over chemically synthesized polymers, synthetic polymers continue to be produced industrially since the productivities of fermentation processes fr PHB are not yet economically competitive. Improvement of a PHB fermentation requires good understanding and optimization under the realistic conditions of large bioreactors.Laboratory-scale studies have shown that co-cultures of Ralstonia eutropha and Lactobacillus delbrueckii generate better fermentation efficiencies than R. eutropha alone. In large bioreactors, incomplete dispersioin and perturbations in the dissolved oxygen (DO) concentration, both of which affect the fermentation, have to be considered. This study analyzes the effect of DO fluctuations on bioreactor performance for both ideal and optimally dispersed broths. RESULTS: Response coefficient analysis was employed to obtain quantitative information on the effect of DO perturbations on different variables. Three values of the Peclet number (Pe) cheracterized three levels of dispersion: Pe = 0.01 for nearly complete dispersion, Pe = 20 for optimum dispersion and Pe = 60 for insufficient dispersion. The response coefficients (RCs) of the pairs of bacterial concentrations and the main substrates, glucose and ammonium chloride, showed contrasting variations with time. Lactate, a critical intermediate, and PHB had similar RC profiles but those of lactate were one to two orders of magnitude larger than other RCs. Significantly, the optimum Pe also resulted in the largest RCs, suggesting a balance between productivity and reactor stability. CONCLUSION: Since R. eutropha requires oxygen for its growth whereas L. delbrueckii does not, fluctuations in the DO concentartion have a strong influence on the fermentation. Apart from this, the mechanism of PHB biosynthesis indicates that control of lactate is a critical determinant of fermentation efficiency. The RC profiles indicate that, under non-ideal conditions, a compromise may be required between PHB formation and reactor stability, especially in the latter half of the process.

"Intelligent" descriptions of microbial kinetics in finitely dispersed bioreactors: neural and cybernetic models for PHB biosynthesis by Ralstonia eutropha.

BACKGROUND: For many microbial processes, the complexity of the metabolisms and the responses to transient and realistic conditions are difficult to capture in mechanistic models. The cells seem to have an innate intelligence that enables them to respond optimally to environmental changes. Some "intelligent" models have therefore been proposed and compared with a mechanistic model for fed-batch cultures of Ralstonia eutropha. RESULTS: Two kinds of models have been proposed to describe such cellular behavior. Cybernetic models are derived through postulates of cellular intelligence and memory, and neural models use artificial intelligence through neural networks. Some competing models of both kinds have been compared for their ability to portray and optimize the synthesis of poly-beta-hydroxybutyrate by Ralstonia eutropha in fed-batch cultures with finite dispersion. Neural models enabled the formation of more of the polymer than cybernetic models, with lesser utilization of the carbon and nitrogen substrates. Both types of models were decidedly superior to a mechanistic model used as a reference, thus supporting the value of intelligent descriptions of microbial kinetics in incompletely dispersed bioreactors. CONCLUSION: Neural and cybernetic models describe and optimize unsteady state fed-batch microbial reactors with finite dispersion more effectively than mechanistic models. However, these "intelligent" models too have weaknesses, and hence a hybrid approach combining such models with some mechanistic features is suggested.

Robustness analysis of the E. coli chemosensory system to perturbations in chemoattractant concentrations.

MOTIVATION: Cells of Escherichia coli sense and move toward chemical attractants. This is done through an intricate sensory system that eventually directs the movements of flagellae which regulate the 'runs' and 'tumbles' of the cells. Under realistic conditions, chemical stimuli often fluctuate due to noise from the environment. The effect of noise on the chemosensory system has been investigated here through the sensitivity coefficients of the concentrations of four key proteins--the phosphorylated forms of CheA, CheB and CheY, and the FliM-CheY-P complex--that govern chemotactic motility. The letter P denotes phosphorylation. RESULTS: All sensitivities increased with time and then stabilized. However, the four sets of sensitivities differed in their magnitudes and the durations of their transient phases before stabilization. CheA-P was the least sensitive and CheY-P the most sensitive. Moreover, while the sensitivities of CheA-P, CheB-P and CheY-P increased with chemoattractant concentration, that of the FliM complex decreased. These differences have been interpreted in terms of the mechanism of the chemosensory system and they have important implications for practical applications of chemotaxis.

Hybrid filtering of feed stream noise from oscillating yeast cultures by combined Kalman and neural network configurations.

Large continuous flow bioreactors are often under the influence of noise in the feed stream(s). Prior removal of noise is done by filters based either on specific algorithms or on artificial intelligence. Neither method is perfect. Hybrid filters combine both methods and thereby capitalize on their strengths while minimizing their weaknesses. In this study, a number of hybrid models have been compared for their ability to recover nearly noise-free stable oscillations of continuous flow Saccharomyces cerevisiae cultures from aberrant behavior caused by noise in the feed stream. Each hybrid filter had a different neural network in conjunction with an extended Kalman filter (EKF). The choice of the best configuration depended on the performance index. All hybrid filters were superior to both the EKF and purely neural filters. Along with previous studies of monotonic fermentations, the present results establish the suitability of hybrid neural filters for noise-affected bioreactors.

