Search

The conversion of CO2 into methanol depicts one of the most promising emerging renewable routes for the chemical and biotech industry. Under this regard, native methylotrophs have a large potential for converting methanol into value-added products but require targeted engineering approaches to enhance their performances and to widen their product spectrum. Here we use a systems-based approach to analyze and engineer M. extorquens TK 0001 for production of glycolic acid. Application of constraint-based metabolic modeling reveals the great potential of M. extorquens for that purpose, which is not yet described in literature. In particular, a superior theoretical product yield of 1.0 C-molGlycolic acid C-molMethanol−1 is predicted by our model, surpassing theoretical yields of sugar fermentation. Following this approach, we show here that strain engineering is viable and present 1st generation strains producing glycolic acid via a heterologous NADPH-dependent glyoxylate reductase. It was found that lactic acid is a surprising by-product of glycolic acid formation in M. extorquens, most likely due to a surplus of available NADH upon glycolic acid synthesis. Finally, the best performing strain was tested in a fed-batch fermentation producing a mixture of up to total 1.2 g L−1 glycolic acid and lactic acid. Several key performance indicators of our glycolic acid producer strain are superior to state-of-the-art synthetic methylotrophs. The presented results open the door for further strain engineering of the native methylotroph M. extorquens and pave the way to produce two promising biopolymer building blocks from green methanol, i.e., glycolic acid and lactic acid. [ABSTRACT FROM AUTHOR]

Replacing traditional substrates in industrial bioprocesses to advance the sustainable production of chemicals is an urgent need in the context of the circular economy. However, since the limited degradability of non-conventional carbon sources often returns lower yields, effective exploitation of such substrates requires a multi-layer optimization which includes not only the provision of a suitable feedstock but the use of highly robust and metabolically versatile microbial biocatalysts. We tackled this challenge by means of systems metabolic engineering and validated Escherichia coli W as a promising cell factory for the production of the key building block chemical 2-ketoisovalerate (2-KIV) using whey as carbon source, a widely available and low-cost agro-industrial waste. First, we assessed the growth performance of Escherichia coli W on mono and disaccharides and demonstrated that using whey as carbon source enhances it significantly. Second, we searched the available literature and used metabolic modeling approaches to scrutinize the metabolic space of E. coli and explore its potential for overproduction of 2-KIV identifying as basic strategies the block of pyruvate depletion and the modulation of NAD/NADP ratio. We then used our model predictions to construct a suitable microbial chassis capable of overproducing 2-KIV with minimal genetic perturbations, i.e., deleting the pyruvate dehydrogenase and malate dehydrogenase. Finally, we used modular cloning to construct a synthetic 2-KIV pathway that was not sensitive to negative feedback, which effectively resulted in a rerouting of pyruvate towards 2-KIV. The resulting strain shows titers of up to 3.22 ± 0.07 g/L of 2-KIV and 1.40 ± 0.04 g/L of L-valine in 24 h using whey in batch cultures. Additionally, we obtained yields of up to 0.81 g 2-KIV/g substrate. The optimal microbial chassis we present here has minimal genetic modifications and is free of nutritional autotrophies to deliver high 2-KIV production rates using whey as a non-conventional substrate.

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their productivity and product quality, media reformulation has been a key strategy, albeit with several technical challenges, due to the myriad of complex molecular mechanisms underlying media effects on culture performance. Thus, it is imperative to characterize metabolic bottlenecks under various media conditions systematically. To do so, we combined partial least square regression (PLS‐R) with the flux balance analysis of a genome‐scale metabolic model to elucidate the physiological states and metabolic behaviors of human alpha‐1 antitrypsin producing CHO‐DG44 cells grown in one commercial and another two in‐house media under development. At the onset, PLS‐R was used to identify metabolite exchanges that were correlated to specific growth and productivity. Then, by comparing metabolic states described by resultant flux distributions under two of the media conditions, we found suboptimal level of four nutrients and two metabolic wastes, which plausibly hindered cellular growth and productivity; mechanistically, lactate and ammonia recycling were modulated by glutamine and asparagine metabolisms in the media conditions, and also by hitherto unsuspected folate and choline supplements. Our work demonstrated how multivariate statistical analysis can be synergistically combined with metabolic modeling to uncover the mechanistic elements underlying differing media performance. It thus paved the way for the systematic identification of nutrient targets for medium reformulation to enhance recombinant protein production in CHO cells. [ABSTRACT FROM AUTHOR]

