Industrial systems biology

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Kinetic and Stoichiometric Modeling-Based Analysis of Docosahexaenoic Acid (DHA) Production Potential by C. cohnii from Glycerol, Glucose and Ethanol

Docosahexaenoic acid (DHA) is one of the most important long-chain polyunsaturated fatty acids (LC-PUFAs), with numerous health benefits. Crypthecodinium cohnii, a marine heterotrophic dinoflagellate, is successfully used for the industrial production of DHA because it can accumulate DHA at high concentrations within the cells. Glycerol is an interesting renewable substrate for DHA production since it is a by-product of biodiesel production and other industries, and is globally generated in large quantities. The DHA production potential from glycerol, ethanol and glucose is compared by combining fermentation experiments with the pathway-scale kinetic modeling and constraint-based stoichiometric modeling of C. cohnii metabolism. Glycerol has the slowest biomass growth rate among the tested substrates. This is partially compensated by the highest PUFAs fraction, where DHA is dominant. Mathematical modeling reveals that glycerol has the best experimentally observed carbon transformation rate into biomass, reaching the closest values to the theoretical upper limit. In addition to our observations, the published experimental evidence indicates that crude glycerol is readily consumed by C. cohnii, making glycerol an attractive substrate for DHA production.

Molecular Tools for Leveraging the Potential of the Acid-Tolerant Yeast Zygosaccharomyces bailii as Cell Factory

Microorganisms offer a tremendous potential as cell factories, and they are indeed been used by humans since the previous centuries for biotransformations. Among them, yeasts combine the advantage of a unicellular state with a eukaryotic organization. Moreover, in the era of biorefineries, their biodiversity can offer solutions to specific process constraints. Zygosaccharomyces bailii, an ascomycete budding yeast, is widely known for its peculiar tolerance to different stresses, among which are organic acids. Moreover, the recent reclassification of the species, including diverse hybrids, is further expanding both fundamental and applied interests. It is therefore reasonable that despite the possibility to apply with this yeast some of the molecular tools and protocols routinely used to manipulate Saccharomyces cerevisiae, adjustments and optimizations are necessary. Here we describe in detail the methods for determining chromosome number, size, and aneuploidy, transformation, classical target gene disruption or gene integration, and designing of episomal expression plasmids helpful for engineering the yeast Z. bailii .

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology

Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology’s environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in the development process of novel solutions may lead to sub-optimal biotechnological production, causing adverse environmental and societal problems proportional to the production volumes.

In the paper, sustainable metabolic engineering (SME) concept is proposed to assess and optimize the sustainability of biotechnological production that can be derived from the features of metabolism of the exploited organism. The SME concept is optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered. The extension of characterising features of strains designed by metabolic engineering methods with sustainability estimation enables ab initio improvement of the biotechnological production design.

Biodegradable polyhydroxyalkanoates production from wheat straw by recombinant Halomonas elongata A1

Waste straw bio-transformation of high value-added macromolecule polyhydroxyalkanoates (PHAs) was significance to environmental sustainable development. As a member of the PHA family, poly-β-hydroxybutyrate (PHB) could be synthesized by Halomonas elongata A1 with maximal yields of 22.8% and 11.8% of bacterial weights using glucose and carboxymethyl cellulose as carbon sources, respectively. To improve PHB production, we generated three recombinant strains, the H. elongata P2 with highest PHB biosynthesis ability. When wheat straw, mixed substrate and oleic acid were individually used as single carbon source, the maximal PHA polymer accumulation in the H. elongata P2 reached 5.2%, 16.5% and 27.5%, respectively, after 84 h of cultivation. This hardness, toughness and crystallization properties of the PHA macromolecule altered dependent on starting substrates, when analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). In terms of the hardness and roughness, the PHA produced from mixed substrates was much softer than that from wheat straw but harder than that from oleic acid. The long-chain carbon improved the softness and strength of the produced PHA. Our data indicate that economical substrates, such as straw and waste oil, can be used in the synthesis of multi-functional plastic products with biodegradable properties.

Model-guided development of an evolutionarily stable yeast chassis

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.

