Models in specific organs
Our work on specific developmental systems is done in close collaboration with experimentalists, mostly with Jukka Jernvall's group and other groups in the center of excellence in experimental and computational developmental biology.
Tooth development with Jukka Jernvall's and his group. (Salazar-Ciudad and Jernvall, 2002, 2010; Salazar-Ciudad, 2008; 2012)
Turtle caparace with Roland Zimm, Jukka Jernvall and Scott Gilbert's group
An important problem in current biology is that of the relationship between genetic and phenotypic variation or genotype-phenotype map. We know that one leads to the other but with our current understanding it is not possible to predict how a complex phenotype will change when we change some gene (this is only possible in trivial cases as when predicting that a specific organ would not be formed) nor which range of changes in the phenotype are possible. This is clearly important to understand phenotypic evolution, but it is also crucial in many other fields. In the case of morphology, this genotype-phenotype map arises because of embryonic development. In our work we use the current knowledge about development in specific organs to build realistic genotype-phenotype map models. We use them to study the origins and nature of morphological variation in natural populations (Salazar-Ciudad and Jernvall, 2010) and how it affects the direction and dynamics of adaptation at the phenotypic level (Salazar-Ciudad and Marin-Riera, 2013).
We have a set of early models in which we identify that there is indeed a mathematical constraint on the possible range of gene network topologies that can lead to pattern formation in cells that are communicating by extracellular signals (e.g. growth factors) (Salazar-Ciudad et al., 2000, 2001a,2001b). Most development, however, can not be understood by looking at gene networks and cell signalling. This is because cells are moving while signaling and then which cells receive a signal does not depend on how much of the signal cells secrete but also on where the cells move. Most of the times cells are moving while signaling so that which cells receive which signals does not only depend on the distances between sending and receiving cells but also on how they move. This in its turn, depends on the mechanical properties of cells and cell aggregates that are affected by genes and by epigenetic factors. For that reason we built and are building more general models of pattern formation and morphogenesis in which cells are not only signalling to each other but do all the things they do in development: dividing, dying, adhering to each other, contracting, etc... (Salazar-Ciudad et al., 2003; Salazar-Ciudad and Jernvall, 2004; Salazar-Ciudad 2006a, 2006b, 2010; Salazar-Ciudad and Jernvall, 2010).
Synthetic Evolutionary Biology
Our ultimate goal is to contribute to a better understanding of evolution and to improve current evolutionary biology. We do that mostly by focusing on those aspects of the theory that have received less attention, but that can be argued to be central to it. As described above, this involves understanding why some phenotypic variations are common while others are not found (the question about nature of variation question) and understanding how the processes that produce them (development) evolve. By doing so we contrast our results with concepts, assumptions and biases that exist in evolutionary theory (specially in evo-devo, populational and quantitative genetics but also in evolutionary ecology). We have done that with important concepts such as developmental constraints (Salazar-Ciudad 2006), graduality (Salazar-Ciudad and Jernvall, 2005), robustness and canalization (Salazar-Ciudad, 2007), the evolution of major animal groups (Salazar-Ciudad, 2010), novelty (Salazar-Ciudad, 2006) and the structure of evolutionary theory in general (Salazar-Ciudad 2006, 2007). From that perspective we also explore how an evo-devo improved evolutionary theory can be used to understand multiple phenomena in cultural evolution (Salazar-Ciudad, 2010) and for other non-biological systems (Salazar-Ciudad, 2008, 2013).