The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Morrell, William; Birkel, Garrett W.; Forrer, Mark; López, Teresa Díaz-Faes; Backman, Tyler W. H.; Dussault, Michael; Kim, Young-Mo; Baidoo, Edward E. K.; Costello, Zak; Ando, David; Alonso-Gutierrez, Jorge; George, Kevin W.; Mukhopadhyay, Aindrila; Vaino, Ian; Keasling, Jay D.; Adams, Paul D.; Hillson, Nathan J.; Martín, Héctor García

doi:10.1021/acssynbio.7b00204

Cited by 49 publications

(44 citation statements)

References 54 publications

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“…35 Supplemental data is available at the Experiment Data Depot (EDD) (https://public-edd.jbei.org/s/eiben-et-al-c5-alcoholproduction/overview/). 36 See the Supporting Information data file for additional information about strain names for archive requests from corresponding author, Addgene, or Molecular Cloud.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Adenosine Triphosphate and Carbon Efficient Route to Second Generation Biofuel Isopentanol

et al. 2020

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Climate change necessitates the development of CO 2 neutral or negative routes to chemicals currently produced from fossil carbon. In this paper we demonstrate a pathway from the renewable resource glucose to next generation biofuel isopentanol by pairing the isovaleryl-CoA biosynthesis pathway from Myxococcus xanthus and a butyryl-CoA reductase from Clostridium acetobutylicum. The best plasmid and Escherichia coli strain combination makes 80.50 ± 8.08 (SD) mg/L of isopentanol after 36 h under microaerobic conditions with an oleyl alcohol overlay. In addition, the system also shows a strong preference for isopentanol production over prenol in microaerobic conditions. Finally, the pathway requires zero adenosine triphosphate and can be paired theoretically with nonoxidative glycolysis, the combination being redox balanced from glucose thus avoiding unnecessary carbon loss as CO 2. These pathway properties make the isovaleryl-CoA pathway an attractive isopentanol production route for further optimization.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Adenosine Triphosphate and Carbon Efficient Route to Second Generation Biofuel Isopentanol

et al. 2020

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“…However, to take full advantage of these technologies, it will be important to facilitate the acquirement, (pre‐)processing, compressing, filing, managing, and evaluation of the resulting complex and heterogeneous datasets. Consequently, further research is needed concerning the establishment of flexible and expandable data warehousing solutions, which should provide convenient software user interfaces, a topic that was put into spotlight again very recently . To this end, research in industrial biotechnology is moving towards an improved knowledge‐based strain and bioprocess design.…”

Section: Future Perspectives For Mbr Systemsmentioning

confidence: 99%

Microbioreactor Systems for Accelerated Bioprocess Development

Hemmerich

Noack

Wiechert

et al. 2018

Biotechnology Journal

138

143

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In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess engineering tools. They provide a unique solution to combine higher experimental throughput with extensive bioprocess monitoring and control, which is indispensable to develop economically and ecologically competitive bioproduction processes. MBR systems are based either on down-scaled stirred tank reactors or on advanced shaken microtiter plate cultivation devices. Importantly, MBR systems make use of optical measurements for non-invasive, online monitoring of important process variables like biomass concentration, dissolved oxygen, pH, and fluorescence. The application range of MBR systems can be further increased by integration into liquid handling robots, enabling automatization and, thus standardization, of various handling and operation procedures. Finally, the tight integration of quantitative strain phenotyping with bioprocess development under industrially relevant conditions greatly increases the probability of finding the right combination of producer strain and bioprocess control strategy. This review will discuss the current state of the art in the field of MBR systems and we can readily conclude that their importance for industrial biotechnology will further increase in the near future.

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“…Recently, a number of efforts have focused on cur-ating experimental metabolic information from published literature (Winkler et al, 2015;Wu et al, 2016). Frameworks like Experimental Data Depot (EDD) (Morrell et al, 2017), LASER (Winkler et al, 2015), and OMERO (Allan et al, 2012) have been developed to standardize documentation and integration of biological experimental information. Frameworks for specific microorganisms have also been developed (Maarleveld et al, 2014).…”

Section: Machine Learning and Knowledge Engineeringmentioning

confidence: 99%

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing

Oyetunde

Bao

Chen

et al. 2018

Biotechnology Advances

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Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development.

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The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Cited by 49 publications

References 54 publications

Adenosine Triphosphate and Carbon Efficient Route to Second Generation Biofuel Isopentanol

Adenosine Triphosphate and Carbon Efficient Route to Second Generation Biofuel Isopentanol

Microbioreactor Systems for Accelerated Bioprocess Development

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing

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