Need for speed: evaluation of dilute and shoot-mass spectrometry for accelerated metabolic phenotyping in bioprocess development

With the utilization of small-scale and highly parallelized cultivation platforms embedded in laboratory robotics, microbial phenotyping and bioprocess development have been substantially accelerated, thus generating a bottleneck in bioanalytical bioprocess sample analytics. While microscale cultivation platforms allow the monitoring of typical process parameters, only limited information about product and by-product formation is provided without comprehensive analytics. The use of liquid chromatography mass spectrometry can provide such a comprehensive and quantitative insight, but is often limited by analysis runtime and throughput. In this study, we developed and evaluated six methods for amino acid quantification based on two strong cation exchanger columns and a dilute and shoot approach in hyphenation with either a triple-quadrupole or a quadrupole time-of-flight mass spectrometer. Isotope dilution mass spectrometry with 13C15N labeled amino acids was used to correct for matrix effects. The versatility of the methods for metabolite profiling studies of microbial cultivation supernatants is confirmed by a detailed method validation study. The methods using chromatography columns showed a linear range of approx. 4 orders of magnitude, sufficient response factors, and low quantification limits (7-443 nM) for single analytes. Overall, relative standard deviation was comparable for all analytes, with < 8% and < 11% for unbuffered and buffered media, respectively. The dilute and shoot methods with an analysis time of 1 min provided similar performance but showed a factor of up to 35 times higher throughput. The performance and applicability of the dilute and shoot method are demonstrated using a library of Corynebacterium glutamicum strains producing L-histidine, obtained from random mutagenesis, which were cultivated in a microscale cultivation platform.

Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion

BACKGROUND

RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification.

RESULTS

EMBR-seq results in 90% of the sequenced RNA molecules from an E. coli culture deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq uses a single or a few oligonucleotides per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits for bacterial rRNA depletion, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA.

CONCLUSIONS

EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.

Resveratrol: A Fair Race Towards Replacing Sulfites in Wines

In recent years, significant efforts to produce healthier wines has led to the replacement or reduction of the addition of sulfites, using alternative substances or techniques. Resveratrol and related biophenols seem to be of great interest, since beyond their protective nature and contrary to sulfites they can positively affect consumer health. These bioactive phytochemicals are naturally produced in grapes as evolutionary acquired mechanisms against pathogens and UV irradiation. However, despite the efforts made so far attempting to develop economic and industrially adopted isolation techniques, available quantities of these biophenols for commercial use are still quite limited. Therefore, such molecules are still not able to meet the needs of industrial use due to their prohibitive marketable cost. In this review we summarize the efforts that have been made to biosynthesize these molecules through alternative, innovative ways. Increasing interest in modern biotechnological approaches has shed light on the exploitation of metabolically engineered microbial factories, instead of plants, to produce molecules of industrial interest. Such approaches, also reviewed here, are expected to lower the cost and appear promising to produce enough surplus to attract further oenological experimentation upon yielding functional wines. This development is expected to attract further industrial attention, continuing the race to partially or totally replace the external addition of sulfites. We also review important physicochemical properties of resveratrol in relation to enriching wines.

Banana Peels: A Promising Substrate for the Coproduction of Pectinase and Xylanase from Aspergillus fumigatus MS16

Banana peels (BP), an under-utilized waste material, was studied for the production of xylanase and pectinase by Aspergillus fumigates MS16. The factors affecting the co-production of both the enzymes were separately studied for their influence under submerged (Smf) and solid-state fermentation (SSF) of BP. The strain was cultivated in the presence of mineral salt (MS) solution containing BP powder as a sole source of carbon and physical and nutritional factors varied to observe the change in the enzyme titers. The data revealed that the MS-based medium was appropriate for the production of both the enzymes; therefore, in subsequent experiments, the same medium was used. A temperature of 30–35°C was found better for the production of the two enzymes under Smf; however, the titers of pectinase dropped significantly at 40°C. Contrarily, xylanase production was inhibited at 40°C under SSF but not under Smf. Whereas, supplementation of xylan or pectin to BP induced the production of xylanase and pectinase, respectively. Lowering the pH value favored the production of both the enzymes under Smf; however, the production of pectinase improved significantly when a higher concentration of BP (1%) was used compared to the concentration (0.25%) required for the production of xylanase. Interestingly, the enzyme preparation obtained under SSF exhibited optimal activities of both the enzymes at higher temperatures when compared to those obtained under Smf. The data indicated that the physiology of the fungus differed greatly when the cultivation pattern varied from Smf to SSF and, hence, the enzymes produced were characteristically distinct.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis

