Low degree of polymerization xylooligosaccharides production from almond shell using immobilized nano-biocatalyst.

In this work, the effect of particle size on alkali pretreatment of the almond shell was evaluated for recovery of hemicellulose. Further, endoxylanase from Thermomyces lanuginosus was immobilized on Fe-based magnetic nanoparticles to enable reuse of enzyme. Reduction in particle size significantly influences the recovery of hemicellulose as particle size below 120 µm enable recovery of 97% available hemicellulose in 1 h at 121 °C with 2 M alkali. The enzyme could retain 93.3% of enzymatic activity upon immobilization onto magnetic support using glutaraldehyde (25 mM) and was at par with the free enzyme in terms of pH and temperature profile. The measurement of reaction kinetics (Km and Vmax) indicates similar values for free and immobilized enzyme. The structural and morphological analysis indicates presence near spherical magnetic core and successful cross-linking of the enzyme without alteration of the magnetic core. The immobilized enzyme was able to hydrolyze hemicellulose to produce XOS, the yield equivalent to 67.4% of that obtained using free enzyme at 50 °C. The comparison of XOS production ability at 50 and 60 °C, suggests that the immobilized enzyme retains activity as similar yield was obtained at both temperatures, whereas, the yield for free enzyme decreases significantly. The XOS yield on recycling of immobilized enzyme for three successive cycles was found to reduce to 41% of the initial cycle. However, in all cycles of enzymatic hydrolysis, the percentage of xylobiose was found to be above 90%.

Production and faecal fermentation of pentose oligomers of hemicellulose: Study of variables influencing bioprocess efficiency.

Almond shell, a by-product obtained from the nut industry, was valorised into low degree of polymerisation xylooligosaccharides using alkaline pretreatment and enzymatic hydrolysis. The effect of particle size on hemicellulose recovery upon pretreatment was studied using 1 and 2 M NaOH. It was observed that particle size significantly influences hemicellulose recovery, as particles below 120 µm resulted in near complete recovery at 2 M NaOH. Enzymatic hydrolysis of hemicellulose was optimised using response surface methodology, to obtain efficient xylooligosaccharides production at low enzyme dose and high substrate concentration. For higher XOS yield, an enzyme dose of 10 U and substrate concentration <2% was optimal. The in-vitro human faecal fermentation study revealed no significant difference in gas and short chain fatty acid level among substrates evaluated. It was observed that short chain oligosaccharides produce higher level of acetate than medium chain oligosaccharides.

Revitalizing personalized medicine: respecting biomolecular complexities beyond gene expression.

Despite recent advancements in "omic" technologies, personalized medicine has not realized its fullest potential due to isolated and incomplete application of gene expression tools. In many instances, pharmacogenomics is being interchangeably used for personalized medicine, when actually it is one of the many facets of personalized medicine. Herein, we highlight key issues that are hampering the advancement of personalized medicine and highlight emerging predictive tools that can serve as a decision support mechanism for physicians to personalize treatments.

Identification of age-structured models: cell cycle phase transitions.

A methodology is developed that determines age-specific transition rates between cell cycle phases during balanced growth by utilizing age-structured population balance equations. Age-distributed models are the simplest way to account for varied behavior of individual cells. However, this simplicity is offset by difficulties in making observations of age distributions, so age-distributed models are difficult to fit to experimental data. Herein, the proposed methodology is implemented to identify an age-structured model for human leukemia cells (Jurkat) based only on measurements of the total number density after the addition of bromodeoxyuridine partitions the total cell population into two subpopulations. Each of the subpopulations will temporarily undergo a period of unbalanced growth, which provides sufficient information to extract age-dependent transition rates, while the total cell population remains in balanced growth. The stipulation of initial balanced growth permits the derivation of age densities based on only age-dependent transition rates. In fitting the experimental data, a flexible transition rate representation, utilizing a series of cubic spline nodes, finds a bimodal G(0)/G(1) transition age probability distribution best fits the experimental data. This resolution may be unnecessary as convex combinations of more restricted transition rates derived from normalized Gaussian, lognormal, or skewed lognormal transition-age probability distributions corroborate the spline predictions, but require fewer parameters. The fit of data with a single log normal distribution is somewhat inferior suggesting the bimodal result as more likely. Regardless of the choice of basis functions, this methodology can identify age distributions, age-specific transition rates, and transition-age distributions during balanced growth conditions.

Solving the inverse problem for aggregation in activated sludge flocculation using a population balance framework.

