In Silico Model Development and Optimization of Lung Cell Population Dynamics
Bibliographic record
Abstract
This thesis introduces a novel methodology for developing and optimizing in silico models predicting the growth and differentiation of cell populations under various culture conditions. By systematically combining first principles with methods from experimental design, statistics, and optimization, we formulate, calibrate, select, and validate biologically informed mathematical models. To illustrate the utility of the proposed methodology, we developed a series of mathematical models of increasing complexity for airway tissue engineering. Specifically, we developed the first mathematical model for the population dynamics of BEAS-2B cells, a human bronchial epithelial cell line commonly used in respiratory research. The model predicts cell growth based on initial population and nutrient and waste concentrations. Using this model, we determined an optimal media refresh schedule that enhances growth while minimizing the consumption of costly growth media. We further applied the proposed methodology to multicellular populations to develop a mathematical model of the differentiation of induced pluripotent stem cells (iPSCs) into definitive endoderm (DE); iPSCs are adult cells reprogrammed to stem cells, and DE is a germ layer giving rise to organs like the lungs, liver, and pancreas. This model suggests no transitory state during differentiation expresses the DE biomarkers CD117 and CD184, a finding corroborated by existing literature. Additionally, the model indicates an optimal differentiation period of 1.9 to 2.4 days and identifies that lower plating populations result in higher DE yield per input cell. Furthermore, we extended the multicellular model to account for the biochemical environment to investigate the differentiation of anterior foregut endoderm (AFE) into lung progenitors (LPs); both cells are precursors to respiratory tissues. The model indicates that daily media refreshment significantly enhances LP yield, compared with no change, approximately doubling it. It also shows that the LP yield per input cell on day 15 can increase 26% at lower split ratios. This thesis underscores the utility of computational tools in tissue engineering by offering a systematic approach to optimizing cell culture conditions and experimental protocols. This approach complements traditional in vitro, ex vivo, and in vivo techniques, reducing experimental costs and timelines and improving protocol efficiency, with broad applications in regenerative medicine and drug development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".