Hybrid filtering to rescue stable oscillations from noise-induced chaos in continuous cultures of budding yeast.

In large-scale fermentations with oscillating microbial cultures, noise is commonly present in the feed stream(s). As this can destabilize the oscillations and even generate chaotic behavior, noise filters are employed. Here three types of filters were compared by applying them to a noise-affected continuous culture of Saccharomyces cerevisiae with chaotic oscillations. The aim was to restore the original noise-free stable oscillations. An extended Kalman filter was found to be the least efficient, a neural filter was better and a combined hybrid filter was the best. In addition, better filtering of noise was achieved in the dilution rate than in the oxygen mass transfer coefficient. These results suggest the use of hybrid filters with the dilution rate as the manipulated variable for bioreactor control.

Dispersion optimization to enhance PHB production in fed-batch cultures of Ralstonia eutropha.

Despite its many useful properties, microbial production of poly-beta-hydroxybutyrate (PHB) is not yet commercially competitive with synthetic polymers. One reason is inadequate optimization of the fermentation under industrial conditions. In this study, a physiologically reasonable and experimentally validated kinetic model for PHB synthesis by Ralstonia eutropha was incorporated into a dispersion model to simulate a large fed-batch bioreactor. Solutions of the model indicated that cell growth and PHB synthesis were maximum at Peclet numbers (Pe) between 20 and 30, representing limited finite dispersion. At these Peclet numbers, the optimum feed rates also showed lower consumptions of the substrates than at Pe=0. Since complete dispersion was also difficult to achieve in production-scale bioreactors, these results pointed to the possibility of exploiting controlled dispersion for productivity enhancement.

Perspectives in the modeling and optimization of PHB production by pure and mixed cultures.

Poly(beta-hydroxybutyrate) or PHB is an important member of the family of polyhydroxyalkanoates with properties that make it potentially competitive with synthetic polymers. In addition, PHB is biodegradable. While the biochemistry of PHB synthesis by microorganisms is well known, improvement of large-scale productivity requires good fermentation modeling and optimization. The latter aspect is reviewed here. Current models are of two types: (i) mechanistic and (ii) cybernetic. The models may be unstructured or structured, and they have been applied to single cultures and co-cultures. However, neither class of models expresses adequately all the important features of large-scale non-ideal fermentations. Model-independent neural networks provide faithful representations of observations, but they can be difficult to design. So hybrid models, combining mechanistic, cybernetic and neural models, offer a useful compromise. All three kinds of basic models are discussed with applications and directions toward hybrid model development.

Oscillatory metabolism of Saccharomyces cerevisiae: an overview of mechanisms and models.

The budding yeast Saccharomyces cerevisiae displays steady oscillations in continuous cultures under certain conditions. Oscillatory responses are important both metabolically and in process applications. Although much information has become available, a definitive theory to explain and model these oscillations is yet to be formulated. Models of oscillatory cultivation have focussed primarily either on intracellular reactions or on transport processes coupled to substantially lumped intracellular kinetics. This review discusses the development of the models and the directions they provide for a comprehensive model of oscillatory metabolism.

Production and characterization of a thermostable beta-galactosidase from Bacillus coagulans RCS3.

A strain of Bacillus coagulans RCS3 isolated from a hot-water spring produced significant beta-galactosidase activity at 10 days of growth in a flask. While enzyme production was maximum at 50 degrees C, the highest activity was at 65 degrees C, where the half-life was 2 h. A 2 degrees C decrease in temperature increased the half-life to 15 h without significantly changing the activity, suggesting that 63 degrees C is the temperature of preference compared with 65 degrees C for a combination of good activity and stability. The beta-galactosidase was also stable over pH 5-8, with peak activity at pH 6-7. It was strongly and competitively inhibited by the hydrolysis product galactose. Bivalent cations (Cu(2+), Ni(2+) and Hg(2+)) in the concentration range of 0.5-2.0 mM also inhibited enzyme activity. Both lactose solution and whey could be hydrolysed substantially within 36 h at 50 degrees C. The thermostability and pH-stability and good hydrolytic capability make this enzyme potentially useful in the dairy industry.

Can imperfections help to improve bioreactor performance?

Pilot-scale and larger bioreactors differ from small laboratory-scale reactors in terms of a greater occurrence of noise and incomplete mixing of the broth. Conventional control tries to induce good mixing and to filter out the noise as completely as possible. As such an 'ideal' operation is difficult to achieve, recent work has tried to exploit the non-ideal features to improve the performance. Using artificial neural networks, the degree of mixing, the extent of filtering of noise and the distribution of plasmid copy number (in a recombinant fermentation) can be controlled effectively on-line. This strategy generates better productivities than well-mixed noise-free operations, which suggests that deviations from ideal behaviour should be gainfully harnessed and not suppressed.