scholarly journals OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

2021 ◽  
Author(s):  
Shouyong Jiang
Keyword(s):  
Metabolic Model ◽  
Strain Engineering ◽  
Design Strategies ◽  
E Coli ◽  
Success Stories ◽  
Strain Design ◽  
Optimality Principles ◽  
Biochemical Production ◽  
Genome Scale ◽  
Flux Difference

Computational tools have been widely adopted for strain optimisation in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximisation of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout) leading to high biochemical production. The usefulness 1and capabilities of OptDesign are demonstrated for the production of three biochemicals in E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.

scholarly journals Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Model ◽  
E Coli ◽  
Model Driven ◽  
Metabolic Characteristics ◽  
Strain Design ◽  
Renewable Feedstocks ◽  
Coa Ligase ◽  
Coa Transferase ◽  
Genome Scale

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

scholarly journals Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Model ◽  
E Coli ◽  
Model Driven ◽  
Metabolic Characteristics ◽  
Strain Design ◽  
Renewable Feedstocks ◽  
Coa Ligase ◽  
Coa Transferase ◽  
Genome Scale

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

scholarly journals Genome-Scale reconstruction ofPaenarthrobacter aurescensTC1 metabolic model towards the study of atrazine bioremediation

2019 ◽  
Author(s):  
Shany Ofaim ◽  
Raphy Zarecki ◽  
Seema Porob ◽  
Daniella Gat ◽  
Tamar Lahav ◽  
...  
Keyword(s):  
Environmental Concern ◽  
Subsequent Development ◽  
Biomass Accumulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Strain Design ◽  
A Genome ◽  
Genome Scale ◽  
Atrazine Degradation

ABSTRACTAtrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as anin silicotool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model,iRZ960, forPaenarthrobacter aurescensTC1 – a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence ofP. aurescensTC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verifiedin vitro. Experimental validations support the reliability of the model’s predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

scholarly journals Predicting Strain Engineering Strategies Using iKS1317: A Genome‐Scale Metabolic Model of Streptomyces coelicolor

2019 ◽  
Vol 14 (4) ◽  
pp. 1800180 ◽  
Author(s):  
Tjaša Kumelj ◽  
Snorre Sulheim ◽  
Alexander Wentzel ◽  
Eivind Almaas
Keyword(s):  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Strain Engineering ◽  
A Genome ◽  
Genome Scale

scholarly journals Genome-Scale Metabolic Model of Actinosynnema pretiosum ATCC 31280 and Its Application for Ansamitocin P-3 Production Improvement

Genes ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 364 ◽  
Author(s):  
Jian Li ◽  
Renliang Sun ◽  
Xinjuan Ning ◽  
Xinran Wang ◽  
Zhuo Wang
Keyword(s):  
Time Course ◽  
Antitumor Agent ◽  
Metabolic Model ◽  
High Yield ◽  
Strain Design ◽  
Production Improvement ◽  
Actinosynnema Pretiosum ◽  
Growth Activation ◽  
Potential Strategy ◽  
Genome Scale

Actinosynnema pretiosum ATCC 31280 is the producer of antitumor agent ansamitocin P-3 (AP-3). Understanding of the AP-3 biosynthetic pathway and the whole metabolic network in A. pretiosum is important for the improvement of AP-3 titer. In this study, we reconstructed the first complete Genome-Scale Metabolic Model (GSMM) Aspm1282 for A. pretiosum ATCC 31280 based on the newly sequenced genome, with 87% reactions having definite functional annotation. The model has been validated by effectively predicting growth and the key genes for AP-3 biosynthesis. Then we built condition-specific models for an AP-3 high-yield mutant NXJ-24 by integrating Aspm1282 model with time-course transcriptome data. The changes of flux distribution reflect the metabolic shift from growth-related pathway to secondary metabolism pathway since the second day of cultivation. The AP-3 and methionine metabolisms were both enriched in active flux for the last two days, which uncovered the relationships among cell growth, activation of methionine metabolism, and the biosynthesis of AP-3. Furthermore, we identified four combinatorial gene modifications for overproducing AP-3 by in silico strain design, which improved the theoretical flux of AP-3 biosynthesis from 0.201 to 0.372 mmol/gDW/h. Upregulation of methionine metabolic pathway is a potential strategy to improve the production of AP-3.

