scholarly journals In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products

2021 ◽  
Vol 12 ◽  
Author(s):  
Albert Enrique Tafur Rangel ◽  
Wendy Ríos ◽  
Daisy Mejía ◽  
Carmen Ojeda ◽  
Ross Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
In Silico ◽  
Industrial Biotechnology ◽  
Cell Factory ◽  
Cell Factories ◽  
Transcriptomics Data ◽  
Valuable Compounds ◽  
By Products

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout’s (feature’s) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

scholarly journals In Silico Design for Systems-Based Metabolic Engineering In Escherichia Coli for The Bioconversion of Aerobic Glycerol to Succinic Acid

2020 ◽  
Author(s):  
Albert Enrique Tafur Rangel ◽  
Wendy Lorena Rios Guzman ◽  
Carmen Elvira Ojeda Cuella ◽  
Daissy Esther Mejia Perez ◽  
Ross Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
In Silico ◽  
Rational Design ◽  
Industrial Biotechnology ◽  
E Coli ◽  
Cell Factories ◽  
Transcriptomics Data

Abstract BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 220
Author(s):  
Wubliker Dessie ◽  
Zongcheng Wang ◽  
Xiaofang Luo ◽  
Meifeng Wang ◽  
Zuodong Qin
Keyword(s):  
Succinic Acid ◽  
In Silico ◽  
Value Added ◽  
Research Progress ◽  
Efficient Production ◽  
Actinobacillus Succinogenes ◽  
Cell Factories ◽  
In Silico Studies ◽  
Current Scenario ◽  
Critical Problems

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

scholarly journals Organic Wastes as Feedstocks for Non-Conventional Yeast-Based Bioprocesses

2019 ◽  
Vol 7 (8) ◽  
pp. 229 ◽  
Author(s):  
Diem T. Hoang Do ◽  
Chrispian W. Theron ◽  
Patrick Fickers
Keyword(s):  
Carbon Sources ◽  
Value Added ◽  
Building Blocks ◽  
Organic Wastes ◽  
Cell Factory ◽  
Microbial Cell Factories ◽  
Cell Factories ◽  
Alternative Feedstocks ◽  
Global Population ◽  
By Products

Non-conventional yeasts are efficient cell factories for the synthesis of value-added compounds such as recombinant proteins, intracellular metabolites, and/or metabolic by-products. Most bioprocess, however, are still designed to use pure, ideal sugars, especially glucose. In the quest for the development of more sustainable processes amid concerns over the future availability of resources for the ever-growing global population, the utilization of organic wastes or industrial by-products as feedstocks to support cell growth is a crucial approach. Indeed, vast amounts of industrial and commercial waste simultaneously represent an environmental burden and an important reservoir for recyclable or reusable material. These alternative feedstocks can provide microbial cell factories with the required metabolic building blocks and energy to synthesize value-added compounds, further representing a potential means of reduction of process costs as well. This review highlights recent strategies in this regard, encompassing knowledge on catabolic pathways and metabolic engineering solutions developed to endow cells with the required metabolic capabilities, and the connection of these to the synthesis of value-added compounds. This review focuses primarily, but not exclusively, on Yarrowia lipolytica as a yeast cell factory, owing to its broad range of naturally metabolizable carbon sources, together with its popularity as a non-conventional yeast.

Direct carbon capture for production of high-performance biodegradable plastic by cyanobacterial cell factory

2021 ◽  
Author(s):  
Chunlin Tan ◽  
Fei Tao ◽  
Ping Xu
Keyword(s):  
Metabolic Engineering ◽  
Carbon Capture ◽  
De Novo ◽  
Pha Synthase ◽  
Cell Factory ◽  
Cyanobacterial Cell ◽  
Plastic Pollution ◽  
Cell Factories ◽  
Coa Transferase ◽  
Dry Cell Weight

