Noise, Signal And Information in Models of Stochastic Gene Expression
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».