scholarly journals In Silico Analysis of Bioethanol Overproduction by Genetically Modified Microorganisms in Coculture Fermentation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Lisha K. Parambil ◽  
Debasis Sarkar
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Genetically Modified ◽  
In Silico Analysis ◽  
Ethanol Productivity ◽  
Microbial Consortia ◽  
Bioethanol Production ◽  
E Coli ◽  
Negative Impacts ◽  
Genome Scale

Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. In this context, use of microbial consortia consisting of substrate-selective microbes is advantageous as it eliminates the negative impacts of glucose catabolite repression. In this study, a detailed in silico analysis of bioethanol production from glucose-xylose mixtures of various compositions by coculture fermentation of xylose-selective Escherichia coli strain ZSC113 and glucose-selective wild-type Saccharomyces cerevisiae is presented. Dynamic flux balance models based on available genome-scale metabolic networks of the microorganisms have been used to analyze bioethanol production and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. A set of genetic engineering strategies for ethanol overproduction by E. coli strain ZSC113 have been evaluated for their efficiency in the context of batch coculture process. Finally, simulations are carried out to determine the pairs of genetically modified E. coli strain ZSC113 and S. cerevisiae that significantly enhance ethanol productivity in batch coculture fermentation.

scholarly journals Two Distinct Modes of Lysis Regulation in Campylobacter Fletchervirus and Firehammervirus Phages

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1247
Author(s):  
Athina Zampara ◽  
Stephen J. Ahern ◽  
Yves Briers ◽  
Lone Brøndsted ◽  
Martine Camilla Holst Sørensen
Keyword(s):  
Signal Peptide ◽  
In Silico ◽  
Protein Domains ◽  
In Silico Analysis ◽  
Cell Lysis ◽  
E Coli ◽  
Sec Pathway ◽  
Gene Functions ◽  
Lytic Genes ◽  
Silico Analysis

Campylobacter phages are divided into two genera; Fletchervirus and Firehammervirus, showing only limited intergenus homology. Here, we aim to identify the lytic genes of both genera using two representative phages (F352 and F379) from our collection. We performed a detailed in silico analysis searching for conserved protein domains and found that the predicted lytic genes are not organized into lysis cassettes but are conserved within each genus. To verify the function of selected lytic genes, the proteins were expressed in E. coli, followed by lytic assays. Our results show that Fletchervirus phages encode a typical signal peptide (SP) endolysin dependent on the Sec-pathway for translocation and a holin for activation. In contrast, Firehammervirus phages encode a novel endolysin that does not belong to currently described endolysin groups. This endolysin also uses the Sec-pathway for translocation but induces lysis of E. coli after overexpression. Interestingly, co-expression of this endolysin with an overlapping gene delayed and limited cell lysis, suggesting that this gene functions as a lysis inhibitor. These results indicate that Firehammervirus phages regulate lysis timing by a yet undescribed mechanism. In conclusion, we found that the two Campylobacter phage genera control lysis by two distinct mechanisms.

scholarly journals Genome‐scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae

2020 ◽  
Vol 21 (4) ◽  
pp. 527-540 ◽  
Author(s):  
Lokanand Koduru ◽  
Hyang Yeon Kim ◽  
Meiyappan Lakshmanan ◽  
Bijayalaxmi Mohanty ◽  
Yi Qing Lee ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Leaf Blight ◽  
Xanthomonas Oryzae ◽  
Metabolic Reconstruction ◽  
Rice Leaf ◽  
Genome Scale ◽  
Silico Analysis

scholarly journals Immune analysis of cry1Ab-genetically modified potato by in-silico analysis and animal model

2017 ◽  
Vol 26 (5) ◽  
pp. 1437-1445 ◽  
Author(s):  
Hassan Rahnama ◽  
Mahdi Nikmard ◽  
Mohsen Abolhasani ◽  
Rahim Osfoori ◽  
Forough Sanjarian ◽  
...  
Keyword(s):  
Animal Model ◽  
In Silico ◽  
Genetically Modified ◽  
In Silico Analysis ◽  
Silico Analysis

scholarly journals Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

2021 ◽  
Author(s):  
Thomas James Moutinho ◽  
Benjamin C Neubert ◽  
Matthew L Jenior ◽  
Jason A. Papin
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Biological Data ◽  
Supporting Evidence ◽  
Phenotypic Data ◽  
E Coli ◽  
Simultaneous Integration ◽  
Improve Model ◽  
K 12 ◽  
Genome Scale

Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial community metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions in order to recapitulate specific growth phenotypes as indicated with associated experimental data. Previous methods have implemented absolute mapping thresholds for the reactions automatically included in draft GENREs; however, there is growing evidence that integrating annotation evidence in a continuous form can improve model accuracy. There is a need for flexibility in the structure of GENREs to better account for uncertainty in biological data, unknown regulatory mechanisms, and context specificity associated with data inputs. To address this issue, we present a novel method that provides a framework for quantifying combined genomic, biochemical, and phenotypic evidence for each biochemical reaction during automated GENRE construction. Our method, Constraint-based Analysis Yielding reaction Usage across metabolic Networks (CANYUNs), generates accurate GENREs with a quantitative metric for the cumulative evidence for each reaction included in the network. The structure of a CANYUN GENRE allows for the simultaneous integration of three data inputs while maintaining all supporting evidence for biochemical reactions that may be active in an organism. CANYUNs is designed to maximize the utility of experimental and annotation datasets and to ultimately assist in the curation of the reference datasets used for the automatic reconstruction of metabolic networks. We validated CANYUNs by generating an E. coli K-12 model and compared it to the manually curated reconstruction iML1515. Finally, we demonstrated the use of CANYUNs to build a model by generating an E. coli Nissle CANYUN GENRE using novel phenotypic data that we collected. This method may address key challenges for the procedural construction of metabolic networks by leveraging uncertainty and redundancy in biological data.

Genome-scale modeling and in silico analysis of mouse cell metabolic network

2009 ◽  
Vol 6 (1) ◽  
pp. 152-161 ◽  
Author(s):  
Suresh Selvarasu ◽  
Iftekhar A. Karimi ◽  
Ghi-Hoon Ghim ◽  
Dong-Yup Lee
Keyword(s):  
Metabolic Network ◽  
In Silico ◽  
In Silico Analysis ◽  
Mouse Cell ◽  
Scale Modeling ◽  
Genome Scale ◽  
Silico Analysis
Sign in / Sign up

Export Citation Format

Share Document