ArticlePLoS computational biology2019
Competing evolutionary paths in growing populations with applications to multidrug resistance.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- BESTish: A Diffusion-Approximation Framework for Inferring Selection and Mutation in Clonal Hematopoiesis.bioRxiv : the preprint server for biology · 2026Article
- Accumulation of Oncogenic Mutations During Progression from Healthy Tissue to Cancer.Bulletin of mathematical biology · 2024Article
- A general multi-scale description of metastable adaptive motion across fitness valleys.Journal of mathematical biology · 2024Article
- Pathogen non-planktonic phases within the urinary tract impact early infection and resistance evolution.The ISME journal · 2024Article
- Sequential mutations in exponentially growing populations.PLoS computational biology · 2023Article
- Bottlenecks can constrain and channel evolutionary paths.Genetics · 2023Article
- The role of memory in non-genetic inheritance and its impact on cancer treatment resistance.PLoS computational biology · 2021Article
- Pharmacokinetic Profiles Determine Optimal Combination Treatment Schedules in Computational Models of Drug Resistance.Cancer research · 2020Article
- Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications.PLoS computational biology · 2020Article
- Cancer recurrence times from a branching process model.PLoS computational biology · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Investigating the emergence of a particular cell type is a recurring theme in models of growing cellular populations. The evolution of resistance to therapy is a classic example. Common questions are: when does the cell type first occur, and via which sequence of steps is it most likely to emerge? For growing populations, these questions can be formulated in a general framework of branching processes spreading through a graph from a root to a target vertex. Cells have a particular fitness value on each vertex and can transition along edges at specific rates. Vertices represent cell states, say genotypes or physical locations, while possible transitions are acquiring a mutation or cell migration. We focus on the setting where cells at the root vertex have the highest fitness and transition rates are small. Simple formulas are derived for the time to reach the target vertex and for the probability that it is reached along a given path in the graph. We demonstrate our results on several scenarios relevant to the emergence of drug resistance, including: the orderings of resistance-conferring mutations in bacteria and the impact of imperfect drug penetration in cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.