Bibliographic record
Abstract
Many biological processes are stochastic, which poses a unique challenge for understandingthe role and behaviour of biological systems because intuition derived from commonly used deterministic models can be severely misleading. In this thesis, I explored this challenge by analyzing models of stochastic variability of several biological processes, with a particular focus on gene expression. First, I analyze the role of microRNAs in stochastic gene expression. These are small RNAs that silence messenger RNA molecules from being translated, and speed up their degradation. Previous work has suggested that the role of these molecules is to decrease detrimental noise in gene expression. My analysis of simple gene expression models suggests that introducing miRNAs into a system will increase rather than decrease protein noise when the silencing of mRNA via miRNA interactions also increases its degradation, which is expected for miRNA interactions with mRNA. This suggests that miRNA binding to mRNA does not generically confer precision to protein expression. Next, I develop tools to analyze the effect of periodic signals in stochastic systems. Applying this method to the mRNA-miRNA-protein networks studied earlier reveals that miRNA generally reduces the fidelity of signal transmission from deterministically varying upstream factors to protein levels, as quantified by the signal to noise ratio with and without miRNA for a periodic transcription rate. These tools also allow me to derive novel relations between glycated protein and blood glucose covariances and correlations, based off previous work with mRNA-protein correlations that I also extend to include miRNA effects. These relations can be used to test models even without high resolution temporal data, and may be useful in inverting for important parameters such as glycated protein lifetimes. Finally, I derive conditions under which violations of the data processing inequality will ii be observed for the stationary state distributions of molecules in a biochemical cascade. The data processing inequality is a key theorem in information theory that constrains the flow of information in Markov chains. However, the premise under which the inequality holds is not satisfied by stationary-state distributions of stochastic biochemical reaction cascades. Here, I show that the mutual information with an upstream signal can increase along a cascade when a slow variable reads out a noisy intermediate. My results intuitively explain the behavior of mutual information in terms of noise propagation and time-averaging. However, the results also highlight that mutual information measurements of stationary state distributions must be interpreted with care. This thesis contributes to our understanding of variability in important biological processes such as gene expression, and showcases the power of several mathematical approaches to analyze broad classes of models. iii
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".