Building Engineered Membranes, Devices, and Experimental Results
Bibliographic record
Abstract
Part II of this book deals with the construction, formation, and operation of engineered tethered membrane devices. Detailed descriptions are provided on the molecular components of engineered membranes, methods for inserting peptides and proteins, and how to measure the structure and dynamics of these biomimetic devices. Four biomimetic devices built out of engineered tethered membranes are discussed: the ion-channel switch (ICS) biosensor, the electroporation measurement platform (EMP), the electrophysiological response platform (ERP), and the pore formation measurement platform (PFMP). Several real-world examples are provided such as how the ICS biosensor can be used for the rapid detection of influenza A, how the PFMP can be used to infer the pore-formation dynamics of the antimicrobial peptide PGLa (peptidylglycylleucine- carboxyamide), how the EMP can be used to study the membrane conductance dynamics, and how the ERP can be used as a noninvasive method for measuring the response of ion channels and cells. We also discuss how to perform experimental measurements using engineered tethered membranes to determine the structure and dynamics of the membrane and macromolecules in the membrane. The experimental techniques discussed include electrical measurements, and spectroscopy and imaging techniques (e.g., X-radiation refractometry, neutron reflectometry, fluorescence recovery after photobleaching, and nuclear magnetic resonance). These measurement methods not only yield important biological details about the membrane but also verify its structure. Since engineered tethered membranes mimic real biological membranes, the experimental studies reported involving antimicrobial peptides, electroporation, growth of cells, and other properties of the membrane give significant insight into how biological membranes function. Parts II and III of the book together give a complete account of how to engineer artificial membranes: building them, mathematically modeling their dynamics, and interpreting and refining their design. The reader interested in mathematical modeling of engineered membranes can read §4.1, §§5.1–5.4, and parts of Chapter 6 before proceeding to Part III. For a laboratory exercise on building engineered tethered membranes, refer to §4.4.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".