Background: Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. Method: In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements’ noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. Results: It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

Phenomenological models as effective tools to discover cellular design principles

Microbes have proved useful to us in many different ways. To utilize microbes, we have mostly focused on maximizing growth, to improve yield of chemicals derived from the microbes. However, to truly tap into their potential, we should also aim to understand microbial physiology. We present a historical perspective of the developments in the field of Microbial Biotechnology, focusing on how the growth-modelling approaches have changed. Starting from simple empirical growth models, we have evolved towards mechanistic and phenomenological models which use molecular and physiological details to drastically improve prediction power of these models. Lastly, we explore the as of yet unsolved questions in microbial physiology, and discuss how the ability to monitor microbial growth at single cell resolution using the lab-on-a-chip technologies is uncovering previously unobservable causal principles underlying microbial growth.

SyNDI: synchronous network data integration framework

BACKGROUND

Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.

RESULTS

In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.

CONCLUSIONS

Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Reconstructing High-Quality Large-Scale Metabolic Models with merlin

Here, the basic principles of reconstructing genome-scale metabolic models with merlin are described. This tool covers the basic stages of this process, providing several tools that allow assembling models, using the sequenced genome as a starting point. merlin has two main modules, separating the process of annotating (enzymes, transporters, and compartments) on the genome from the process of model assembly, though information from the former is integrated in the latter after curation. Moreover, merlin provides several tools to curate the model, including tools for generating reactions' gene rules and placeholder entities for biomass precursors, such as proteins (e-protein) or nucleotides (e-DNA and e-RNA) among others.This tutorial covers each feature of merlin in detail, including the assessment of experimental data for the validation of the model.

Metabolomics of Pichia pastoris: impact of buffering conditions on the kinetics and nature of metabolite loss during quenching

Mass spectrometry-based metabolomic profiling is a powerful strategy to quantify the concentrations of numerous primary metabolites in parallel. To avoid distortion of metabolite concentrations, quenching is applied to stop the cellular metabolism instantly. For yeasts, cold methanol quenching is accepted to be the most suitable method to stop metabolism, while keeping the cells intact for separation from the supernatant. During this treatment, metabolite loss may occur while the cells are suspended in the quenching solution. An experiment for measuring the time-dependent loss of selected primary metabolites in differently buffered quenching solutions was conducted to study pH and salt concentration-dependent effects. Molecular properties of the observed metabolites were correlated with the kinetics of loss to gain insight into the mechanisms of metabolite leakage. Size and charge-related properties play a major role in controlling metabolite loss. We found evidence that interaction with the cell wall is the main determinant to retain a molecule inside the cell. Besides suggesting an improved quenching protocol to keep loss at a minimum, we could establish a more general understanding of the process of metabolite loss during quenching, which will allow to predict optimal conditions for hitherto not analysed metabolites.

Sortase A-Assisted Metabolic Enzyme Ligation in Escherichia coli for Enhancing Metabolic Flux

Metabolic engineering has been an important approach for microbial bio-production. To produce bio-chemicals with engineered microorganisms, metabolic pathways have been edited using several common strategies, including gene disruption, gene overexpression, and gene attenuation. Here, we demonstrated metabolic channeling based on enzymatic metabolic enzyme ligation as a noteworthy approach for enhancing a desired metabolic flux. To achieve metabolic channeling , the metabolic enzymes should be in close proximity in cells. In the literature, several methodologies have been recently applied to achieve metabolic channeling . Meanwhile, we have proposed a strategy for possessing metabolic enzymes in close proximity, by utilizing sortase A as a stapler to tether such enzymes in Escherichia coli. By tethering metabolic enzymes that catalyze the reactions before and after a target metabolite, the metabolic flux may be enhanced. This chapter describes the approach for enhancing acetate-producing flux by sortase-A-assisted metabolic ligation in E. coli.

