These shared features include phenotypic bias, genetic correlations and a non-negative trend between phenotypic robustness and evolvability.
These features make the model useful as a tractable system for future work on how the structure of non-deterministic GP maps shapes evolutionary dynamics.
In this paper, I go beyond this limitation by considering non-deterministic GP maps, where each genotype generates an ensemble of phenotypes.
These maps can be characterised in close analogy with “classic” GP maps, but they are harder to build computationally and harder to understand conceptually.
Last few days to submit your abstract 📮
Beyond providing a practical tool, the model also shows that these shared structural features can emerge from a small number of ingredients — suggesting they may be quite general.
Paper: doi.org/10.1371/jour...
To help tackle this complexity, I show that a simple, tuneable synthetic model for non-deterministic GP maps reproduces key shared features of non-deterministic GP maps derived from biophysical models.
Out now in PLOS Computational Biology 🧬
We have an established theoretical framework for genotype–phenotype (GP) maps, describing their structural features and how those features shape evolution. But much of this assumes one discrete phenotype per genotype - which is often not true.
🧮 Join us for "Maths and Numbers in Biology" - a one-day symposium on 17th June at IQS Barcelona.
Co-organised with @giodal.bsky.social, to bring together mathematical and computational biology research from across BCN:
mathsbio.iqs.url.edu