scholarly journals A genome-scale metabolic model forMethylococcus capsulatuspredicts reduced efficiency uphill electron transfer to pMMO

2018 ◽  
Author(s):  
Christian Lieven ◽  
Leander A. H. Petersen ◽  
Sten Bay Jørgensen ◽  
Krist V. Gernaey ◽  
Markus J. Herrgard ◽  
...  
Keyword(s):  
Electron Transfer ◽  
Metabolic Model ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Methylococcus Capsulatus ◽  
Design Strategies ◽  
C1 Metabolism ◽  
Oxidation Of Methane ◽  
A Genome ◽  
Genome Scale

AbstractBackgroundGenome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modificationsin silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts likeE. coliandS. cerevisiae, few such models exist for one-carbon (C1) metabolizers.ResultsHere we present a genome-scale metabolic model forMethylococcus capsulatus, a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 877 metabolites connected via 898 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base forM. capsulatus. With it, we determined that oxidation of methane by the particulate methane monooxygenase is most likely driven through uphill electron transfer operating at reduced efficiency as this scenario matches best with experimental data from literature.ConclusionsThe metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies towards improved SCP production processes inM. capsulatus.

scholarly journals Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

2019 ◽  
Author(s):  
Sara A. Amin ◽  
Elizabeth Chavez ◽  
Nikhil U. Nair ◽  
Soha Hassoun
Keyword(s):  
Metabolic Model ◽  
Enzyme Promiscuity ◽  
Metabolomics Data ◽  
Structural Modifications ◽  
E Coli ◽  
Cellular Metabolic Activity ◽  
Metabolic Models ◽  
Genome Scale ◽  
Primary Substrate ◽  
Model Annotation

AbstractBackgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous – i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.ResultsOur workflow utilizes PROXIMAL – a tool that uses reactant-product transformation patterns from the KEGG database – to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).ConclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but have not been documented in iML1515 or other databases. Among these, we found that 17 metabolites have previously been documented in E. coli metabolomics studies. Further, 6 of these metabolites are not documented for any other E. coli metabolic model (e.g. KEGG, EcoCyc). The corresponding reactions should be added to iML1515 to create an Extended Metabolic Model (EMM). Other predicted metabolites and reactions can guide future experimental metabolomics studies. Further, our workflow can easily be applied to other organisms for which comprehensive genome-scale metabolic models are desirable.

scholarly journals Transcriptome and translatome of CO2 fixing acetogens under heterotrophic and autotrophic conditions

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yoseb Song ◽  
Jiyun Bae ◽  
Jongoh Shin ◽  
Sangrak Jin ◽  
Jung-Kul Lee ◽  
...  
Keyword(s):  
Translational Regulation ◽  
Anaerobic Bacteria ◽  
Regulatory Elements ◽  
Ribosome Profiling ◽  
Strain Design ◽  
Thermochemical Processes ◽  
Systematic Understanding ◽  
Growth Limits ◽  
Biochemical Production ◽  
Genome Scale

AbstractAcetogens are anaerobic bacteria that utilise gaseous feedstocks such as carbon monoxide (CO) and carbon dioxide (CO2) to synthesise biomass and various metabolites via the energetically efficient Wood-Ljungdahl pathway. Because of this pathway, acetogens have been considered as a novel platform to produce biochemicals from gaseous feedstocks, potentially replacing the conventional thermochemical processes. Despite their advantages, a lack of systematic understanding of the transcriptional and translational regulation in acetogens during autotrophic growth limits the rational strain design to produce the desired products. To overcome this problem, we presented RNA sequencing and ribosome profiling data of four acetogens cultivated under heterotrophic and autotrophic conditions, providing data on genome-scale transcriptional and translational responses of acetogens during CO2 fixation. These data facilitate the discovery of regulatory elements embedded in their genomes, which could be utilised to engineer strains to achieve better growth and productivity. We anticipate that these data will expand our understanding of the processes of CO2 fixation and will help in the designing of strains for the desired biochemical production.

Sign in / Sign up

Export Citation Format

Share Document