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Record W2904166393 · doi:10.1101/477703

A Multi-Scale Modelling Framework of the Whole Human Body Based on Multi-Objective Functions

2018· preprint· en· W2904166393 on OpenAlexaff
Masood Khaksar, Will Cluett, Radhakrishnan Mahadevan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOdeOrdinary differential equationComputer scienceScale (ratio)Mathematical optimizationConvergence (economics)Constraint (computer-aided design)MathematicsDifferential equationApplied mathematicsPhysics

Abstract

fetched live from OpenAlex

Constraint-based multi-scale metabolic models are powerful tools to study complex biological systems like the human body, and to develop new treatment strategies for human diseases. They capture different scales of the system under study and provide the opportunity of understanding the interaction between multiple scales. In this paper, we have used our previously developed multi-scale whole body metabolism framework to establish a new approach to include multiple objectives of the human cells in computational analysis. The model has 3555 ordinary differential equations (ODEs) integrated with a genome-scale model of the hepatocyte, including 1826 biochemical reactions. The model has been solved for 74 objective functions simultaneously. Simulation results show that the integration algorithm has promise with respect to convergence, computational efficiency and response to perturbation. The results suggest that this method holds significant potential for the simulation of the range of metabolic phenotypes for mammalian cells where there are multiple objectives.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

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