Many systems contain populations of individuals. Often, they are regarded as a lumped phase, which might, for some applications, lead to inadequate model predictive power. An alternative framework, Population Balance Models, has been used here to describe such a system, activated sludge flocculation in which particle size is the property one wants to model. An important problem to solve in population balance modelling is to determine the model structure that adequately describes experimentally obtained data on for instance, the time evolution of the floc size distribution. In this contribution, an alternative method based on solving the inverse problem is used to recover the model structure from the data. In this respect, the presence of similarity in the data simplifies the problem significantly. Similarity was found and the inverse problem could be solved. A forward simulation then confirmed the quality of the model structure to describe the experimental data.

Analysis of resonance chemotherapy in leukemia treatment via multi-staged population balance models.

An age-structured population balance model that explicitly models cell cycle phases is developed to investigate the effects of cell cycle specific (CCS) drugs. In particular, the benefits of timing CCS drug treatments in resonance chemotherapy are predicted and measured directly in vitro before evaluating likely in vivo scenarios. The phase transition rates are measured in vitro for the HL60 leukemia cell line and are used to predict the transient phase dynamics after exposure to the S phase specific drug, camptothecin. The phase oscillations predicted by the model are observed experimentally and the timing of a second camptothecin pulse is shown to significantly alter the overall treatment effectiveness. To explore the feasibility of designing resonance chemotherapeutic treatments to preferentially eliminate one cell type over another, Jurkat and HL60 leukemia cells are exposed to the same dual-pulse camptothecin treatment regimen. With the model framework validated for simplified cases, the model is used to extrapolate the effectiveness of resonance chemotherapy considering in vivo effects such as quiescence, drug metabolism, drug properties, and transport considerations that were not included in the in vitro experiments. While resonance chemotherapy is intuitive and looks promising in vitro, when in vivo considerations are included in the model, the phenomenon is dampened and the window of applicability becomes narrower.

Cybernetic modeling and regulation of metabolic pathways in multiple steady states of hybridoma cells.

Hybridoma cells utilize a pair of complementary and partially substitutable substrates, glucose and glutamine, for growth. It has been shown that cellular metabolism shifts under different culture conditions. When those cultures at different metabolic states are switched to a continuous mode, they reach different steady states under the same operating conditions. A cybernetic model was constructed to describe the complementary and partial substitutable nature of substrate utilization. The model successfully predicted the metabolic shift and multiple steady-state behavior. The results are consistent with the experimental observation that the history of the culture affects the resulting steady state.

Metabolic engineering from a cybernetic perspective. 1. Theoretical preliminaries

The theoretical basis of a cybernetic metabolic network design and analysis framework, which has been subsequently successfully applied to predict system response to genetic alteration, is presented. This conceptual methodology consists of three main branches, namely, a model realization framework, a representation of genetic alteration, and lastly, a metabolic design component. These concepts are introduced as a series of postulates that describe the basic tenets of the approach. Each branch is discussed in turn, starting with the cybernetic representation of arbitrarily complex metabolic networks. A set of postulates is put forth that affords the modular construction of cybernetic models of metabolic networks using as a base a library of elementary pathways. This is followed by a discussion of the representation of genetic alterations within the cybernetic framework. It is postulated that the objective of the base network and the altered system are identical (at least on the time scale required for the organism to "learn" new objectives). This implies, with respect to resource allocation, that the base network and its genetically altered counterpart may still be treated as optimal systems; however, the set of competing physiological choices open to the altered network expands or contracts depending upon the nature of the genetic perturbation. Lastly, to add a predictive design aspect to the methodology, we present a set of postulates that outline the application of metabolic control analysis to cybernetic model systems. We postulate that sensitivity coefficients computed from a cybernetic model, although still local in scope, have the added benefit of a systematic representation of regulatory function as described by the cybernetic variables. Thus, information gained from sensitivity measurements stemming from a cybernetic model include the explicit input of metabolic regulation, a component that is lacking in a purely kinetic representation of metabolic function. The sensitivity results can then be employed to develop qualitative strategies for the rational alteration of metabolic function, which can be evaluated by simulation of an appropriately modified cybernetic model of the base network.

Metabolic engineering from a cybernetic perspective. 2. Qualitative investigation of nodal architechtures and their response to genetic perturbation

