Mesoscopic Models of Cellular Signaling Reveal Strategies for Specificity in Crosstalk Signal Pathways
Bibliographic record
Abstract
Successful cellular signal processing strategies must be robust to stochastic fluctuations and yet sensitive to small differences between molecular signals, often in a signaling environment containing many cross-wired signals. Understanding how cells overcome challenges posed by signal crosstalk is important for our understanding of immune processes such as the initiation of anti-viral defenses by Type I Interferon signaling, or kinetic proofreading by T cell receptors. This thesis addresses several interrelated problems regarding molecular mechanisms for distinguishing signals and managing stochastic fluctuations for reliable cellular decision making in situations with signal crosstalk. Despite systematic categorization and highly detailed structural descriptions of the various molecular signals involved in the immune response, quantitative predictions of immune behaviour are difficult.Immune signaling pathways involve many sequential molecular interactions, and this organizational complexity presents a significant barrier to modeling immune processes. In this thesis, I study how receptor signaling dynamics contribute to signal processing specificity in situations with signal crosstalk. I use the Type I Interferon signaling pathway as a model system to study signal crosstalk and I develop a computational model of this pathway to investigate how the Interferon receptor performs signal discrimination. I use mesoscopic modeling to capture key features of complex signal pathways and to capture the intrinsic stochasticity of receptor signaling dynamics, without directly modeling every known molecular detail of these signaling systems. Using this approach, I show that the intrinsic stochasticity of kinetic proofreading receptors fundamentally constrains the robustness to fluctuations of downstream signal processing. Both the Interferon receptor and the kinetic proofreading receptor exhibit deficiencies in achieving specificity in crosstalk signal pathways. I develop a minimal model for a receptor with multiple outputs which can overcome these deficiencies to simultaneously identify and accurately sense the concentrations of arbitrary unknown ligands present individually or in a mixture, even in the presence of stochasticity. As a whole, this thesis focuses on the dynamical factors of cell signaling which contribute to specificity in noisy crosstalk signal pathways.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".