Selection among replicators and protocell chemotaxis
Bibliographic record
Abstract
<p>This thesis describes a series of projects which have autocatalysis as the overarching theme. </p>\n\n<p>Chapter 1 is a general introduction to the construction of chemical models of biological systems. There is a discussion of what mechanisms are used to achieve autocatalysis. The biomimetic work of chapters 3 and 4 use lipid replicators, so there is particular focus on the physical autocatalysis of lipid aggregates and their use in prebiotic models. The roles that lipids play in life are used as an illustration of the promise they offer to systems chemists. A brief history of biological homochirality is given.</p>\n\n<p>Two designs for novel asymmetric autocatalytic reactions are described in chapter 2, both based on hydrogen bonding catalysis. Though neither system showed the desired amplification of chirality, this work led to the study of positive nonlinear effects within thiourea catalysis. </p>\n\n<p>Projects which attempt to couple lipid replication with protocell behaviour are presented in chapter 3. In this chapter oil droplets are used as simple model protocells. Interestingly, feedback between the motion of droplets and a lipid forming reaction are observed, with the system showing striking chemotaxis. In the second part of this chapter other systems are used to explore the relationship between lipid formation and compartment division.</p>\n\n<p>Chapter 4 describes observations relating to transient lipid replicators which can be fuelled to be maintained out of equilibrium. An initial finding shows that fuelling can result in dramatic oscillations, as well as stable concentrations. When lipids compete for resources, they typically show a succession through time, an interesting protocell model. Which replicating species dominates results from a multitude of combined effects which cannot be predicted simply using rates of formation of each lipid in isolation. </p>\n\n<p>Finally, chapter 5 outlines the conclusions that can be drawn from these results and what future work remains. </p>\n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".