On enhancing productivity of bioethanol with multiple species

The present work is initiated to investigate whether a defined culture comprising a mixture of three yeast species, Kluyveromyces marxianus, Saccharomyces cerevisiae, and Pichia stipitis can ferment a mixture of sugars to produce bioethanol at rates higher than those achieved by pure cultures of the same. For this purpose, we develop models of single species based on the hybrid cybernetic model framework, and simulate fermentations in the mixed culture by combining individual models. An underlying assumption is that the behavior of each species is determined only by the common environment independently of the presence and metabolism of other species. Model performance is thoroughly assessed using the experimental data available in the literature. The dynamic behavior of mixed cultures in mixed culture experiments are accurately predicted by the model reflecting faithfully the simultaneous/sequential uptake patterns of mixed substrates. This model is then used to investigate performance of various possible reactor configurations. With the foregoing species of organisms, mixed culture itself does not lead to a significant increase of bioethanol productivity. Rather, the model shows that substantial improvement is acquired by sequential use of different, properly chosen organisms during fermentation. Thus, the successive use of K. marxianus and P. stipitis is shown to increase bioethanol productivity up to about 58% in comparison to fermentation by single species alone.     

气液双升式反应器动物细胞培养及批式生长动力学模型

初步研究了气液双升式动物细胞反应器微载体培养 Bowes细胞和悬浮培养 M4G3杂交瘤细胞的生长条件 ,在不加入消泡剂和保护剂的情况下 ,批式培养 Bowes细胞的最大密度为 2 .6×1 0 6/ml,批式培养 M4G3细胞的最大密度为 1 .5× 1 0 6/ml。基于细胞生长的密度效应 ,建立了动物细胞生长动力学模型 :　　 μ=0　　 t相似文献   

Analytical solution for a hybrid Logistic-Monod cell growth model in batch and continuous stirred tank reactor culture

Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis–Menten equation. In the case of the Logistic equation, the specific growth rate is determined by the carrying capacity of the system, which could be growth-inhibiting factors (i.e., toxic chemical accumulation) other than the nutrient level. Both equations have been found valuable to guide us build unstructured kinetic models to analyze the fermentation process and understand cell physiology. In this work, we present a hybrid Logistic-Monod growth model, which accounts for multiple growth-dependent factors including both the limiting nutrient and the carrying capacity of the system. Coupled with substrate consumption and yield coefficient, we present the analytical solutions for this hybrid Logistic-Monod model in both batch and continuous stirred tank reactor (CSTR) culture. Under high biomass yield (Y_x/s) conditions, the analytical solution for this hybrid model is approaching to the Logistic equation; under low biomass yield condition, the analytical solution for this hybrid model converges to the Monod equation. This hybrid Logistic-Monod equation represents the cell growth transition from substrate-limiting condition to growth-inhibiting condition, which could be adopted to accurately describe the multi-phases of cell growth and may facilitate kinetic model construction, bioprocess optimization, and scale-up in industrial biotechnology.     

Mycoplasma detection in cell culture by concomitant use of bisbenzamide and fluoresceinated antibody

Summary Conditions are presented for application of both bisbenzamide (Hoechst 33258) stain and a specific fluoresceinated anti-Mycoplasma hyorhinis IgG to a single cell culture preparation. This allows the same field on a slide to be viewed for presumptive diagnosis of any cell culture contaminant mycoplasma by bisbenzamide staining and for definitive diagnosis ofM. hyorhinis strains using fluoresceinated antibody. The use of this method plus a cultural procedure will permit identification of the “noncultivable”M. hyorhinis strain DBS 1050.     

