LOICA: Integrating Models with Data for Genetic Network Design Automation

Genetic design automation tools are necessary to expand the scale and complexity of possible synthetic genetic networks. These tools are enabled by abstraction of a hierarchy of standardized components and devices. Abstracted elements must be parametrized from data derived from relevant experiments, and these experiments must be related to the part composition of the abstract components. Here we present Logical Operators for Integrated Cell Algorithms (LOICA), a Python package for designing, modeling, and characterizing genetic networks based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. These models can be parametrized by direct connection to data contained in Flapjack so that abstracted components of designs can characterize themselves. Models can be simulated using continuous or stochastic methods and the data published and managed using Flapjack. LOICA also outputs SBOL3 descriptions and generates graph representations of genetic network designs.

Automated summary

Genetic design automation tools enhance the scale and complexity of synthetic genetic networks by abstracting standardized components and devices. LOICA, a Python package, introduces a simple object-oriented design abstraction for designing, modeling, and characterizing genetic networks. It uses classes to represent biological and experimental components, generating models that can be parametrized with data from Flapjack.