1. CLONEID simplifies collaborations between computational and experimental biologists. Without CLONEID, coordinating experimental data with mathematical modelling often takes weeks per experiment. CLONEID’s workflow can take years of cell imaging data and move it seamlessly into deep learning algorithms and mathematical models. Potential applications include evolutionary steering, lineage tracing, multi-omics, identification of mycoplasma contamination and distinction of dead from live cells.

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2. ALFA-K takes longitudinal single cell sequencing data from an evolving cell population as input, then estimates a local fitness landscape encompassing thousands of karyotypes located near to the input data in karyotype space. This repository contains source code and examples for running ALFA-K, as well as an Agent Based Model (ABM) which simulates evolving cell populations using fitness landscapes estimated by ALFA-K.