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.
Highlights
Microorganisms serve important roles in diverse areas of fundamental and applied research such as health and sustainability
The framework requires raw quantified analyte data as input, which can be directly parsed from analytical equipment or a laboratory information management system (LIMS)
Quantified raw data are extracted from analytical equipment without significant curation and saved into a spreadsheet
Summary
Microorganisms serve important roles in diverse areas of fundamental and applied research such as health and sustainability. Laboratory throughput has been significantly increased, owing to advanced analytics and automation (Huber et al, 2009; Jacques et al, 2017). These advancements have drastically improved our understanding and ability to engineer biology to solve new challenges (Lee et al, 2012). A microbe's physiology and metabolic state can be assessed, often studied in batch, semi-batch, or chemostat culture. To engineer these microbes, their metabolism is perturbed based on metabolic hypotheses to be tested. Metabolic engineering continues to strive for modular and predictable designs common to other engineering disciplines (Nielsen et al, 2016; Salis et al, 2009; Olson et al, 2014), but the complexity of metabolism imposes significant challenges
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