Mathematical modeling is a promising tool for better understanding of cellular processes. In recent years, the development of coarse-grained models has gained attraction since these simple models are able to capture and describe a broad range of growth conditions. Coarse-grained models often comprise only two cellular components, a low molecular component as representative for central metabolism and energy generation and a macromolecular component, representing the entire proteome. A framework is presented that presents a strict mass conservative model for bacterial growth during a biotechnological production process. After providing interesting properties for the steady-state solution, applications are presented 1) for a production process of an amino acid and 2) production of a metabolite from central metabolism.

Abstract Background Lipid accumulation by oleaginous microorganisms is of great scientific interest and biotechnological potential. While nitrogen limitation has been routinely employed, low-cost raw materials usually contain rich nitrogenous components, thus preventing from efficient lipid production. Inorganic phosphate (Pi) limitation has been found sufficient to promote conversion of sugars into lipids, yet the molecular basis of cellular response to Pi limitation and concurrent lipid accumulation remains elusive. Results Here, we performed multi-omic analyses of the oleaginous yeast Rhodosporidium toruloides to shield lights on Pi-limitation-induced lipid accumulation. Samples were prepared under Pi-limited as well as Pi-repleted chemostat conditions, and subjected to analysis at the transcriptomic, proteomic, and metabolomic levels. In total, 7970 genes, 4212 proteins, and 123 metabolites were identified. Results showed that Pi limitation facilitates up-regulation of Pi-associated metabolism, RNA degradation, and triacylglycerol biosynthesis while down-regulation of ribosome biosynthesis and tricarboxylic acid cycle. Pi limitation leads to dephosphorylation of adenosine monophosphate and the allosteric activator of isocitrate dehydrogenase key to lipid biosynthesis. It was found that NADPH, the key cofactor for fatty acid biosynthesis, is limited due to reduced flux through the pentose phosphate pathway and transhydrogenation cycle and that this can be overcome by over-expression of an endogenous malic enzyme. These phenomena are found distinctive from those under nitrogen limitation. Conclusions Our data suggest that Pi limitation activates Pi-related metabolism, RNA degradation, and TAG biosynthesis while inhibits ribosome biosynthesis and TCA cycle, leading to enhanced carbon fluxes into lipids. The information greatly enriches our understanding on microbial oleaginicity and Pi-related metabolism. Importantly, systems data may facilitate designing advanced cell factories for production of lipids and related oleochemicals.

Future manufacturing will focus on new, improved products as well as on new and enhanced production methods. Recent biotechnological and scientific advances, such as CRISPR/Cas and various omic technologies, pave the way to exciting novel biotechnological research, development, and commercialization of new sustainable products. Rigorous mathematical descriptions of microbial cells and consortia thereof will enable deeper biological understanding and lead to powerful in silico cellular models. Biological engineering, namely model-based design together with synthetic biology, will accelerate the construction of robust and high-performing microorganisms. Using these organisms, and ambitions towards zero-concepts with respect to emissions and excess resources in bioprocess engineering, industrial biotechnology is expected to become highly integrated into sustainable generations of technology systems. Recent biotechnological and scientific advances are paving the way to exciting novel biotechnological research, development, and commercialization of new sustainable products. Rigorous mathematical descriptions of microbial cells and consortia thereof will enable deeper biological understanding and lead to powerful in silico cellular models. Biological engineering will accelerate the construction of robust and high-performing microorganisms. Using these organisms and 'zero-concepts' in bioprocess engineering, industrial biotechnology will become highly integrated into sustainable generations of technology systems. [ABSTRACT FROM AUTHOR]