Incorporating comparative genomics into the design-test-learn cycle of microbial strain engineering

Engineering microbes with new properties is an important goal in industrial engineering, to establish biological factories for production of biofuels, commodity chemicals and pharmaceutics. But engineering microbes to produce new compounds with high yield remains a major challenge toward economically viable production. Incorporating several modern approaches, including synthetic and systems biology, metabolic modeling and regulatory rewiring, has proven to significantly advance industrial strain engineering. This review highlights how comparative genomics can also facilitate strain engineering, by identifying novel genes and pathways, regulatory mechanisms and genetic background effects for engineering. We discuss how incorporating comparative genomics into the design-test-learn cycle of strain engineering can provide novel information that complements other engineering strategies.

Genetic-Metabolic Coupling for Targeted Metabolic Engineering

Production of chemicals in microbes often employs potent biosynthetic enzymes, which can interact with the microbial native metabolism to affect cell fitness and product yield. However, production optimization largely relies on data collected from wild-type strains in the absence of metabolic perturbations, thus limiting their relevance to specific conditions. Here, we address this issue by coupling cell fitness to the production of thiamine diphosphate in Escherichia coli using a synthetic RNA biosensor. We use this strategy to interrogate a library of transposon mutants and elucidate the native gene network influencing both cell fitness and thiamine production. Ultimately, we identify effectors of the OxyR-Fur stress response that limit thiamine biosynthesis via alternative regulation of iron storage and Fe-S cluster inclusion in enzymes. This study presents a new approach for the reliable high-throughput identification of genetic targets of both known and unknown function that are directly relevant to a specific biosynthetic process.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering

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Model-driven systems engineering has been more fascinating process for microbial biofuel production.

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Clostridium sticklandii is a potential strain for the solventogenesis and acidogenesis.

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The present review provides an insight for the protein catabolism-directed biofuel production.

Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n-butanol, n-butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways.

A design-build-test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

BACKGROUND

Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies.

RESULTS

We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose-xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain.

CONCLUSIONS

We present a design-build-test cycle composed of modeling efforts and experiments with a glucose-xylose co-utilizing recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated "modeling and experiments" systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.

Manipulation of the ATP pool as a tool for metabolic engineering

Cofactor engineering has been long identified as a valuable tool for metabolic engineering. Besides interventions targeting the pools of redox cofactors, many studies addressed the adenosine pools of microorganisms. In this mini-review, we discuss interventions that manipulate the availability of ATP with a special focus on ATP wasting strategies. We discuss the importance to fine-tune the ATP yield along a production pathway to balance process performance parameters like product yield and volumetric productivity.

Systems Biology of Microbial Exopolysaccharides Production

Exopolysaccharides (EPSs) produced by diverse group of microbial systems are rapidly emerging as new and industrially important biomaterials. Due to their unique and complex chemical structures and many interesting physicochemical and rheological properties with novel functionality, the microbial EPSs find wide range of commercial applications in various fields of the economy such as food, feed, packaging, chemical, textile, cosmetics and pharmaceutical industry, agriculture, and medicine. EPSs are mainly associated with high-value applications, and they have received considerable research attention over recent decades with their biocompatibility, biodegradability, and both environmental and human compatibility. However, only a few microbial EPSs have achieved to be used commercially due to their high production costs. The emerging need to overcome economic hurdles and the increasing significance of microbial EPSs in industrial and medical biotechnology call for the elucidation of the interrelations between metabolic pathways and EPS biosynthesis mechanism in order to control and hence enhance its microbial productivity. Moreover, a better understanding of biosynthesis mechanism is a significant issue for improvement of product quality and properties and also for the design of novel strains. Therefore, a systems-based approach constitutes an important step toward understanding the interplay between metabolism and EPS biosynthesis and further enhances its metabolic performance for industrial application. In this review, primarily the microbial EPSs, their biosynthesis mechanism, and important factors for their production will be discussed. After this brief introduction, recent literature on the application of omics technologies and systems biology tools for the improvement of production yields will be critically evaluated. Special focus will be given to EPSs with high market value such as xanthan, levan, pullulan, and dextran.

