Biomimetic Model Membrane Systems Serve as Increasingly Valuable in Vitro Tools
Bibliographic record
Abstract
the composition and complexity of the natural membrane will provide further insight into the mechanisms of membrane processes in biological systems. MembranesAs lipids are small amphiphilic molecules, there are three aspects that define the physical characteristics of a lipid: the polar headgroup, the hydrophobic acyl chains and the interface between them.There are several different lipid headgroup classes, each with unique chemical properties.Some biological headgroups are negatively charged and exhibit charge-charge repulsions, which result in larger effective cross-sectional areas (Cullis et al., 1986).However, the charge, and thus the area, is subject to the experimental conditions.Changes in the pH of the solution can impart or eliminate charges from the lipid based on the specific pKa values of the headgroup.The presence of mono-or divalent cations can serve to shield or neutralize the charge-charge repulsions, thus decreasing their effective cross-sectional area and consequently altering the properties of the lipid (Tate et al., 1991).Unlike the polar headgroups, which can be altered by the environment, the behavior of the hydrophobic acyl chains is mainly based on their chemical structure.Acyl chains are typically 14 to 22 carbons long and can be fully saturated, mono-unsaturated, or polyunsaturated.Length and degree of saturation play a major role in lipid packing and the behaviour of the membrane.Fully saturated lipids pack more tightly than lipids with unsaturated acyl chains, changing the fluidity, transition temperature, and the lateral membrane pressure profile.Longer chains also have greater van der Waals interactions that stabilize membranes (Birdi, 1988).In contrast, the increased cross-sectional area of unsaturated lipids enhances membrane fluidity (de Kruijff, 1997).Membranes are known to play an important role in many crucial biological functions, be it as the cellular membrane or as barrier of intracellular compartments.The fluid mosaic model of biological membranes (Singer and Nicolson, 1972) was groundbreaking in the understanding of membrane dynamics and organization, and the main concept of free diffusion of lipid and protein molecules within a dynamic fluid bilayer is still relevant.Current research supports the fact that several proteins are sensitive to the presence of specific lipids, with some experiencing an increase in activity while others require the presence of certain lipids for proper membrane insertion or multimeric stability (van der Does et al., 2000;van Dalen et al., 2002;van den Brink-van der Laan et al., 2004).However, one of the main emphases of the fluid mosaic model was that proteins and lipids were free to diffuse within the membrane, distributed randomly throughout with no regions of distinct composition.Research now supports the existence of lipid domains, distinct regions of specific lipid composition within the fluid bilayer (Rietveld and Simons, 1998;Zerrouk et al., 2008).These domains possess unique physical properties and could be vital for many cell processes such as signal transduction, cell adhesion, and the function of several membrane proteins (Simons and Ikonen, 1997;Harder et al., 1998). The mammalian membraneMammalian membranes are primarily composed of phosphatidylcholine (PC), sphingomyelin (SM), phosphatidylserine (PS), phosphatidylethanolamine (PE), and cholesterol (Chol) lipid species in various ratios depending on cell type.The human erythrocyte membrane, one of the best characterized systems, is composed of 19.5% (w/w) of water, 39.5% of proteins, 35.1% of lipids, and 5.8% of carbohydrates (Yawata, 2003). www.intechopen.com How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: Mary T.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".