A combined experimental/theoretical approach is presented, for improving the predictability of Saccharomyces cerevisiae fermentations. In particular, a mathematical model was developed explicitly taking into account the main mechanisms of the fermentation process, allowing for continuous computation of key process variables, including the biomass concentration and the respiratory quotient (RQ). For model calibration and experimental validation, batch and fed-batch fermentations were carried out. Comparison of the model-predicted biomass concentrations and RQ developments with the corresponding experimentally recorded values shows a remarkably good agreement for both batch and fed-batch processes, confirming the adequacy of the model. Furthermore, sensitivity studies were performed, in order to identify model parameters whose variations have significant effects on the model predictions: our model responds with significant sensitivity to the variations of only six parameters. These studies provide a valuable basis for model reduction, as also demonstrated in this paper. Finally, optimization-based parametric studies demonstrate how our model can be utilized for improving the efficiency of Saccharomyces cerevisiae fermentations. [ABSTRACT FROM AUTHOR]

Shikimic acid is an important intermediate for the manufacture of the antiviral drug oseltamivir (Tamiflu®) and many other pharmaceutical compounds. Much of its existing supply is obtained from the seeds of Chinese star anise (Illicium verum). Nevertheless, plants cannot supply a stable source of affordable shikimate along with laborious and cost-expensive extraction and purification process. Microbial biosynthesis of shikimate through metabolic engineering and synthetic biology approaches represents a sustainable, cost-efficient, and environmentally friendly route than plant-based methods. Metabolic engineering allows elevated shikimate production titer by inactivating the competing pathways, increasing intracellular level of key precursors, and overexpressing rate-limiting enzymes. The development of synthetic and systems biology-based novel technologies have revealed a new roadmap for the construction of high shikimate-producing strains. This review elaborates the enhanced biosynthesis of shikimate by utilizing an array of traditional metabolic engineering along with novel advanced technologies. The first part of the review is focused on the mechanistic pathway for shikimate production, use of recombinant and engineered strains, improving metabolic flux through the shikimate pathway, chemically inducible chromosomal evolution, and bioprocess engineering strategies. The second part discusses a variety of industrially pertinent compounds derived from shikimate with special reference to aromatic amino acids and phenazine compound, and main engineering strategies for their production in diverse bacterial strains. Towards the end, the work is wrapped up with concluding remarks and future considerations. [ABSTRACT FROM AUTHOR]

Objective: Chlamydomonas reinhardtii, the most commonly studied model organism in microalgal kingdom, is known to be a rich lipid producer. Methods: Here, a naturally isolated C. reinhardtii strain was cultivated phototrophically at Erlenmeyer scale in BG11 culture medium. After reaching to stationary growth phase, a nitrogen limited regime with different nitrogen concentrations was applied. Results: Quantitative real time PCR analysis of DGTT1 and DGTT2 genes involved in triacylglycerol accumulation confirmed an increased level of expression for DGTT1 (21.05-fold increase) in nitrogen starvation compared with the nitrogen rich conditions. Besides, DGTT2 transcript levels showed a slight decrease in nitrogen deprivation condition (0.61-fold). Conclusion: The results opened a new way to direct microalgal systems biotechnology towards maximization of triacylglycerol production for green energy production. [ABSTRACT FROM AUTHOR]

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods. [ABSTRACT FROM AUTHOR]

The rising potential for CRISPR–Cas-mediated genome editing has revolutionized our strategies in basic and practical bioengineering research. It provides a predictable and precise method for genome modification in a robust and reproducible fashion. Emergence of systems biotechnology and synthetic biology approaches coupled with CRISPR–Cas technology could change the future of cell factories to possess some new features which have not been found naturally. We have discussed the possibility and versatile potentials of CRISPR–Cas technology for metabolic engineering of a recombinant host for heterologous protein production. We describe the mechanisms involved in this metabolic engineering approach and present the diverse features of its application in biotechnology and protein production. [ABSTRACT FROM AUTHOR]

The production of aromatic amino acids using fermentation processes with recombinant microorganisms can be an advantageous approach to reach their global demands. In addition, a large array of compounds with alimentary and pharmaceutical applications can potentially be synthesized from intermediates of this metabolic pathway. However, contrary to other amino acids and primary metabolites, the artificial channelling of building blocks from central metabolism towards the aromatic amino acid pathway is complicated to achieve in an efficient manner. The length and complex regulation of this pathway have progressively called for the employment of more integral approaches, promoting the merge of complementary tools and techniques in order to surpass metabolic and regulatory bottlenecks. As a result, relevant insights on the subject have been obtained during the last years, especially with genetically modified strains of Escherichia coli. By combining metabolic engineering strategies with developments in synthetic biology, systems biology and bioprocess engineering, notable advances were achieved regarding the generation, characterization and optimization of E. coli strains for the overproduction of aromatic amino acids, some of their precursors and related compounds. In this paper we review and compare recent successful reports dealing with the modification of metabolic traits to attain these objectives. [ABSTRACT FROM AUTHOR]

Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. [ABSTRACT FROM AUTHOR]

Abstract: Streptomyces species continue to attract attention as a source of novel medicinal compounds. Despite a long history of studies on these microorganisms, they still have many biochemical mysteries to be elucidated. Investigations of novel secondary metabolites and their biosynthetic gene clusters have been more systematized with high-throughput techniques through inspections of correlations among components of the primary and secondary metabolisms at the genome scale. Moreover, up-to-date information on the genome of Streptomyces species with emphasis on their secondary metabolism has been collected in the form of databases and knowledgebases, providing predictive information and enabling one to explore experimentally unrecognized biological spaces of secondary metabolism. Herein, we review recent trends in the systems biology and biotechnology of Streptomyces species. [Copyright &y& Elsevier]

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications. [ABSTRACT FROM AUTHOR]

Abstract: The demand for bio-based processes and materials in the petrochemical industry has significantly increased during the last decade because of the expected running out of petroleum. This trend can be ascribed to three main causes: (1) the increased use of renewable resources for chemical synthesis of already established product classes, (2) the replacement of chemical synthesis of already established product classes by new biotechnological processes based on renewable resources, and (3) the biotechnological production of new molecules with new features or better performances than already established comparable chemically synthesized products. All three approaches are currently being pursued for surfactant production. Biosurfactants are a very promising and interesting substance class because they are based on renewable resources, sustainable, and biologically degradable. Alkyl polyglycosides are chemically synthesized biosurfactants established on the surfactant market. The first microbiological biosurfactants on the market were sophorolipids. Of all currently known biosurfactants, rhamnolipids have the highest potential for becoming the next generation of biosurfactants introduced on the market. Although the metabolic pathways and genetic regulation of biosynthesis are known qualitatively, the quantitative understanding relevant for bioreactor cultivation is still missing. Additionally, high product titers have been exclusively described with vegetable oil as sole carbon source in combination with Pseudomonas aeruginosa strains. Competitive productivity is still out of reach for heterologous hosts or non-pathogenic natural producer strains. Thus, on the one hand there is a need to gain a deeper understanding of the regulation of rhamnolipid production on process and cellular level during bioreactor cultivations. On the other hand, there is a need for metabolizable renewable substrates, which do not compete with food and feed. A sustainable bioeconomy approach should combine a holistic X-omics strategy with metabolic engineering to achieve the next step in rhamnolipid production based on non-food renewable resources. This review discusses different approaches towards optimization of rhamnolipid production and enhancement of product spectra. The optimization of rhamnolipid production with P. aeruginosa strains, screening methods for new non-pathogenic natural rhamnolipid producers and recombinant rhamnolipid production are examined. Finally, biocatalysis with rhamnolipids for the synthesis of l-rhamnose, β-hydroxyfatty acids, and tailor-made surfactants is discussed. Biosurfactants are still in the phase of initial commercialization. However, for next generation development of rhamnolipid production processes and next generation biosurfactants there are still considerable obstacles to be surmounted, which are discussed here. [Copyright &y& Elsevier]

A central concern of biopharmaceutical R&D is the production of sufficient quantities of recombinant products from manufacturing processes based on animal cell culture. The way in which bioprocess researchers have addressed this question experienced a tremendous shift over the years, progressing from almost empirical to more rational approaches. A step further is the application of systems biotechnology: recent technological advances for large-scale cell state characterization and creative methods for host cell modeling are becoming crucial for next-generation bioprocess optimization. Here we provide an overview of the main trends towards this goal, with a focus on metabolic models as central scaffolds for data integration and prediction of bioprocess outcomes. [Copyright &y& Elsevier]

