Molecular communication in biological systems
Bibliographic record
Abstract
Molecular communication occurs ubiquitously at all levels of biological systems including molecule, cell, tissue, and organ levels. In this chapter, we examine how bionanomachines or a system of bio-nanomachines communicate using molecules. First, we introduce two dimensions to characterize molecular communication systems: scale and mode. The scale refers to the range of distances over which bio-nanomachines communicate by propagating molecules, and it is roughly divided into intracellular, intercellular, and inter-organ levels. The mode of molecular communication systems refers to how molecules propagate between bio-nanomachines; it is either passively or actively. We then go over, from a communication engineering perspective, a number of examples of molecular communication systems found in nature. Following the previous chapter, basic biology terms are in bold in this chapter. 3.1 Scales of molecular communication Molecular communication in a human body can be studied at three structurally different levels: intracellular, intercellular, and inter-organ levels, which are respectively, molecular communication within a cell (up to the size of a cell about 100 μm), between nearby cells (up to a population of cells, from a few μm to 10 mm or longer), and between distant cells (up to a few meters). At the intracellular level, a number of sub-cellular bio-nanomachines within a cell communicate to sustain the life of the cell. At this level, physically separated bio-nanomachines interact directly through diffusion and collision or indirectly by propagating diffusive molecules.
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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 | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".