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.     

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.     

Designing hybrid life cycle assessment models based on uncertainty and complexity

Purpose

Despite the wide use of LCA for environmental profiling, the approach for determining the system boundary within LCA models continues to be subjective and lacking in mathematical rigor. As a result, life cycle models are often developed in an ad hoc manner, and are difficult to compare. Significant environmental impacts may be inadvertently left out. Overcoming this shortcoming can help elicit greater confidence in life cycle models and their use for decision making.

Methods

This paper describes a framework for hybrid life cycle model generation by selecting activities based on their importance, parametric uncertainty, and contribution to network complexity. The importance of activities is determined by structural path analysis—which then guides the construction of life cycle models based on uncertainty and complexity indicators. Information about uncertainty is from the available life cycle inventory; complexity is quantified by cost or granularity. The life cycle model is developed in a hierarchical manner by adding the most important activities until error requirements are satisfied or network complexity exceeds user-specified constraints.

Results and Discussion

The framework is applied to an illustrative example for building a hybrid LCA model. Since this is a constructed example, the results can be compared with the actual impact, to validate the approach. This application demonstrates how the algorithm sequentially develops a life cycle model of acceptable uncertainty and network complexity. Challenges in applying this framework to practical problems are discussed.

Conclusion

The presented algorithm designs system boundaries between scales of hybrid LCA models, includes or omits activities from the system based on path analysis of environmental impact contribution at upstream network nodes, and provides model quality indicators that permit comparison between different LCA models.

     

Neural and Hybrid Optimizations of the Fed-Batch Synthesis of Poly-β-Hydroxybutyrate by Ralstonia eutropha in a Nonideal Bioreactor

Poly-β-hydroxybutyrate (PHB) is synthesized by some microorganisms under stressful conditions. Despite its properties being comparable to those of synthetic polymers, and its biocompatibility and biodegradability, low productivities have dampened commercial interest in microbial PHB production. To increase production efficiency, a fed-batch fermentation with Ralstonia eutropha was optimized recently through a neural-cum-dispersion model (D-model) incorporating incomplete dispersion and noise in the feed streams. The approach described in the work has been improved in two ways: first by a model comprising neural networks only (N-model) and then by a hybrid neural model (H-model) with a mathematical component. At optimum dispersion, PHB production through the N-model optimization was 35% more than by the D-model, and this was enhanced by a further 58% using hybrid optimization. Recognizing that the D-model itself more than doubled the PHB production compared to a noise-free fully dispersed bioreactor, the present results establish hybrid neural optimization as a viable method for PHB production improvement under realistic conditions.     

Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment

Purpose

Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA.

Methods

Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts.

Results and discussion

According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N₂O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner.

Conclusions

Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.

     

Microbial Metabolism as an Evolutionary Response: The Cybernetic Approach to Modeling

ABSTRACT:?

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.

IMTECH communication no.001/2001     

Synthesizing cellular intelligence and artificial intelligence for bioprocesses

Microbial processes operated under realistic conditions are difficult to describe by mechanistic models, thereby limiting their optimization and control. Responses of living cells to their environment suggest that they possess some "innate intelligence". Such responses have been modeled by a cybernetic approach. Furthermore, the overall behavior of a bioreactor containing a population of cells may be described and controlled through artificial intelligence methods. Therefore, it seems logical to combine cybernetic models with artificial intelligence to evolve an integrated intelligence-based strategy that is physiologically more faithful than the current approaches. This possibility is discussed, together with practical considerations favoring a hybrid approach that includes some mathematical modeling.     