During the last decade, the demand for economical and sustainable bioprocesses replacing petrochemical-derived products has significantly increased. Rhamnolipids are interesting biosurfactants that might possess a broad industrial application range. However, despite of 60 years of research in the area of rhamnolipid production, the economic feasibility of these glycolipids is pending. Although the biosynthesis and regulatory network are in a big part known, the actual incidents on the cellular and process level during bioreactor cultivation are not mastered. Traditional engineering by random and targeted genetic alteration, process design, and recombinant strategies did not succeed by now. For enhanced process development, there is an urgent need of in-depth information about the rhamnolipid production regulation during bioreactor cultivation to design knowledge-based genetic and process engineering strategies. Rhamnolipids are structurally comparable, simple secondary metabolites and thus have the potential to become instrumental in future secondary metabolite engineering by systems biotechnology. This review summarizes current knowledge about the regulatory and metabolic network of rhamnolipid synthesis and discusses traditional and advanced engineering strategies performed for rhamnolipid production improvement focusing on Pseudomonas aeruginosa. Finally, the opportunities of applying the systems biotechnology toolbox on the whole-cell biocatalyst and bioprocess level for further rhamnolipid production optimization are discussed. [ABSTRACT FROM AUTHOR]

Abstract: In systems biotechnology biochemical network maps are the essential bases for analyzing their topological features and for simulating their dynamic behaviors. Most of network data are available at public databases, relating to gene expression, protein–protein interaction, and metabolic pathways. On the other hand, since those databases use a variety of data formats, computational tools always require the conversion of such database formats into their available format. In this work, we developed a GUI-based WWW browser program, which converts different databases (KEGG, RegulonDB, DIP, etc.) into the format available to our CADLIVE system, integrates a variety of network data into a fully functional pathway map, and visualizes the integrated map. This program is named the CADLIVE Converter. We demonstrated the feasibility and availability of it by performing metabolic pathway analysis and dynamic modeling. The visualization of the network map lead to an intuitive understanding of topological architecture of complex networks. [Copyright &y& Elsevier]

Rhamnolipids are biosurfactants with interesting physico-chemical properties. However, the main obstacles towards an economic production are low productivity, high raw-material costs, relatively expensive downstream processing, and a lack of understanding the rhamnolipid production regulation in bioreactor systems. This study shows that the sequenced Pseudomonas aeruginosa strain PAO1 is able to produce high quantities of rhamnolipid during 30 L batch bioreactor cultivations with sunflower oil as sole carbon source and nitrogen limiting conditions. Thus PAO1 could be an appropriate model for rhamnolipid production in pilot plant bioreactor systems. In contrast to well-established production strains, PAO1 allows knowledge-based systems biotechnological process development combined with the frequently used heuristic bioengineering approach. The maximum rhamnolipid concentration obtained was 39 g/L after 90 h of cultivation. The volumetric productivity of 0.43 g/Lh was comparable with previous described production strains. The specific rhamnolipid productivity showed a maximum between 40 and 70 h of process time of 0.088 gRL/gBDMh. At the same time interval, a shift of the molar di- to mono-rhamnolipid ratio from 1:1 to about 2:1 was observed. PAO1 not only seems to be an appropriate model, but surprisingly has the potential as a strain of choice for actual biotechnological rhamnolipid production. [ABSTRACT FROM AUTHOR]

Abstract: Genetic algorithms and optimization in general, enable us to probe deeper into the metabolic pathway recipe for multi-product biosynthesis. An augmented model for optimizing serine and tryptophan flux ratios simultaneously in Escherichia coli, was developed by linking the dynamic tryptophan operon model and aromatic amino acid-tryptophan biosynthesis pathways to the central carbon metabolism model. Six new kinetic parameters of the augmented model were estimated with considerations of available experimental data and other published works. Major differences between calculated and reference concentrations and fluxes were explained. Sensitivities and underlying competition among fluxes for carbon sources were consistent with intuitive expectations based on metabolic network and previous results. Biosynthesis rates of serine and tryptophan were simultaneously maximized using the augmented model via concurrent gene knockout and manipulation. The optimization results were obtained using the elitist non-dominant sorting genetic algorithm (NSGA-II) supported by pattern recognition heuristics. A range of Pareto-optimal enzyme activities regulating the amino acids biosynthesis was successfully obtained and elucidated wherever possible vis-à-vis fermentation work based on recombinant DNA technology. The predicted potential improvements in various metabolic pathway recipes using the multi-objective optimization strategy were highlighted and discussed in detail. [Copyright &y& Elsevier]

