Hagolani, P. | Search bioRxiv

bioRxiv2024

Bounding phenotype transition probabilities via conditional complexity

By linking genetic sequences to phenotypic traits, genotype-phenotype maps represent a key layer in biological organisation. Their structure modulates the effects of genetic mutations which can contribute to shaping evolutionary outcomes. Recent work based on algorithmic information theory introduced an upper bound on the likelihood of a random genetic mutation causing a transition between two phenotypes, using only the conditional complexity between them. Here we evaluate how well this bound works for a range of genotype-phenotype maps, including a differential equation model for circadian rhythm, a matrix-multiplication model of gene regulatory networks, a developmental model of tooth morphologies for ringed seals, a polyomino-tile shape model of biological self-assembly, and the HP lattice protein model. By assessing three levels of predictive performance, we find that the bound provides meaningful estimates of phenotype transition probabilities across these complex systems. These results suggest that transition probabilities can be predicted to some degree directly from the phenotypes themselves, without needing detailed knowledge of the underlying genotype-phenotype map.

biophysics↗

bioRxiv2019

Cell signalling stabilizes morphogenesis against noise

Embryonic development involves gene networks, extracellular signaling, cell behaviors (cell division, apoptosis, adhesion, etc.) and mechanical interactions. How should gene networks, extracellular signaling and cell behaviors be coordinated to lead to complex and robust morphologies?\n\nTo explore this question, we randomly wired genes and cell behaviors into a huge number of networks in EmbryoMaker. EmbryoMaker is a general mathematical model of animal development that simulates how embryos change, i.e. how the 3D spatial position of cells change, over time due such networks. Real gene networks are not random. Random networks, however, allow an unbiased view on the requirements for complex and robust development.\n\nWe found that the mere autonomous activation of cell behaviors, especially cell division and contraction, was able to lead to the development of complex morphologies. We also found that complex morphologies tend to be less robust to noise than simple morphologies. However, we found that morphologies that developed through extracellular signaling and complex gene networks were, on average, more robust to noise. This stabilization occurs when gene networks and extracellular signaling partition the embryo into different regions where cell behaviors are regulated in slightly different ways. Our results are consistent with theories proposing that morphological complexity arose in early metazoan evolution as a consequence of the cell bio-mechanics already present in protozoa and that robustness evolved by the co-option of gene networks and extracellular cell signaling.

developmental biology↗