Quantitative Assessment of Biological Dynamics with Aggregate Data.

Stephen McCoy, Daniel McBride, D Katie McCullough, Benjamin C Calfee, Erik Zinser, David Talmy, Ioannis Sgouralis

Abstract read

In one paragraph

Article in Bulletin of mathematical biology, 2025. 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

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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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Stephen McCoyDepartment of Mathematics, University of Tennessee Knoxville, Knoxville, TN, USA.

Daniel McBrideDepartment of Mathematics, University of Tennessee Knoxville, Knoxville, TN, USA.

D Katie McCulloughDepartment of Microbiology, University of Tennessee Knoxville, Knoxville, TN, USA.

Benjamin C CalfeeDepartment of Microbiology, University of Tennessee Knoxville, Knoxville, TN, USA.

Erik ZinserDepartment of Microbiology, University of Tennessee Knoxville, Knoxville, TN, USA.

David TalmyDepartment of Microbiology, University of Tennessee Knoxville, Knoxville, TN, USA.

Ioannis SgouralisDepartment of Mathematics, University of Tennessee Knoxville, Knoxville, TN, USA. isgoural@utk.edu.ORCID http://orcid.org/0000-0001-7858-0197

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop and apply a learning framework for parameter estimation in initial value problems that are assessed only indirectly via aggregate data such as sample means and/or standard deviations. Our comprehensive framework follows Bayesian principles and consists of specialized Markov chain Monte Carlo computational schemes that rely on modified Hamiltonian Monte Carlo to align with constraints induced by summary statistics and a novel elliptical slice sampler adapted to the parameters of biological models. We benchmark our methods with synthetic data on microbial growth in batch culture and test them with real growth curve data from laboratory replication experiments on Prochlorococcus microbes. The results indicate that our learning framework can utilize experimental or historical data and lead to robust parameter estimation and data assimilation in ODE models that outperform least-squares fitting.

Indexed as

Models, BiologicalBayes TheoremComputer SimulationLeast-Squares AnalysisMarkov ChainsMathematical ConceptsMonte Carlo MethodProchlorococcusBatch cultureDynamical systemsGrowth curveHamiltonian Monte CarloProchlorococcusStatistical learning

Identifiers

PMID41094342

DOI10.1007/s11538-025-01534-x

PMCPMC12528303

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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.