Abstract: The developments of the systems biotechnology and its application in the industrial process open up new horizons to industrial biotechnology. The unprecedented understanding of the relationships between cellular behaviors and the surrounding environments during the bioprocess has been achieved. In this paper, we review new advances in the strain improvement, bioprocess control and optimization. The holistic viewpoints and ideas applied in industrial bioprocesses and their future prospects are discussed by illustrating some successful cases. [Copyright &y& Elsevier]

Systems biotechnology has been established as a highly potent tool for bioprocess development in recent years. The applicability to complex metabolic processes such as protein synthesis and secretion, however, is still in its infancy. While yeasts are frequently applied for heterologous protein production, more progress in this field has been achieved for bacterial and mammalian cell culture systems than for yeasts. A critical comparison between different protein production systems, as provided in this review, can aid in assessing the potentials and pitfalls of applying systems biotechnology concepts to heterologous protein producing yeasts. Apart from modelling, the methodological basis of systems biology strongly relies on postgenomic methods. However, this methodology is rapidly moving so that more global data with much higher sensitivity will be achieved in near future. The development of next generation sequencing technology enables an unexpected revival of genomic approaches, providing new potential for evolutionary engineering and inverse metabolic engineering. [ABSTRACT FROM AUTHOR]

Abstract: The well-documented ability to degrade lignin and a variety of complex chemicals showed by the white-rot fungus Phanerochaete chrysosporium has made it the subject of many studies in areas of environmental concern, including pulp bioleaching and bioremediation technologies. However, until now, most of the work in this field has been focused on the ligninolytic sub-system but, due to the great complexity of the involved processes, less progress has been made in understanding the biochemical regulatory structure that could explain growth dynamics, the substrate utilization and the ligninolytic system production itself. In this work we want to tackle this problem from the perspectives and approaches of systems biology, which have been shown to be effective in the case of complex systems. We will use a top-down approach to the construction of this model aiming to identify the cellular sub-systems that play a major role in the whole process. We have investigated growth dynamics, substrate consumption and lignin peroxidase production of the P. chrysosporium wild type under a set of definite culture conditions. Based on data gathered from different authors and in our own experimental determinations, we built a model using a GMA power-law representation, which was used as platform to make predictive simulations. Thereby, we could assess the consistency of some current assumptions about the regulatory structure of the overall process. The model parameters were estimated from a time series experimental measurements by means of an algorithm previously adapted and optimized for power-law models. The model was subsequently checked for quality by comparing its predictions with the experimental behavior observed in new, different experimental settings and through perturbation analysis aimed to test the robustness of the model. Hence, the model showed to be able to predict the dynamics of two critical variables such as biomass and lignin peroxidase activity when in conditions of nutrient deprivation and after pulses of veratryl alcohol. Moreover, it successfully predicts the evolution of the variables during both, the active growth phase and after the deprivation shock. The close agreement between the predicted and observed behavior and the advanced understanding of its kinetic structure and regulatory features provides the necessary background for the design of a biotechnological set-up designed for the continuous production of the ligninolityc system and its optimization. [Copyright &y& Elsevier]

The increasing demand for recombinant therapeutic proteins has placed significant pressure on the biopharmaceutical industry to develop high-yielding, mammalian cell-based production systems. Current efforts to increase the production of recombinant proteins by mammalian host cells largely proceed by the lengthy screening of clonal derivatives rather than by directed genetic or metabolic engineering. However, the advent of systems biology has created a new set of tools that will ensure that future engineering strategies will be informed by an understanding of the genetic/regulatory and metabolic networks that determine the functional competence of mammalian cell factories in vitro. In this review we summarize recent systems-level studies that utilize genome-scale analytical tools to analyse the functional basis for key production process characteristics such as high cell-specific productivity, correct product processing and rapid cell proliferation in the in vitro environment. We also describe the use of high-throughput-omic technologies to investigate how mammalian cell factories respond to environmental and metabolic perturbation. [ABSTRACT FROM AUTHOR]