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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.

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.

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.

Even after many years of research and industrial practice, the production of penicillin G in fed-batch fermentation by Penicillium crysogenum continues to attract research interest. There are many reasons: the commercial and therapeutic importance of penicillin and its derivatives, the complexity of cell growth, and the impact of engineering variables, the last of which are significant in large bioreactors but are not yet fully understood. Extensive research has generated new information on the mechanisms of cellular reactions and morphological features of the mycelia and their role in the synthesis of the product. Given a choice of mechanisms, models of different degrees of complexity, for both cellular differentiation and bioreactor performance, have been proposed. The more complex models require and provide more information. They are also more difficult to evaluate and apply in automatic control systems for production-scale bioreactors. The present review considers the evolution of recent knowledge and models from this perspective.

Monocyclic enzyme cascades are important regulators of biochemical reactions in living cells. The reaction network may have either one steady state or many, depending on its structure. The occurrence of multiple steady states has important biological implications. A simple graph-theoretic method has been applied to five reaction mechanisms—which together cover many common monocyclic cascades—to determine which mechanisms generate just one steady state and which ones allow more than one state. It is shown that an unstable steady state in a multiplicity region can be usefully exploited and that in some cases transitions may occur between uniqueness and multiplicity regions. The possible effects of such transitions are discussed.

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.

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.Contact: pratap@imtech.res.in

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.

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.

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.

The growth and metabolic capabilities of microorganisms depend on their interactions with the culture medium. Many media contain two or more key substrates, and an organism may have different preferences for the components. Microorganisms adjust their preferences according to the prevailing conditions so as to favor their own survival. Cybernetic modeling describes this evolutionary strategy by defining a goal that an organism tries to attain optimally at all times. The goal is often, but not always, maximization of growth, and it may require the cells to manipulate their metabolic processes in response to changing environmental conditions. The cybernetic approach overcomes some of the limitations of metabolic control analysis (MCA), but it does not substitute MCA. Here we review the development of the cybernetic modeling of microbial metabolism, how it may be combined with MCA, and what improvements are needed to make it a viable technique for industrial fermentation processes.