A cybernetic representation of the branch point development of Stephanopoulos and Vallino is formulated. The model systems are employed to translate the qualitative properties of the nodal control architectures characterized by Stephanopoulos and Vallino into a mathematical context. It is shown that a cybernetic model in which the objective is the independent maximization of the levels of branch point products is consistent with the characterization of a flexible node. In contrast, the rigid control architecture is shown to be equivalent to the maximization of the mathematical product of the branch point products. It has been demonstrated subsequently that cybernetic metabolic network models are capable of predicting the system response to enzymatic amplification. However, given the complicated nature of the subsequent models, a clear illustration of the basic mechanism by which such predictions are manifested is not forthwith. Thus, a second objective of the present work is the examination of the response of the flexible and rigid control architectures to genetic perturbation, specifically enzymatic overexpression, with the expressed aim of elucidating the mechanism by which a cybernetic model predicts metabolic network responsiveness. It is shown that the ramifications of genetic perturbation are transmitted through the cybernetic representation of a metabolic network via the resource allocation structure which acts as the conduit by which regulatory signals are transmitted to seemingly unconnected portions of the network. It is postulated that enzymatic overexpression under an artificial promoter represents, from the perspective of the microorganism, an uncontrollable resource drain that forces the metabolic network control architecture to reevaluate the standing resource allocation policy as implemented via the cybernetic control variables. In biological terms, the reevaluation of allocation policy implies a shift in the level and activity of network enzymes yielding, in some cases, qualitatively different network function. It is our position that, conceptually, this is equivalent to the conventional wisdom that genetic manipulation of a metabolic network is the impetus for shifts in the network functionality, i.e., enzyme levels as well as activity. Thus, this development provides a necessary intellectual precursor for the formulation and analysis of the model systems that follow.

Mathematical models of metabolic pathways.

There have been recent advances in metabolic flux analysis. In particular, the marriage of traditional flux balancing with NMR isotopomer distribution analysis holds great promise for the detailed quantification of physiology. Nevertheless, flux analysis yields only static snap-shots of metabolism. To robustly predict the time evolution of metabolic networks, dynamic mathematical models, especially those that contain a description of both gene expression as well as enzyme activity, must be utilized. When mechanistic control and regulatory information is not available, heuristic-based methods, such as the cybernetic framework, can be employed to describe the action of these control mechanisms. In the 'high-information' future, as more biological information becomes available, such heuristic-based approaches can be replaced by mechanistic mass-action representations of physiology that stem directly from genetic sequence.

Metabolic engineering from a cybernetic perspective: aspartate family of amino acids.

Using the modular cybernetic framework developed by Varner and Ramkrishna (Varner and Ramkrishna; 1998a, b) a cybernetic model is formulated that describes the time evolution of the aspartate family of amino acids in Corynebacterium lactofermentum ATCC 21799. The network model formulation is employed in the role of a diagnostic tool for the overproduction of threonine. More precisely, having determined a parameter set that describes the time evolution of a base strain (lysine producer), the model predicted response to genetic perturbations, designed to enhance the level of threonine, are simulated using an appropriately modified cybernetic model and compared with the experimental results of Stephanopoulos and Sinskey (Colón et al., 1995a, Appl. Environ. Microbiol. 61, 74-78) for identical genetic perturbations. It is found that the model predicted response to enzymatic over-expression in the aspartate pathway agrees, for the most part, with experimental observations within the experimental error bounds. This result lends credence to the hypothesis that cybernetic models can be employed to predict the local response of a metabolic network to genetic perturbation, thereby, affording cognizance of the potential pitfalls of a particular genetic alteration strategy a priori.

Application of cybernetic models to metabolic engineering: investigation of storage pathways

A cybernetic model is proposed to examine generic features of storage pathways. This model is capable of describing synthesis of carbon and non-carbon storage polymers. The effect of environmental conditions is evaluated using storage polymer level as a fraction of total biomass as a gauge of pathway performance. The base wild-type pathway is then analyzed to determine the effect of genetic alterations upon system performance. Proposed modifications are tested using the cybernetic model as a diagnostic tool to ascertain the ramifications of potential genetic alterations. A methodology is developed within the cybernetic framework to describe alterations of enzyme activity and over-expression of pathway enzymes. Copyright 1998 John Wiley & Sons, Inc.

Microbial growth on substitutable substrates: characterizing the consumer-resource relationship.

In this article, we consider the growth of microorganisms on mixtures of carbon sources and characterize the consumer-resource relationship for this system. The characteristic features observed for the growth of a single microorganism on a pair of carbon sources allow a representation of this relationship based on a general paradigm for resource classification. This representation is verified using a comprehensive model for microbial growth on carbon sources. The results show that for the same pair of carbon sources the qualitative nature of the consumer-resource relationship changes with changing specific growth rate, and therefore a change in the identity of the rate-limiting substrate(s).

New patterns of mixed-substrate utilization during batch growth of Escherichia coli K12.