Signaling pathways of prohibitin and its role in diseases

Abstract

Prohibitin (PHB), appearing to be a negative regulator of cell proliferation and to be a tumor suppressor, has been connected to diverse cellular functions including cell cycle control, senescence, apoptosis and the regulation of mitochondrial activities. It is a growth regulatory gene that has pleiotropic functions in the nucleus, mitochondria and cytoplasmic compartments. However, in different tissues/cells, the expression of PHB was different, such as that it was increased in most of the cancers, but its expression was reduced in kidney diseases. Signaling pathways might be very important in the pathogenesis of diseases. This review was performed to provide a relatively complete signaling pathways flowchart for PHB to the investigators who were interested in the roles of PHB in the pathogenesis of diseases. Here, we review the signal transduction pathways of PHB and its role in the pathogenesis of diseases.     

Interaction between O-GlcNAc Modification and Tyrosine Phosphorylation of Prohibitin: Implication for a Novel Binary Switch

Prohibitin (PHB or PHB1) is an evolutionarily conserved, multifunctional protein which is present in various cellular compartments including the plasma membrane. However, mechanisms involved in various functions of PHB are not fully explored yet. Here we report for the first time that PHB interacts with O-linked β-N-acetylglucosamine transferase (O-GlcNAc transferase, OGT) and is O-GlcNAc modified; and also undergoes tyrosine phosphorylation in response to insulin. Tyrosine 114 (Tyr114) and tyrosine 259 (Tyr259) in PHB are in the close proximity of potential O-GlcNAc sites serine 121 (Ser121) and threonine 258 (Thr258) respectively. Substitution of Tyr114 and Tyr259 residues in PHB with phenylalanine by site-directed mutagenesis results in reduced tyrosine phosphorylation as well as reduced O-GlcNAc modification of PHB. Surprisingly, this also resulted in enhanced tyrosine phosphorylation and activity of OGT. This is attributed to the presence of similar tyrosine motifs in PHB and OGT. Substitution of Ser121 and Thr258 with alanine and isoleucine respectively resulted in attenuation of O-GlcNAc modification and increased tyrosine phosphorylation of PHB suggesting an association between these two dynamic modifications. Sequence analysis of O-GlcNAc modified proteins having known O-GlcNAc modification site(s) or known tyrosine phosphorylation site(s) revealed a strong potential association between these two posttranslational modifications in various proteins. We speculate that O-GlcNAc modification and tyrosine phosphorylation of PHB play an important role in tyrosine kinase signaling pathways including insulin, growth factors and immune receptors signaling. In addition, we propose that O-GlcNAc modification and tyrosine phosphorylation is a novel previously unidentified binary switch which may provide new mechanistic insights into cell signaling pathways and is open for direct experimental examination.     

Extending, changing, and explaining the brain

This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and systems neuroscience which also elicit neural plasticity. Furthermore, I propose that Datteri and Craver’s arguments concerning the limitations of prosthetic modelling in basic neuroscience, as opposed to neuroengineering, rests on too narrow a view of the ways models in neuroscience should be evaluated, and that a more pluralist approach is needed. I distinguish organisational validity of models from mechanistic validity. I argue that while prosthetic models may be deficient in the latter of these explanatory virtues because of neuroplasticity, they excel in the former since organisational validity tracks the extent to which a model captures coding principles that are invariant with plasticity. Changing the brain, I conclude, is one viable route towards explaining the brain.     

Oxidative and non-oxidative DNA damage and cardiovascular disease

Abstract

Evidence for the association of DNA damage with cardiovascular disease has been obtained from in vitro cell culture models, experimental cardiovascular disease and analysis of samples obtained from humans with disease. There is general acceptance that several factors associated with the risk of developing cardiovascular disease cause oxidative damage to DNA in cell culture models with both nuclear and mitochondrial DNA as targets. Moreover, evidence obtained over the past 10 years points to a possible mechanistic role for DNA damage in experimental atherosclerosis culminating in recent studies challenging the assumption that DNA damage is merely a biomarker of the disease process. This kind of mechanistic insight provides a renewed impetus for further studies in this area.     

