A Multi-Scale Model of the Whole Human Body based on Dynamic Parsimonious Flux Balance Analysis
Bibliographic record
Abstract
The multi-scale modelling approach is a powerful mathematical technique for simulating and analyzing complex biological systems such as the human body. This tool can help study the interactions of the various networks in a living organism, from the cellular level up to the population scale, in one framework. In this paper, a generic mathematical model is developed that describes human metabolism with 237 serum metabolites integrated with a chosen set of human metabolic networks. A new computational approach is presented for solving the resulting dynamic problem using parsimonious flux balance analysis (pFBA). To illustrate the performance of the proposed approach, the human hepatocyte genome scale model is selected for the metabolic network to be included. The simulation results show that the proposed approach has promise with respect to both computational efficiency and convergence. To demonstrate the potential application of the developed model, prediction of amino acid biomarkers for a set of inborn errors of metabolism (IEM) is considered as an example. All the simulations are performed using MATLAB and the COBRA toolbox. This framework has the potential to simulate various human metabolic disorders to help with the diagnosis of associated human diseases and to suggest novel treatment strategies. In addition, it opens the door to new opportunities for personalized medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".