Editorial: Pattern formation in biology
Bibliographic record
Abstract
Pattern formation in biologyCells can self-organize in time and space forming biological patterns [1].Examples of pattern formation in biology are very diverse and can be found in a wide variety of tissues and organisms.For instance, the segmentation process along the longitudinal axes of vertebrates and invertebrates [2, 3], the fine-grained mixtures of different cell types appearing in both plant and animal tissues [4], the regular arrangement of organs along the plant shoot [5], and the cell polarity patterns appearing in multiple cell types [6], among many others.Pattern formation arises from the coordination and interplay of several mechanisms and processes across molecular, cellular and tissue scales.At the cellular level, growth, cell fate specification, migration and cell-cell interactions can be important and influence each other during the formation of a tissue.All these processes are finely orchestrated in space and time by gene expression, which in turn can also be affected by these processes.Over the past two decades, the study of pattern formation in biology has attracted the attention of many scientists from diverse fields, ranging from developmental biology, cell biology and synthetic biology, to physics, mathematics and computer science.Quantitative and interdisciplinary approaches have become essential for understanding these challenging phenomena [7,8].This Research Topic contains a collection of articles and reviews that use quantitative and interdisciplinary perspectives to understand the underlying mechanisms driving biological pattern formation.Modeling morphogenetic processes, gene regulatory network dynamics and morphogen gradients link the articles of this Research Topic, with a focus on three research areas: 1) underlying mechanisms of patterning processes; 2) cross-talk of morphogenetic and pattern formation processes, and 3) mathematical methods for modeling and quantifying biological patterning and morphogenesis.Below, each of the present Research Topic papers is briefly discussed.One of the most celebrated mechanisms to explain self-organizing spatial structures is known as the Turing instability [9-13].Lacalli's review provides a history of the application of Turing's ideas in developmental biology, which he has been a part of since the 1970's. In particular, Lacalli emphasizes the progress that can be made by investigating and understanding the role of such physicochemical systems that can make patterns de novo within the context of evolved biochemical or gene regulatory networks and that confer some degree of "programmatic assembly" on developmental phenomena. Lacalli details ways in which the relative contribution of de novo and programmatic elements may manifest in the generation of robust body and brain structures, including consciousness.Certainly, although today there are no doubts about the Turing instability as a source of symmetry breaking in biological patterning, the molecular mechanisms behind Turing
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".