Flux-sum analysis identifies metabolite targets for strain improvement

Background

Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets.

Results

In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology.

Conclusions

The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0198-3) contains supplementary material, which is available to authorized users.

Fibrous proteins: At the crossroads of genetic engineering and biotechnological applications

Fibrous proteins, such as silk, elastin and collagen are finding broad impact in biomaterial systems for a range of biomedical and industrial applications. Some of the key advantages of biosynthetic fibrous proteins compared to synthetic polymers include the tailorability of sequence, protein size, degradation pattern, and mechanical properties. Recombinant DNA production and precise control over genetic sequence of these proteins allows expansion and fine tuning of material properties to meet the needs for specific applications. We review current approaches in the design, cloning, and expression of fibrous proteins, with a focus on strategies utilized to meet the challenges of repetitive fibrous protein production. We discuss recent advances in understanding the fundamental basis of structure-function relationships and the designs that foster fibrous protein self-assembly towards predictable architectures and properties for a range of applications. We highlight the potential of functionalization through genetic engineering to design fibrous protein systems for biotechnological and biomedical applications.

Comparative fluxome and metabolome analysis for overproduction of succinate in Escherichia coli

An aerobic succinate-producing Escherichia coli mutant was compared to its wild-type by quantitatively analyzing both the metabolome and fluxome, during glucose-limited steady-state and succinate excess dynamic conditions, in order to identify targets for further strain engineering towards more efficient succinate production. The mutant had four functional mutations under the conditions investigated: increased expression of a succinate exporter (DcuC), deletion of a succinate importer (Dct), deletion of succinate dehydrogenase (SUCDH) and expression of a PEP carboxylase (PPC) with increased capacity due to a point mutation. The steady-state and dynamic patterns of the intracellular metabolite levels and fluxes in response to changes were used to locate the quantitative differences in the physiology/metabolism of the mutant strain. Unexpectedly the mutant had a higher energy efficiency, indicated by a much lower rate of oxygen consumption, under glucose-limited conditions, caused by the deletion of the transcription factors IclR and ArcA. Furthermore the mutant had a much lower uptake capacity for succinate (26-fold) and oxygen (17-fold under succinate excess) compared to the wild-type strain. The mutant strain produced 7.9 mmol.CmolX(-1).h(-1) succinate during chemostat cultivation, showing that the choice of the applied genetic modifications was a successful strategy. Furthermore, the applied genetic modifications resulted in multiple large changes in metabolite levels (FBP, pyruvate, 6PG, NAD(+) /NADH ratio, α-ketogluarate) corresponding to large changes in fluxes. Compared to the wild-type a considerable flux shift occurred from the tricarboxylic acid (TCA) cycle to the oxidative part of the pentose phosphate pathway, including an inversion of the pyruvate kinase flux. The mutant responded very differently to excess of succinate, with a remarkable possible reversal of the TCA cycle. The mutant and the wild-type both showed homeostatic behaviour with respect to the energy charge. In contrast, large changes in redox ratios (NAD(+) /NADH) occurred in the wild-type, while the mutant showed even larger changes. This large redox change can be associated to the reversal of flux directions. The observed large flexibility in the central metabolism following genetic (deletions) and environmental (substrate excess) perturbations of the mutant, indicates that introducing a more efficient succinate exporter could result in an even higher succinate production rate.