Plastic pollution has become one of the most pressing environmental issues today, leading to an urgent need to develop biodegradable plastics1-3. Polylactic acid (PLA) is one of the most promising biodegradable materials because of its potential applications in disposable packaging, agriculture, medicine, and printing filaments for 3D printers4-6. However, current biosynthesis of PLA entirely uses edible biomass as feedstock, which leads to competition for resources between material production and food supply7,8. Meanwhile, excessive emission of CO2 that is the most abundant carbon source aggravates global warming, and climate instability. Herein, we first developed a cyanobacterial cell factory for the de novo biosynthesis of PLA directly from CO2, using a combinational strategy of metabolic engineering and high-density cultivation (HDC). Firstly, the heterologous pathway for PLA production, which involves engineered D-lactic dehydrogenase (LDH), propionate CoA-transferase (PCT), and polyhydroxyalkanoate (PHA) synthase, was introduced into Synechococcus elongatus PCC7942. Subsequently, different metabolic engineering strategies, including pathway debottlenecking, acetyl-CoA self-circulation, and carbon-flux redirection, were systematically applied, resulting in approximately 19-fold increase to 15 mg/g dry cell weight (DCW) PLA compared to the control. In addition, HDC increased cell density by 10-fold. Finally, the PLA titer of 108 mg/L (corresponding to 23 mg/g DCW) was obtained, approximately 270 times higher than that obtained from the initially constructed strain. Moreover, molecular weight (Mw, 62.5 kDa; Mn, 32.8 kDa) of PLA produced by this strategy was among the highest reported levels. This study sheds a bright light on the prospects of plastic production from CO2 using cyanobacterial cell factories.

In Silico Machine Learning Methods in Drug Development

2014 ◽  
Vol 14 (16) ◽  
pp. 1913-1922 ◽  
Author(s):  
Dimitar Dobchev ◽  
Girinath Pillai ◽  
Mati Karelson
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
In Silico ◽  
Learning Methods ◽  
Machine Learning Methods

In Silico Study of 1, 4 Alpha Glucan Branching Enzyme and Substrate Docking Studies

2020 ◽  
Vol 17 (1) ◽  
pp. 40-50
Author(s):  
Farzane Kargar ◽  
Amir Savardashtaki ◽  
Mojtaba Mortazavi ◽  
Masoud Torkzadeh Mahani ◽  
Ali Mohammad Amani ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
In Silico ◽  
Alpha Helix ◽  
In Silico Analysis ◽  
Glycogen Storage ◽  
Docking Studies ◽  
Random Coil ◽  
Special Topic ◽  
Industrial Biotechnology ◽  
In Silico Study

Background: The 1,4-alpha-glucan branching protein (GlgB) plays an important role in the glycogen biosynthesis and the deficiency in this enzyme has resulted in Glycogen storage disease and accumulation of an amylopectin-like polysaccharide. Consequently, this enzyme was considered a special topic in clinical and biotechnological research. One of the newly introduced GlgB belongs to the Neisseria sp. HMSC071A01 (Ref.Seq. WP_049335546). For in silico analysis, the 3D molecular modeling of this enzyme was conducted in the I-TASSER web server. Methods: For a better evaluation, the important characteristics of this enzyme such as functional properties, metabolic pathway and activity were investigated in the TargetP software. Additionally, the phylogenetic tree and secondary structure of this enzyme were studied by Mafft and Prabi software, respectively. Finally, the binding site properties (the maltoheptaose as substrate) were studied using the AutoDock Vina. Results: By drawing the phylogenetic tree, the closest species were the taxonomic group of Betaproteobacteria. The results showed that the structure of this enzyme had 34.45% of the alpha helix and 45.45% of the random coil. Our analysis predicted that this enzyme has a potential signal peptide in the protein sequence. Conclusion: By these analyses, a new understanding was developed related to the sequence and structure of this enzyme. Our findings can further be used in some fields of clinical and industrial biotechnology.

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