ECUT (Energy Conversion and Utilization Technologies) program: Biocatalysis Project

Fiscal year 1987 research activities and accomplishments for the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Division are presented. The project's technical activities were organized into three work elements. The Molecular Modeling and Applied Genetics work element includes modeling and simulation studies to verify a dynamic model of the enzyme carboxypeptidase; plasmid stabilization by chromosomal integration; growth and stability characteristics of plasmid-containing cells; and determination of optional production parameters for hyper-production of polyphenol oxidase. The Bioprocess Engineering work element supports efforts in novel bioreactor concepts that are likely to lead to substantially higher levels of reactor productivity, product yields, and lower separation energetics. The Bioprocess Design and Assessment work element attempts to develop procedures (via user-friendly computer software) for assessing the economics and energetics of a given biocatalyst process

The mathematical model of Schizosaccharomyces pombe : Batch and repeated batch simulations.

Mathematical models are playing an important part in the current developments in engineering, science and biotechnology. Within this field the most fashionable and representative organisms are the ones who are genetically and physiologically tractable. Since the fission yeast Schizosaccharomyces pombe plays a role model among them and its behaviour has medical, genetic and industrial links (related to cancer research, metabolic pathways and beer production), this makes it a particularly interesting organism for study. This dissertation presents the first physiological model ever developed for the yeast S. pombe. The model allows for the simulation and prediction of batch and repeated batch experiments which are an important engineering tool in terms of optimization of industrial processes improving yield in bioreactors by predicting precise values of harvest fraction (HF) and dilution cycle times (DCT). The model has been developed within the generic modelling framework of CelCyMUS (Cell Cycle Model University of Surrey). As part of the research being carried out CelCyMUS has been up-dated by introducing the new Fortran 95 features and utilities in order to exploit its powerful new features and to keep the generic model in pace with technological software advancements. The model is a one-dimensional age-based population balance for the fission yeast S. pombe. It includes the four typical phases (S, G2, M and G1) with the G2 phase divided into two phases (G2A, G2B) and two checkpoints that govern the movement of cells between G1 and S, and G2B and M phases. The transitions (movement of cells between phases) are determined by a probability function related to the consumption of glucose. The G2B-M transition is also dependent on cell size, but since individual growth of cells is related to the consumption of the carbon source (in this case glucose), cell size is dependent upon the amount of glucose consumed per cell. The model also includes a phase for cells facing starvation before going into a meiotic cycle, with some chance of coming back to the mitotic cycle, and a death phase that accounts for cells dying with no chance of recovering at all. Parameters in the S. pombe model have been gathered from experimental data in batch culture reported in literature. Data generated from this specific model have been compared with data from experiments (Fotuhi, 2002) in batch and repeated batch cultures of S. pombe following the behaviour of population balance, consumption of nutrients, and production of metabolites. The new code was tested by successftilly reproducing data from mm-321 hybridoma cell line, the first specific model of a cell line developed in CelCyMUS. As a new feature a model of mass transfer has been incorporated in the generic framework. This mass transfer module accounts for interactions of metabolites (oxygen and carbon dioxide) in the gas and liquid phase of bioreactors. The new S. pombe model was fitted to the experiments of Creanor (1992) on synchronised cultures where the consumption of oxygen was being measured. Such experiments identify two points (G2B and G1) where the rate of oxygen uptake increased in the cycle, doubling the consumption at the end of every cycle. With the model fitted to experimental results in synchronised cultures of S. pombe the model was then used to simulate desynchronised cultures. S. pombe was successfully tested when reproducing experimental data generated by Fotuhi (2002) in S.pombe for batch and repeated batch bioreactors. The S. pombe model was able to simulate cell number, oxygen and glucose consumption. Carbon dioxide and ATP production were predicted by the model however there was no experimental data to compare with. Now that the S. pombe model has been tested against experimental data it will be applied in a model-based observer strategy for the online control of bioreactors

