On the evolution and development of morphological complexity: A view from gene regulatory networks

Abstract

How does morphological complexity evolve? This study suggests that the likelihood of mutations increasing phenotypic complexity becomes smaller when the phenotype itself is complex. In addition, the complexity of the genotype-phenotype map (GPM) also increases with the phenotypic complexity. We show that complex GPMs and the above mutational asymmetry are inevitable consequences of how genes need to be wired in order to build complex and robust phenotypes during development. We randomly wired genes and cell behaviors into networks in EmbryoMaker. EmbryoMaker is a mathematical model of development that can simulate any gene network, all animal cell behaviors (division, adhesion, apoptosis, etc.), cell signaling, cell and tissues biophysics, and the regulation of those behaviors by gene products. Through EmbryoMaker we simulated how each random network regulates development and the resulting morphology (i.e. a specific distribution of cells and gene expression in 3D). This way we obtained a zoo of possible 3D morphologies. Real gene networks are not random, but a random search allows a relatively unbiased exploration of what is needed to develop complex robust morphologies. Compared to the networks leading to simple morphologies, the networks leading to complex morphologies have the following in common: 1) They are rarer; 2) They need to be finely tuned; 3) Mutations in them tend to decrease morphological complexity; 4) They are less robust to noise; and 5) They have more complex GPMs. These results imply that, when complexity evolves, it does so at a progressively decreasing rate over generations. This is because as morphological complexity increases, the likelihood of mutations increasing complexity decreases, morphologies become less robust to noise, and the GPM becomes more complex. We find some properties in common, but also some important differences, with non-developmental GPM models (e.g. RNA, protein and gene networks in single cells).

Evolution of the G Matrix under Nonlinear Genotype-Phenotype Maps

“…Second, theoretical work supports the view that the when development evolves to be able to produce complex morphologies it leads, as a side-effect, to quite complex GPMs (Newman and Müller 2000;Salazar-Ciudad et al 2001;Hagolani et al 2021). Related theoretical work suggests that GPMs can become more linear over evolutionary time but that this is very unlikely for complex morphologies (Salazar-Ciudad et al 2001;Salazar-Ciudad and Jernvall 2004).…”

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

“…Similarly, for both teeth (37) and leaf shape (38), mutations to simpler tooth phenotypes are more likely than mutations to more complex phenotypes, an effect our theory also predicts. A recent theoretical study (39) of the development of morphology also found that simple morphologies were more likely to appear than complex ones upon random parameter choices. The L systems used to model plant development (4) show simplicity bias (8), and Azevedo et al (40) showed that developmental pathways for cell lineages are significantly simpler (in a Kolmogorov complexity sense) than would be expected by chance.…”

Using phenotypic plasticity to understand the structure and evolution of the genotype–phenotype map

“…On the one hand, contrary to G-P map models of arbitrary complexity (Kauffman and Levin 1987), gene network models are based on biological principles. On the other hand, as compared to quantitative genetics models that introduce arbitrary forms of epistasis (gene-gene interactions) over a traditionally additive setting (such as the multilinear model, Hansen and Wagner 2001), network models display "realistic" features of developmental systems, such as developmental constraints and discontinuities in the G-P relationship (Hagolani et al 2021).…”