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Record W4307395132 · doi:10.1016/j.isci.2022.105421

Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies

2022· review· en· W4307395132 on OpenAlexfundno aff
Andrew Millar‐Wilson, Órla Ward, Eolann Duffy, Gary Hardiman

Bibliographic record

VenueiScience · 2022
Typereview
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesQueen's UniversityNational Institutes of HealthEuropean Society of AnaesthesiologyNational Institute on Drug AbuseQueen's University BelfastEuropean Space AgencyNational Aeronautics and Space Administration
KeywordsSpaceflightSystems biologyModelling biological systemsWeightlessnessComponent (thermodynamics)BiologyComputer scienceBiochemical engineeringComputational biologyNeuroscienceEngineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

A central tenet of systems biology is that biological systems are greater than the sum of their component parts. Spaceflight is associated with hazards including radiation exposure and microgravity which impact different echelons of biological organizations spanning molecular, cellular, organ, and organismal levels. These insults lead to physical damage, including muscle and bone loss, neurological damage, and impaired immunity. Mitochondrial dysfunction and biological alterations occurring during spaceflight have been reported. The health challenges presented by long-term space travel must be addressed and appropriate countermeasures developed to protect astronauts. Increasing quantity of multiomics data are being generated from cells and model organisms flown in space, with physiological data from astronauts. Systems biology approaches leveraging mathematical reasoning and computational modeling are required to characterize these components in a holistic fashion. In this review, we provide an historic perspective on multiscale biological systems modeling, followed by a discussion on its utility for spaceflight biology research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.233
GPT teacher head0.445
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2022
Admission routes1
Has abstractyes

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