Dimensionality-Reduction in the Drosophila Wing as Revealed by Landmark-Free Measurements of Phenotype
Alba, V.; Carthew, J. E.; Carthew, R. W.; Mani, M.
Show abstract
Organismal development is a complex process, involving a vast number of molecular constituents interacting on multiple spatio-temporal scales in the formation of intricate body structures. Despite this complexity, development is remarkably reproducible and displays tolerance to both genetic and environmental perturbations. This robust-ness implies the existence of hidden simplicities in developmental programs. Here, using the Drosophila wing as a model system, we develop a new quantitative strategy that enables a robust description of biologically salient phenotypic variation. Analyzing natural phenotypic variation across a highly outbred population, and variation generated by weak perturbations in genetic and environmental conditions, we observe a highly constrained set of wing phenotypes. Remarkably, the phenotypic variants can be described by a single integrated mode that corresponds to a non-intuitive combination of structural variations across the wing. This work demonstrates the presence of constraints that funnel environmental inputs and genetic variation into phenotypes stretched along a single axis in morphological space. Our results provide quantitative insights into the nature of robustness in complex forms while yet accommodating the potential for evolutionary variations. Methodologically, we introduce a general strategy for finding such invariances in other developmental contexts.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Ligand-receptor promiscuity enables cellular addressing 94%
- Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma 94%
- Emergence of synchronized multicellular mechanosensing from spatiotemporal integration of heterogeneous single-cell information transfer 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.