Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Stress modulation as a means to improve yeasts for lignocellulose bioconversion

The second-generation (2G) fermentation environment for lignocellulose conversion presents unique challenges to the fermentative organism that do not necessarily exist in other industrial fermentations. While extreme osmotic, heat, and nutrient starvation stresses are observed in sugar- and starch-based fermentation environments, additional pre-treatment-derived inhibitor stress, potentially exacerbated by stresses such as pH and product tolerance, exist in the 2G environment. Furthermore, in a consolidated bioprocessing (CBP) context, the organism is also challenged to secrete enzymes that may themselves lead to unfolded protein response and other stresses. This review will discuss responses of the yeast Saccharomyces cerevisiae to 2G-specific stresses and stress modulation strategies that can be followed to improve yeasts for this application. We also explore published –omics data and discuss relevant rational engineering, reverse engineering, and adaptation strategies, with the view of identifying genes or alleles that will make positive contributions to the overall robustness of 2G industrial strains

Kinetics and thermodynamic analysis in one-pot pyrolysis of rice hull using renewable calcium oxide based catalysts

Thermodynamic and kinetic parameters of catalytic pyrolysis of rice hull (RH) pyrolysis using two different types of renewable catalysts namely natural limestone (LS) and eggshells (ES) using thermogravimetric analysis (TG) approach at different heating rates of 10–100 K min-1in temperature range of 323–1173 K are investigated. Catalytic pyrolysis mechanism of both catalysts had shown significant effect on the degradation of RH. Model free kinetic of iso-conversional method (Flynn-Wall-Ozawa) and multi-step reaction model (Distributed Activation Energy Model) were employed into present study. The average activation energy was found in the range of 175.4–177.7 kJ mol-1(RH), 123.3–132.5 kJ mol-1(RH-LS), and 96.1–100.4 kJ mol-1(RH-ES) respectively. The syngas composition had increased from 60.05 wt% to 63.1 wt% (RH-LS) and 63.4 wt% (RH-ES). However, the CO2content had decreased from 24.1 wt% (RH) to 20.8 wt% (RH-LS) and 19.9 wt% (RH-ES)

Thermogravimetric kinetic modelling of in-situ catalytic pyrolytic conversion of rice husk to bioenergy using rice hull ash catalyst

The thermal degradation behaviour and kinetic parameter of non-catalytic and catalytic pyrolysis of rice husk (RH) using rice hull ash (RHA) as catalyst were investigated using thermogravimetric analysis at four different heating rates of 10, 20, 50 and 100 K/min. Four different iso conversional kinetic models such as Kissinger, Friedman, Kissinger-Akahira-Sunose (KAS) and Ozawa-Flynn-Wall (OFW) were applied in this study to calculate the activation energy (EA) and pre-exponential value (A) of the system. The EAof non-catalytic and catalytic pyrolysis was found to be in the range of 152–190 kJ/mol and 146–153 kJ/mol, respectively. The results showed that the catalytic pyrolysis of RH had resulted in a lower EAas compared to non-catalytic pyrolysis of RH and other biomass in literature. Furthermore, the high Gibb's free energy obtained in RH implied that it has the potential to serve as a source of bioenergy production

An In-Situ Thermogravimetric Study of Pyrolysis of Rice Hull with Alkali Catalyst of CaCO<inf>3</inf>

© Published under licence by IOP Publishing Ltd. Pyrolysis of rice hull (RH) with the presence of CaCO3 catalyst was carried out in this study to understand the effect of alkali catalyst in the thermal degradation behaviour and evaluate the kinetic parameter of rice hull for bio-oil or syngas production. Five different heating rates of the pyrolysis experiments at 10, 20, 30, 50, and 100 Kmin-1 were carried out in thermogravimetric analysis (TGA) equipment. Model fitting kinetic Coats Redfern integral method was applied in this study to estimate the activation energy (EA) and pre-exponential (A) value of catalytic pyrolysis in RH. The results showed that the maximum degradation increased from 6.69 to 52.67 wt% min-1 as heating rates increases from 10 to 100 Kmin-1. Besides that, the EA of the catalytic pyrolysis for RH using CaCO3 catalyst 60.86 kJmol-1 which is lower than other similar pyrolysis reaction reported in literature i.e. 77.4 kJ/mol. Meanwhile, the A value for the catalytic pyrolysis for RH using CaCO3 catalyst was 4.68×1010 min-1 which is significantly higher than 1.1×106 min-1 as reported in literature for non-catalytic pyrolysis of rice husk

Thermogravimetric kinetic analysis of in-situ catalytic pyrolysis of palm oil wastes with the presence of palm oil wastes ash catalyst

The thermal degradation and kinetic analysis for oil palm frond (OPF) and oil palm trunk (OPT) with its ashes were investigated using thermogravimetric approach (TGA). OPF ash, OPT ash and its mixtures are used as a natural source of catalysts in the pyrolytic conversion of the palm oil wastes to bioenergy. The TGA experiments were conducted in a range of heating rates of 10-100 K/min from the temperature of 323 K to 1173 K. Coats-Redfern model is applied in this study to evaluate the activation energy (EA) and pre-exponential factor (A). The average EA values ranged 24.27-32.36 kJ.mol−1 and 41.42-46.10 kJ.mol−1 for pyrolysis of OPF and OPT respectively. Meanwhile, the average EA values ranged 24.27-31.06 kJ.mol−1 and 31.77-43.45 kJ.mol−1 for catalytic pyrolysis of OPF and OPT respectively