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Branching and fragmenting particle systems in biology

Abstract:

We introduce and study two new classes of branching and fragmenting particle systems in biology. The first class of branching particle systems with interactions model spatially-structured populations with density-dependent birth and death mechanisms. Our spatial model describes spatially heterogeneous populations using point measures, considering birth and death rates based on spatial position and local population density. Notably, our model includes a juvenile phase, where offspring mature with a density-dependent probability. This results in novel scaling limits: an interacting superprocess, a nonlocal partial differential equation (PDE), and a classical PDE (in which the diffusion term is nonlinear). Furthermore, using a lookdown representation, we retain information about genealogies and, in the case of deterministic limiting models, use this to deduce the backwards in time motion of the ancestral lineage of a sampled individual. We observe that knowing the history of the population density is not enough to determine the motion of ancestral lineages in our model.

In our second class of models, motivated by using cell-free DNA (cfDNA) as a biomarker, we capture three mechanisms governing cfDNA fragmentation in the bloodstream. Our Markovian model, FRIME (Fragmentation, Immigration, and Exit), allows the simulation of cfDNA fragment profiles by sampling from its stationary distribution. We validate the model by comparing simulated profiles with mitochondrial and genomic cfDNA fragments, finding consistency with experimental observations. We employ probabilistic tools and the theory of fragmentation processes to establish the first moments of FRIME processes mathematically. This simulation framework improves our computational understanding of DNA fragmentation dynamics in the bloodstream and has the potential to underpin tools for the analysis of liquid biopsy data.

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author

Contributors

Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Supervisor
ORCID:
0000-0003-3669-8423


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Funder identifier:
https://ror.org/018mejw64
Grant:
EXC-2047/1 – 390685813
Programme:
Hausdorff Institute of Mathematics Junior Trimester Programme 2022
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Funder identifier:
https://ror.org/04v48nr57
Grant:
Rhodes Scholarship
Programme:
Rhodes Scholarship
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Funder identifier:
https://ror.org/0439y7842
Grant:
EP/S023925/1
Programme:
Mathematics of Random System CDT


DOI:
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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