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. 2019 Apr 4:9:e00089.
doi: 10.1016/j.mec.2019.e00089. eCollection 2019 Dec.

Impact framework: A python package for writing data analysis workflows to interpret microbial physiology

Naveen Venayak  1 , Kaushik Raj  1 , Radhakrishnan Mahadevan  1   2
Affiliations

Impact framework: A python package for writing data analysis workflows to interpret microbial physiology

Naveen Venayak et al. Metab Eng Commun. .

Erratum in

Abstract

Microorganisms can be genetically engineered to solve a range of challenges in diverse including health, environmental protection and sustainability. The natural complexity of biological systems makes this an iterative cycle, perturbing metabolism and making stepwise progress toward a desired phenotype through four major stages: design, build, test, and data interpretation. This cycle has been accelerated by advances in molecular biology (e.g. robust DNA synthesis and assembly techniques), liquid handling automation and scale-down characterization platforms, generating large heterogeneous data sets. Here, we present an extensible Python package for scientists and engineers working with large biological data sets to interpret, model, and visualize data: the IMPACT (Integrated Microbial Physiology: Analysis, Characterization and Translation) framework. Impact aims to ease the development of Python-based data analysis workflows for a range of stakeholders in the bioengineering process, offering open-source tools for data analysis, physiology characterization and translation to visualization. Using this framework, biologists and engineers can opt for reproducible and extensible programmatic data analysis workflows, mediating a bottleneck limiting the throughput of microbial engineering. The Impact framework is available at https://github.com/lmse/impact.

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Figures

Fig. 1
Fig. 1
The software tool stack that depicts the tools available for various users to access and handle experimental data. Users with varying level of coding expertise can employ the Impact framework using different interfaces to read and analyze their data before storing it in a database.
Fig. 2
Fig. 2
Overview of the data flow, from raw data to visualization, in the Impact framework.
Fig. 3
Fig. 3
Overview of key elements in the design, built, test, learn cycle of metabolic engineering.
Fig. 4
Fig. 4
Sample data visualization for a metabolic engineering problem generated with the Impact framework - (a) time course data (b) titer (c) yield. Data adapted from Nemr et al., 2018 where the aim was to develop a platform strain to produce 1,3-butanediol. Such a comparison of yields, titers, and productivities of different microbial strains could help scientists decide on appropriate intervention strategies to improve the metric of their choice. For example, the data analysis by the Impact framework seems to suggest that “strain_E” with the plasmid “pBD_3” has the highest end point titer of 1,3-butanediol (shown in panel ‘b’) while having a significantly lower yield than other strains as seen in panel ‘c’. The Impact framework can perform this analysis and visualize the data within a few seconds while analysis of the data, calculation of statistical information and plotting the processed data manually might take several hours.
Fig. 5
Fig. 5
Sample data visualization generated with Escher with data from the Impact framework.
Fig. 6
Fig. 6
Carbon balance using an anaerobic E. coli simulation with iJO1366. for: formate, ac: acetate, etoh: ethanol, succ: succinate.
See this image and copyright information in PMC

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