Bibliographic record
Abstract
Model-based design (MBD) is a well-known approach in the biomedical and health sciences research community. It has a significant impact for answering important questions related to human metabolism, systems pharmacology, precision medicine, epidemiology and public health. Multi-scale modelling is one of the MBD approaches to analyze and study complex biological systems like the human body. However, there are some challenges associated with this approach. For instance, a lack of systematic integration methods, a lack of efficient computational algorithms, and difficulty for model validation. This thesis aims to meet these challenges. First, I present a multi-scale modelling framework that was developed using a dynamic parsimonious flux balance analysis technique. The mechanistic model integrates the metabolomic and genomic data with human physiology. Simulations are performed to demonstrate the computational efficiency of integration algorithm. Then, I use the proposed model to identify biomarkers for inborn errors of metabolism. I explore how the developed model can be used to predict blood alcohol concentrations. Here, in vivo data are integrated with the metabolic model to validate the prediction results. In the next section of the thesis, I develop a mathematical model for low-density lipoprotein cholesterol (LDL-C) regulation in the body. The proposed model integrates the metabolic pathway for cholesterol synthesis, hepatic LDL receptor (LDLRs) signaling pathway, and proprotein convertase subtilisin/kexin type 9 (PCSK9) mechanism to degrade LDLRs at the surface of the hepatocyte cells. Circulating PCSK9 increases endosomal and lysosomal degradation of hepatic LDL receptors, resulting in the decreased ability to clear LDL-C from the circulation. Experimental evidence and previous studies are used to obtain the parameters of the model. The LDL-C model is employed to predict the effect of anti-PCSK9 and statin drugs on LDL-C levels in a certain populations with hypercholesterolemia. In the next part of the research, I develop an algorithm to estimate drug dosage for individual patients with hyperuricemia. The proposed computational algorithm uses the multi-scale modelling framework to create individual disease models. An in silico study shows the potential of proposed modelling framework in precision medicine. Finally, aligned with the research objective, I develop a new modelling framework to capture multiple functions of the liver cells simultaneously. The simulation results show that the developed model has promise with respect to computational efficiency, convergence and robustness. The proposed multi-scale metabolic modelling framework integrating genomic and metabolomic data presented in this thesis should aid biomedical engineers and clinicians to address different health-related problems such as obesity, diabetes, cardiovascular, and genetic diseases.
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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 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.005 | 0.001 |
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.
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".