Purification of extensin from cell walls of tomato (hybrid of Lycopersicon esculentum and L. peruvianum) cells in suspension culture

Extensin, a hydroxyproline-rich glycoprotein comprising substantial amounts of -l-arabinose-hydroxyproline glycosidic linkages is believed to be insolubilized in the cell wall during host-pathogen interaction by a peroxidase/hydroperoxide-mediated cross-linking process. Both extensin precursor and extensin peroxidase were ionically eluted from intact water-washed tomato (hybrid) of Lycopersicon esculentum Mill. and L. peruvianum L. (Mill.) cells in suspension cultures and purified to homogeneity by a rapid and simple procedure under mild and non-destructive experimental conditions. The molecular weight of native extensin precursor was estimated to be greater than 240–300 kDa by Superose-12 gel-filtration chromatography. Extensin monomers have previously been designated a molecular weight of approximately 80 kDa. Our results indicate that salt-eluted extensin precursor is not monomeric. Agarose-gel electrophoresis, Superose-12-gel-filtration, extensin-peroxidase-catalysed cross-linking, Mono-S ion-exchange fast protein liquid chromatography (FPLC), and peptide-sequencing data confirmed the homogeneity of the extensin preparation. Evidence that the purified protein was extensin is attributed to the presence of the putative sequence motif — Ser (Hyp)₄ — within the N-terminal end of the protein. Treatment of extensin with trifluoroacetic acid demonstrated that arabinose was the principal carbohydrate. The amino-acid composition of the purified extensin was similar to those reported in the literature. The cross-linking of extensin in vitro upon incubation with extensin peroxidase and exogenous H₂O₂ was characteristic of other reported extensins. Furthermore, Mono-S ion-exchange FPLC of native extensin precursor resolved it into two isoforms, A (90%) and B (10%). The amino-acid compositions of extensin A and extensin B were found to be similar to each other and both extensins were cross-linked in vitro by extensin peroxidase.Abbreviations CM-cellulose carboxymethyl-cellulose - FPLC fast protein liquid chromatography - HF hydrogen fluoride - HRGP hydroxyproline-rich glycoprotein - Hyp hydroxyproline - V_c retention volume - TCA trichloroacetic acid - TFA tri-fluoroacetic acid This work was supported by a A.F.R.C. postdoctoral assistantship to Michael D. Brownleader. We thank Dr. Anthony K. Allen (Department of Biochemistry, Charing Cross and Westminster Hospital, London, UK) for performing the amino-acid analysis and Mrs. Margaret Pickering (Department of Biochemistry, Royal Holloway) for performing the peptide-sequence analysis of extensin. We also express our gratitide to Dr. A. Mort (Oklahoma State University) for performing the HF-deglycosylation of extensin.     

Effects of the sampling design and selection of parameter values on pollen-based quantitative reconstructions of regional vegetation: a case study in southern Sweden using the REVEALS model

The need for quantification of land cover from pollen data has led to the development of a Landscape Reconstruction Algorithm (LRA). The LRA includes several models of which the REVEALS model estimates regional vegetation abundance using pollen assemblages from large sites (lakes or bogs). In this paper we explore the effects of selection and number of pollen samples, and choice of pollen productivity estimates on the REVEALS results. The effect of the size of vegetation surveys is also tested. The results suggest that the differences between two sizes of vegetation surveys have little effect on the model validation. The “characteristic radius” of regional vegetation in southern Sweden was estimated as 200 km. However, the vegetation composition in a 100 × 100 km² square matches well with that estimated by REVEALS. Whether 25, 20 (outliers excluded) or 4 pollen samples are used does not change the REVEALS reconstructions much although the error estimates are larger when outliers are included, and very large when only four samples are used. Therefore validation of the REVEALS model and REVEALS reconstructions of past vegetation can be performed using a limited number of pollen samples, although with caution. The use of many pollen samples from multiple sites is always better whenever possible. REVEALS reconstructions are closer to the actual vegetation when the Danish Pollen Productivity Estimates (PPEs) are used instead of the Swedish PPEs for Cereals, Rumex acetosa/acetosella, Plantago lanceolata and Calluna, indicating that the Danish PPEs are more reliable than the Swedish ones for those taxa. It is recommended to test more than one set of PPEs in validation and applications of the REVEALS model for a better evaluation of the results.