Designing novel cellulase systems through agent-based modeling and global sensitivity analysis

Screening, Gene Cloning and Expression of Cellulase-Producing Strain Bacillus subtilis Xh-16

Cellulase is a complex enzyme system composed of multiple hydrolytic enzymes. It can degrade cellulose into glucose and improve the utilization efficiency of cellulose resources. Cellulase produced by microorganisms is the main method used in industry, offering the advantages of convenience and being environmentally friendly. A strain Xh-16 with high cellulase production was screened from the rotten rice straw. It was identified as Bacillus subtilis by morphological identification, physiological and biochemical identification, and 16S rRNA gene sequence analysis. Strain Xh-16 was used to degrade rice straw. After a 48 h cellulase treatment, the complete degradation and structural breakdown of the straw were observed. We cloned the endoglucanase Cel5L gene of Bacillus subtilis Xh-16 and induced expression of the cloned gene in Escherichia coli BL21 (DE3). The results showed that the coding length of Cel5L gene was 1500 bp, and the molecular weight of the encoded protein was about 55 kDa. The molecular formula is C2456H3811N671O761S10 and it has 7709 atoms and 499 amino acids. Cel5L differs significantly from some the glycoside hydrolase family 5 cellulases because it has only one carbohydrate binding module family 3 at the C-terminus of its catalytic domain. The cellulase gene Cel5L in Xh-16 can encode active cellulase and can be heterologously expressed in Escherichia coli, which makes Escherichia coli have cellulase function. This study serves as a foundation for further research on cellulase diversity in Bacillus subtilis and offers insights for enhancing cellulase production.

Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework

Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.

High-yield hydrogen production from biomass by in vitro metabolic engineering: Mixed sugars coutilization and kinetic modeling

The use of hydrogen (H2) as a fuel offers enhanced energy conversion efficiency and tremendous potential to decrease greenhouse gas emissions, but producing it in a distributed, carbon-neutral, low-cost manner requires new technologies. Herein we demonstrate the complete conversion of glucose and xylose from plant biomass to H2 and CO2 based on an in vitro synthetic enzymatic pathway. Glucose and xylose were simultaneously converted to H2 with a yield of two H2 per carbon, the maximum possible yield. Parameters of a nonlinear kinetic model were fitted with experimental data using a genetic algorithm, and a global sensitivity analysis was used to identify the enzymes that have the greatest impact on reaction rate and yield. After optimizing enzyme loadings using this model, volumetric H2 productivity was increased 3-fold to 32 mmol H2⋅L(-1)⋅h(-1). The productivity was further enhanced to 54 mmol H2⋅L(-1)⋅h(-1) by increasing reaction temperature, substrate, and enzyme concentrations--an increase of 67-fold compared with the initial studies using this method. The production of hydrogen from locally produced biomass is a promising means to achieve global green energy production.

A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories

Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.