Evidence map›Paper›PMID 38293045›Full record

ArticlebioRxiv : the preprint server for biology2024

Resource-explicit interactions in spatial population models.

Samuel E Champer, Bryan Chae, Benjamin C Haller, Jackson Champer, Philipp W Messer

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Samuel E ChamperDepartment of Computational Biology, Cornell University, Ithaca, NY 14853.ORCID 0000-0002-4559-7627
Bryan ChaeDepartment of Computational Biology, Cornell University, Ithaca, NY 14853.
Benjamin C HallerDepartment of Computational Biology, Cornell University, Ithaca, NY 14853.ORCID 0000-0002-9787-1579
Jackson ChamperCenter for Bioinformatics, School of Life Sciences, Center for Life Sciences, Peking University, Beijing, China 100871.ORCID 0000-0002-3814-3774
Philipp W MesserDepartment of Computational Biology, Cornell University, Ithaca, NY 14853.

Funding

Scaling up computational genomics with tree sequencesR01HG012473 · NHGRI · UNIVERSITY OF OREGON · PI PETER Lochhead RALPH · 2023 to 2026
$2.3M
Evolutionary dynamics of CRISPR gene drives in natural populationsR01GM127418 · NIGMS · CORNELL UNIVERSITY · PI MESSER, PHILIPP W · 2018 to 2022
$1.6M
Population genetics of rapid evolutionary processesR35GM152242 · NIGMS · CORNELL UNIVERSITY · PI Philipp W Messer · 2024 to 2026
$1.2M
NHGRI NIH HHS R01 HG012473NIGMS NIH HHS R01 GM127418NIGMS NIH HHS R35 GM152242
6 · The paper itself

Abstract

Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.

Identifiers

PMID38293045
PMCPMC10827080

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

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.