White biotechnology: State of the art strategies for the development of biocatalysts for biorefining

White biotechnology is a term that is now often used to describe the implementation of biotechnology in the industrial sphere. Biocatalysts (enzymes and microorganisms) are the key tools of white biotechnology, which is considered to be one of the key technological drivers for the growing bioeconomy. Biocatalysts are already present in sectors such as the chemical and agro-food industries, and are used to manufacture products as diverse as antibiotics, paper pulp, bread or advanced polymers. This review proposes an original and global overview of highly complementary fields of biotechnology at both enzyme and microorganism level. A certain number of state of the art approaches that are now being used to improve the industrial fitness of biocatalysts particularly focused on the biorefinery sector are presented. The first part deals with the technologies that underpin the development of industrial biocatalysts, notably the discovery of new enzymes and enzyme improvement using directed evolution techniques. The second part describes the toolbox available by the cell engineer to shape the metabolism of microorganisms. And finally the last part focuses on the 'omic' technologies that are vital for understanding and guide microbial engineering toward more efficient microbial biocatalysts. Altogether, these techniques and strategies will undoubtedly help to achieve the challenging task of developing consolidated bioprocessing (i.e. CBP) readily available for industrial purpose.

Marine extremophiles: a source of hydrolases for biotechnological applications

The marine environment covers almost three quarters of the planet and is where evolution took its first steps. Extremophile microorganisms are found in several extreme marine environments, such as hydrothermal vents, hot springs, salty lakes and deep-sea floors. The ability of these microorganisms to support extremes of temperature, salinity and pressure demonstrates their great potential for biotechnological processes. Hydrolases including amylases, cellulases, peptidases and lipases from hyperthermophiles, psychrophiles, halophiles and piezophiles have been investigated for these reasons. Extremozymes are adapted to work in harsh physical-chemical conditions and their use in various industrial applications such as the biofuel, pharmaceutical, fine chemicals and food industries has increased. The understanding of the specific factors that confer the ability to withstand extreme habitats on such enzymes has become a priority for their biotechnological use. The most studied marine extremophiles are prokaryotes and in this review, we present the most studied archaea and bacteria extremophiles and their hydrolases, and discuss their use for industrial applications.

When plants produce not enough or at all: metabolic engineering of flavonoids in microbial hosts

As a result of the discovery that flavonoids are directly or indirectly connected to health, flavonoid metabolism and its fascinating molecules that are natural products in plants, have attracted the attention of both the industry and researchers involved in plant science, nutrition, bio/chemistry, chemical bioengineering, pharmacy, medicine, etc. Subsequently, in the past few years, flavonoids became a top story in the pharmaceutical industry, which is continually seeking novel ways to produce safe and efficient drugs. Microbial cell cultures can act as workhorse bio-factories by offering their metabolic machinery for the purpose of optimizing the conditions and increasing the productivity of a selective flavonoid. Furthermore, metabolic engineering methodology is used to reinforce what nature does best by correcting the inadequacies and dead-ends of a metabolic pathway. Combinatorial biosynthesis techniques led to the discovery of novel ways of producing natural and even unnatural plant flavonoids, while, in addition, metabolic engineering provided the industry with the opportunity to invest in synthetic biology in order to overcome the currently existing restricted diversification and productivity issues in synthetic chemistry protocols. In this review, is presented an update on the rationalized approaches to the production of natural or unnatural flavonoids through biotechnology, analyzing the significance of combinatorial biosynthesis of agricultural/pharmaceutical compounds produced in heterologous organisms. Also mentioned are strategies and achievements that have so far thrived in the area of synthetic biology, with an emphasis on metabolic engineering targeting the cellular optimization of microorganisms and plants that produce flavonoids, while stressing the advances in flux dynamic control and optimization. Finally, the involvement of the rapidly increasing numbers of assembled genomes that contribute to the gene- or pathway-mining in order to identify the gene(s) responsible for producing species-specific secondary metabolites is also considered herein.

Halophiles, coming stars for industrial biotechnology

Industrial biotechnology aims to produce chemicals, materials and biofuels to ease the challenges of shortage on petroleum. However, due to the disadvantages of bioprocesses including energy consuming sterilization, high fresh water consumption, discontinuous fermentation to avoid microbial contamination, highly expensive stainless steel fermentation facilities and competing substrates for human consumption, industrial biotechnology is less competitive compared with chemical processes. Recently, halophiles have shown promises to overcome these shortcomings. Due to their unique halophilic properties, some halophiles are able to grow in high pH and high NaCl containing medium under higher temperature, allowing fermentation processes to run contamination free under unsterile conditions and continuous way. At the same time, genetic manipulation methods have been developed for halophiles. So far, halophiles have been used to produce bioplastics polyhydroxyalkanoates (PHA), ectoines, enzymes, and bio-surfactants. Increasing effects have been made to develop halophiles into a low cost platform for bioprocessing with advantages of low energy, less fresh water consumption, low fixed capital investment, and continuous production.

