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

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.