Abstract
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
| Original language | English (US) |
|---|---|
| Article number | e1011937 |
| Journal | PLoS computational biology |
| Volume | 20 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2024 |
Funding
This work was supported by - The NIH/ NIGMS, Genomics of rapid adaptation in the lab and in the wild R35GM11816506 (MIRA grant, to DP) - The NIH, Unravelling mechanisms of tumor suppression in lung cancer (R01CA23434903, to DP) - The NIH (PQ4), Quantitative and multiplexed analysis of gene function in cancer in vivo (R01CA23125303, to DP) - The NIH, Genetic Determinants of Tumor Growth and Drug Sensitivity in EGFR Mutant Lung Cancer (R01CA263715, to DP) - The NIH, Dissecting the interplay between aging, genotype, and the microenvironment in lung cancer (U01AG077922, to DP) - The NIH, Genetic dissection of oncogenic Kras signaling (R01CA230025, to DP) - The National Science Foundation-Simons Center for Quantitative Biology at Northwestern University and the Simons Foundation grant (597491, to MM) - The Chan Zuckerberg Initiative, an advised fund of Silicon Valley Community Foundation (DAF2023-329587, to MM) - MRM was supported by the Schmidt Science Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank Griffin Chure and Michael Betancourt for their helpful advice and discussion. We would like to thank Karna Gowda, Spencer Farrell, and Shaili Mathur for critical observations on the manuscript. We are especially thankful to Grant Kinsler for kindly providing raw experimental data as well as lengthy discussions about the state-of-the-art inference method.
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Modeling and Simulation
- Ecology
- Molecular Biology
- Genetics
- Cellular and Molecular Neuroscience
- Computational Theory and Mathematics