Production of medium chain length polyhydroxyalkanoate in metabolic flux optimized Pseudomonas putida

BACKGROUND

Pseudomnas putida is a natural producer of medium chain length polyhydroxyalkanoates (mcl-PHA), a polymeric precursor of bioplastics. A two-fold increase of mcl-PHA production via inactivation of the glucose dehydrogenase gene gcd, limiting the metabolic flux towards side products like gluconate was achieved before. Here, we investigated the overproduction of enzymes catalyzing limiting steps of mcl-PHA precursor formation.

RESULTS

A genome-based in silico model for P. putida KT2440 metabolism was employed to identify potential genetic targets to be engineered for the improvement of mcl-PHA production using glucose as sole carbon source. Here, overproduction of pyruvate dehydrogenase subunit AcoA in the P. putida KT2440 wild type and the Δgcd mutant strains led to an increase of PHA production. In controlled bioreactor batch fermentations PHA production was increased by 33% in the acoA overexpressing wild type and 121% in the acoA overexpressing Δgcd strain in comparison to P. putida KT2440. Overexpression of pgl-encoding 6-phosphoglucolactonase did not influence PHA production. Transcriptome analyses of engineered PHA producing P. putida in comparison to its parental strains revealed the induction of genes encoding glucose 6-phosphate dehydrogenase and pyruvate dehydrogenase. In addition, NADPH seems to be quantitatively consumed for efficient PHA synthesis, since a direct relationship between low levels of NADPH and high concentrations of the biopolymer were observed. In contrast, intracellular levels of NADH were found increased in PHA producing organisms.

CONCLUSION

Production of mcl-PHAs was enhanced in P. putida when grown on glucose via overproduction of a pyruvate dehydrogenase subunit (AcoA) in combination with a deletion of the glucose dehydrogenase (gcd) gene as predicted by in silico elementary flux mode analysis.

Production of scopularide A in submerged culture with Scopulariopsis brevicaulis

BACKGROUND

Marine organisms produce many novel compounds with useful biological activity, but are currently underexploited. Considerable research has been invested in the study of compounds from marine bacteria, and several groups have now recognised that marine fungi also produce an interesting range of compounds. During product discovery, these compounds are often produced only in non-agitated culture conditions, which are unfortunately not well suited for scaling up. A marine isolate of Scopulariopsis brevicaulis, strain LF580, produces the cyclodepsipeptide scopularide A, which has previously only been produced in non-agitated cultivation.

RESULTS

Scopulariopsis brevicaulis LF580 produced scopularide A when grown in batch and fed-batch submerged cultures. Scopularide A was extracted primarily from the biomass, with approximately 7% being extractable from the culture supernatant. By increasing the biomass density of the cultivations, we were able to increase the volumetric production of the cultures, but it was important to avoid nitrogen limitation. Specific production also increased with increasing biomass density, leading to improvements in volumetric production up to 29-fold, compared with previous, non-agitated cultivations. Cell densities up to 36 g L-1 were achieved in 1 to 10 L bioreactors. Production of scopularide A was optimised in complex medium, but was also possible in a completely defined medium.

CONCLUSIONS

Scopularide A production has been transferred from a non-agitated to a stirred tank bioreactor environment with an approximately 6-fold increase in specific and 29-fold increase in volumetric production. Production of scopularide A in stirred tank bioreactors demonstrates that marine fungal compounds can be suitable for scalable production, even with the native production organism.