Microbial growth on mixtures of substrates is of considerable engineering and biological interest. Most of the work until now has dealt with microbial growth on binary mixtures of sugars or polyols. In these cases, it is often found that no matter how the inoculum is precultured, only one of the two substrates is consumed in the first growth phase, leading to the diauxic growth pattern. The goal of the experiments reported here is to investigate growth on mixtures containing at least one organic acid. These experiments show that the substrate utilization patterns in such mixtures are qualitatively different from the diauxic growth pattern. For instance, during growth of Escherichia coli K12 on certain binary mixtures of organic acids, the two substrates are utilized simultaneously, and the mixed-substrate maximum specific growth rate exceeds the single-substrate maximum specific growth rate on either one of the two constituent substrates. Furthermore, the very same mixed-substrate maximum specific growth and substrate uptake rates are observed no matter how the inoculum is precultured. On the other hand, in a mixture of glucose and pyruvate, the maximum specific growth rate seems to depend on the preculturing conditions, thus suggesting the existence of multiple physiological quasi-steady states. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 747-757, 1997.

Cybernetic modeling of growth in mixed, substitutable substrate environments: Preferential and simultaneous utilization.

Growth of microorganisms on substitutable substrate mixtures display diverse growth dynamics characterized by simultaneous or preferential uptake of carbon sources. This article shows that cybernetic modeling concepts which were successful in predicting diauxic growth patterns can be extended to describe simultaneous consumption of substrates. Thus the growth of Escherichia coli on mixtures of glucose and organic acids such as pyruvate, fumarate, and succinate has been described successfully by the cybernetic model presented here showing both diauxic and simultaneous uptake when observed. The model also describes the changes in utilization patterns that occur under changing dilution rates, substrate concentrations, and models of preculturing. The model recognizes the importance of the synthesis of biosynthetic precursors in cell growth through a kinetic structure that is quite general for any mixture of carbon-energy sources. (c) 1996 John Wiley & Sons, Inc.

Toward a self-similar theory of microbial populations.

A fresh quest is made of segregated cell models of microbial populations with a view to determine whether the multivarite distribution of physiological states, during transient growth, can attain self-similar forms (i.e., become time invariant) when each physiological state variable is scaled with respect to its population average. Such self-similar growth situations are believed to be more general than those of balanced growth. The conditions under which self-similarity is possible are investigated. Thus conditions are stipulated on the synthesis rates of different physiological entities, cell division rate, and the partitioning of the parent cell's components among the daughter cells (assuming binary division) in order for self-similar growth to be attained. Subject to the attainment of self-similar growth, it is shown that cytometric data can be analyzed systematically to determine how the rates of syntheses of various biochemical entities and cell division rates vary with the physiological entities that are measured. Inverse problems, represented by algebraic systems, are identified which will potentially allow flow cytometric data to be inverted to yield quantitative information on the absolute rates of cellular growth and reproductory processes as a function of the cell states chosen for measurement. It is suggested that the methods become more effective when cytometry can be used to make direct observations on dividing cells so that the number of unknowns in the inverse problem can be reduced, thus facilitating its more complete solution. Preliminary analysis of cytometric data obtained in the literature show promise of self-similarity and thus the possibility of application of the methods discussed here.

Metabolic regulation in bacterial continuous cultures: I.

Dilution rate steps in continuous culture experiments with Klebsiella pneumoniae growing on single substrate feeds have brought out interesting features of metabolic regulation not observed in batch cultures. In a step-up experiment, the adjustment of the culture to a new steady state is preceded by an undershoot in cell density. Results of a step-down experiment indicate a corresponding overshoot phenomenon. These observations of the transient behavior of the culture growing on glucose and xylose as well as the steady-state results are interpreted with cybernetic models. The development of the model explicitly accounts for the lumped internal resource, which is optimally allocated toward the synthesis of key enzymes catalyzing different cellular processes. The model also includes a description of the increased maintenance demand observed at low growth rates. It reduces to previous cybernetic models in situations where the cell does not experience a sudden change in its environment and, hence, retains their predictive capability.

Metabolic regulation in bacterial continuous cultures: II.

The transient behavior of a continuous culture of Klebsiella pneumoniae with mixed feed of glucose and xylose arising from step-up and step-down in dilution rates and from a feed-switching experiment is presented. he organism gradually switches from simultaneous utilization of the substrates at low growth rates to preferred utilization of the faster substrate (i.e, supporting a higher growth rate) at high dilution rates. The metabolic lags following a step increase in dilution rate and a significant accumulation of the slower substrate during the transient period result from the effects of metabolic regulation. The cybernetic modeling approach that successfully described the foregoing situations with single-substrate feeds is employed to describe mixed substrate behavior. The parameters in the mixed-substrate (glucose and xylose) model are the same as those in the single-substrate models with the singular exception of the rate constant for the xylose growth enzyme synthesis. The reason for this discrepancy is discussed in detail. It appears that the constitutive rate of enzyme synthesis for growth on a given substrate may be related to the past history of the organism in regard to whether or not the organism has been exposed to the particular substrate. Thus, the results further demonstrate the ability of the framework to effectively describe metabolic regulation in batch, fedbatch, and continuous microbial cultures.