Fate of trace metals in anaerobic digestion

© Springer International Publishing Switzerland 2015. A challenging, and largely uncharted, area of research in the field of anaerobic digestion science and technology is in understanding the roles of trace metals in enabling biogas production. This is a major knowledge gap and a multifaceted problem involving metal chemistry; physical interactions of metal and solids; microbiology; and technology optimization. Moreover, the fate of trace metals, and the chemical speciation and transport of trace metals in environments— often agricultural lands receiving discharge waters from anaerobic digestion processes— simultaneously represents challenges for environmental protection and opportunities to close process loops in anaerobic digestion.The authors acknowledge funding within the framework of the COST Action 1302 (‘European Network on Ecological Roles of Trace Metals in Anaerobic Biotechnologies’). GC is supported by a European Research Council Starting Grant (‘3C-BIOTECH; No. 261330).Peer Reviewe

Bioengineering approaches to simulate human colon microbiome ecosystem

Background Several diseases associated to colon microbial imbalance (dysbiosis), such as obesity, diabetes, inflammatory bowel disease, cardiovascular disease and cancer, are being reverted by modulation of gut microbiota composition through treatment with prebiotics and probiotics. Multiple in vitro models have been developed over the past three decades, with several experimental configurations, as they provide a quick, easy, and cost-effective approach to study the gut microbiome, as compared to troublesome and time-consuming in vivo studies. Scope and approach This review aims to provide an overview of the most relevant available in vitro models used to mimic the human colon microbiome dynamics, including macro-scale and microfluidic-based models. Main characteristics, functionalities, current applications and advantages or disadvantages of the models are discussed in order to provide useful information for end users (namely food and pharmaceutical researchers), when selecting the most appropriated model for assessing health claims and safety of novel functional food and drugs. Finally, the use of these colon models as a tool to study prebiotic and probiotic response in host-microbiota interaction is reviewed. Key findings and conclusions A wide range of in vitro models representing specific colon parts have been developed. However, none of these models can simultaneously cover all the key conditions found in the human colon (namely anatomical, physical, biochemical, and biological characteristics), as well as the complex microbiome-host interaction. Thus, there is a significant opportunity for further improvement of the models experimental setups towards more realistic operating systems, including mucosal surfaces, intestinal cells and tissues allowing microbiomehost crosstalk representation.SFRH/BD/139884/2018 from the Portuguese Foundation for Science and Technology (FCT). This study was supported by the FCT under the scope of the strategic funding of UIDB/04469/2020 unit and the Project ColOsH PTDC/BTM–SAL/30071/2017 (POCI–01–0145–FEDER–030071)info:eu-repo/semantics/publishedVersio

Simulation and Optimization of a Continuous Biochemical Reactor

The present work focused on the dynamic and optimization of a continuous biochemical reactor using the glucose as a substrate. Simulated model provides the development of the process and reducing the risk of experimental runs. The selected process variables are; dilution rate (D), feed substrate concentration (Si), pH and temperature (T).The  major effect of D was observed at Si below 20 g/L. pH and T are affecting within Si of 60 g/L.Si is the effective process variable on the dynamic characteristics of the reactor. Reasonable agreement has found when compared the simulated results with that obtained by the previous work .Optimization technique guides the decision maker to select the best operating conditions.Stochastic genetic algorithm has found suitable for the nonlinear reactor.Optimal results indicate that the maximum biomass concentration (X) is 80.57 g/L at Si of 197.56 g/L and low D of 0.1(1/hr).Si was the sensitive variable for changing of the objective X.   Keywords: Biochemical reactor; Dynamic model; Optimization; Simulation

Fermentation: Metabolism, Kinetic Models, and Bioprocessing

Biochemical and metabolic interpretation of microbial growth is an important topic in bioreactor design. We intend to address valuable information about the relation of critical operation variables and the simulation of bioprocesses with unstructured and structured kinetic models. Process parameters such as nutrient supply, pH, dissolved oxygen, and metabolic end-products directly impact the physiology and metabolism of microorganisms. Changes in the membrane as well as cell viability are of interest since protein expression and maturation in prokaryota are directly related to membrane integrity. This chapter intends to deliver an insight of different alternatives in kinetic modeling