Cell system engineering to produce extracellular polyhydroxyalkanoate depolymerase with targeted applications

Novel platforms based on the application of bacterial cell systems as factories for production of new bioproducts open avenues and dramatically expand the catalogue of existing biomaterials. Herein, we designed the strategy based on in vivo production of extracellular Pseudomonas fluorescens GK13 (PhaZGK13) depolymerase to degrade previously biosynthesized polyhydroxyalkanotes (PHAs) or to obtain 3-hydroxyalkanoic acids (HAs). With this aim, extracellular PhaZGK13 was produced in recombinant strains and the optimal conditions for controlled release of HAs and oligomers by growing cells were set up with a particle suspension of (14)C-labelled PHA, being maximal after 24h of incubation. Genetic modification of key factors involved in fatty acids metabolism revealed the influence of an active β-oxidation pathway on the extracellular degradation of PHA and subsequent HAs isolation. The highest HAs production was obtained using Pseudomonas putida KT2442 fadB mutant (0.27mg/mL) due to the reduced ability of this strain to metabolize the degradation products. The system was applied to produce new added value HAs harboring thioester groups in the side chain from the functionalized mcl-PHA, PHACOS. Remarkably, hydrolyzed PHACOS showed greater potential to inhibit Staphylococcus aureus(T) growth when compared to that of degradation products of non functionalized polyhydroxyoctanoate-co-hexanoate P(HO-co-HH).

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Whole-cell biocatalysis for selective and productive C-O functional group introduction and modification

During the last decades, biocatalysis became of increasing importance for chemical and pharmaceutical industries. Regarding regio- and stereospecificity, enzymes have shown to be superior compared to traditional chemical synthesis approaches, especially in C-O functional group chemistry. Catalysts established on a process level are diverse and can be classified along a functional continuum starting with single-step biotransformations using isolated enzymes or microbial strains towards fermentative processes with recombinant microorganisms containing artificial synthetic pathways. The complex organization of respective enzymes combined with aspects such as cofactor dependency and low stability in isolated form often favors the use of whole cells over that of isolated enzymes. Based on an inventory of the large spectrum of biocatalytic C-O functional group chemistry, this review focuses on highlighting the potentials, limitations, and solutions offered by the application of self-regenerating microbial cells as biocatalysts. Different cellular functionalities are discussed in the light of their (possible) contribution to catalyst efficiency. The combined achievements in the areas of protein, genetic, metabolic, and reaction engineering enable the development of whole-cell biocatalysts as powerful tools in organic synthesis.

An extended dynamic model of Lactococcus lactis metabolism for mannitol and 2,3-butanediol production

Biomedical research and biotechnological production are greatly benefiting from the results provided by the development of dynamic models of microbial metabolism. Although several kinetic models of Lactococcus lactis (a Lactic Acid Bacterium (LAB) commonly used in the dairy industry) have been developed so far, most of them are simplified and focus only on specific metabolic pathways. Therefore, the application of mathematical models in the design of an engineering strategy for the production of industrially important products by L. lactis has been very limited. In this work, we extend the existing kinetic model of L. lactis central metabolism to include industrially relevant production pathways such as mannitol and 2,3-butanediol. In this way, we expect to study the dynamics of metabolite production and make predictive simulations in L. lactis. We used a system of ordinary differential equations (ODEs) with approximate Michaelis-Menten-like kinetics for each reaction, where the parameters were estimated from multivariate time-series metabolite concentrations obtained by our team through in vivo Nuclear Magnetic Resonance (NMR). The results show that the model captures observed transient dynamics when validated under a wide range of experimental conditions. Furthermore, we analyzed the model using global perturbations, which corroborate experimental evidence about metabolic responses upon enzymatic changes. These include that mannitol production is very sensitive to lactate dehydrogenase (LDH) in the wild type (W.T.) strain, and to mannitol phosphoenolpyruvate: a phosphotransferase system (PTS(Mtl)) in a LDH mutant strain. LDH reduction has also a positive control on 2,3-butanediol levels. Furthermore, it was found that overproduction of mannitol-1-phosphate dehydrogenase (MPD) in a LDH/PTS(Mtl) deficient strain can increase the mannitol levels. The results show that this model has prediction capability over new experimental conditions and offers promising possibilities to elucidate the effect of alterations in the main metabolism of L. lactis, with application in strain optimization.