Application of spectrofluorometry to the prediction of PHB concentrations in a fed-batch process

On-line estimation of biopolymer production during fermentation would be a useful adjunct to the development of strategies for process control and optimization. This study examined the applicability of spectrofluorometry, along with other on-line measurements, for the prediction of poly-ß-hydroxybutyric acid (PHB) concentrations in a high-cell density fed-batch fermentation of Ralstonia eutropha. Models previously used for modelling PHB evolution with time are not sufficiently accurate for situations where transient intermediate accumulations or PHB degradation occur. Thus, the mass balance in the model was modified to account for these situations. An estimation algorithm was developed that is based on a hybrid model consisting of a dynamic mass balance of PHB where the main reaction coefficient was regressed with respect to spectrofluorometric data. The regression between the kinetic parameter and the spectrofluorometric data was accomplished using partial least squares (PLS) regression to avoid high sensitivity to noise expected from highly correlated data, such as the spectrofluorometric readings. The model accounts for dynamics of intermediates and in this way allows the prediction of dynamic behaviour in PHB concentrations that cannot be predicted with other reported mathematical models.     

Molecular Simulation of Solution Conformations of the Amyloid β-Peptide Aβ(1–42) by a Backpropagation Neural Network Model

Abstract

The predictive power of solution-dependent conformational states of the Aβ(1–42) peptide of Alzheimer's disease by an optimized backpropagation neural network was tested. It was found that the neural network simulates well the solution-dependent conformations. The model was also examined by using geometry-optimized conformations (hybrid approach of Gasteiger charges plus MM+ molecular-mechanics) where the initial coordinates were obtained by NMR solution spectroscopy.     

Optimum DNA Curvature Using a Hybrid Approach Involving an Artificial Neural Network and Genetic Algorithm

Abstract

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 R_L value. Using the ANN-GA methodology, a number of sequences possessing high R_L 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 R_L values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

Toxic gas dispersion prediction for point source emission using deep learning method

Abstract

Accurate and rapid toxic gas concentration prediction model plays an important role in emergency aid of sudden gas leak. However, it is difficult for existing dispersion model to achieve accuracy and efficiency requirements at the same time. Although some researchers have considered developing new forecasting models with traditional machine learning, such as back propagation (BP) neural network, support vector machine (SVM), the prediction results obtained from such models need to be improved still in terms of accuracy. Then new prediction models based on deep learning are proposed in this paper. Deep learning has obvious advantages over traditional machine learning in prediction and classification. Deep belief networks (DBNs) as well as convolution neural networks (CNNs) are used to build new dispersion models here. Both models are compared with Gaussian plume model, computation fluid dynamics (CFD) model and models based on traditional machine learning in terms of accuracy, prediction time, and computation time. The experimental results turn out that CNNs model performs better considering all evaluation indexes.     

Characterization and functional analysis of the proteins Prohibitin 1 and 2 in Trypanosoma cruzi

BackgroundChagas disease is the third most important neglected tropical disease. There is no vaccine available, and only two drugs are generally prescribed for the treatment, both of which with a wide range of side effects. Our study of T. cruzi PHBs revealed a pleiotropic function in different stages of the parasite, participating actively in the transformation of the non-infective replicative epimastigote form into metacyclic trypomastigotes and also in the multiplication of intracellular amastigotes.Methodology/principal findingsTo obtain and confirm our results, we applied several tools and techniques such as electron microscopy, immuno-electron microscopy, bioinformatics analysis and molecular biology. We transfected T. cruzi clones with the PHB genes, in order to overexpress the proteins and performed a CRISPR/Cas9 disruption to obtain partially silenced PHB1 parasites or completely silenced PHB2 parasites. The function of these proteins was also studied in the biology of the parasite, specifically in the transformation rate from non-infective forms to the metacyclic infective forms, and in their capacity of intracellular multiplication.Conclusion/significanceThis research expands our understanding of the functions of PHBs in the life cycle of the parasite. It also highlights the protective role of prohibitins against ROS and reveals that the absence of PHB2 has a lethal effect on the parasite, a fact that could support the consideration of this protein as a possible target for therapeutic action.