What we aim at

Our group is focused in the study of Evolution.

We are mostly interested in the evolution of complex phenotypes and gene networks. We focus mostly on morphology but we also study other phenotypic levels (even beyond biology). Our approach considers that both natural selection and variation determine the direction of evolutionary change.

Variation is, however, neither totally random (it is only random in respect to its adaptive value) nor equally possible in all phenotypic directions. The directions of possible variation are determined by the process of development in which genetic and environmental variation gives rise to specific phenotypic variation. Thus, to understand the direction of evolutionary change one needs to understand something about developmental dynamics and its variation. That "something" is mostly pattern formation and morphogenesis. This is how a simple egg cell gives rise, over time, to many different cell types that are organized in a specific and complex manner in space. Variation in that process is what gives variation in the final morphology. In addition, development itself also evolves so, in the long run, to understand the direction of evolutionary change, one also needs to understand the forces affecting the evolution of development (this is again natural selection and variation). We aim, thus, to understand which kind of phenotypic variation is possible in the different modes of development existing in animals. We also aim to understand its evolution by looking at the genetic bases (mostly gene network topology) of each mode of development and its relationship with the kind of phenotypic variation it can produce. Then we study how this and natural selection determines how the phenotype changes. In that process we have found that there is a limited number of ways in which genes can connect into networks to lead to pattern formation and morphogenesis. This largely simplifies the understanding of development and its evolution because: 1) The whole development of an organism can be understood as a composition of those basic gene networks 2) The evolution of development can be understood as the replacement between those gene networks on the bases of which ones are most likely to arise by mutation (that depends on its genetic structure: e.g. number of genes) and which of them are most likely to produce adaptive phenotypic variation in each different environment. We largely explore this to better understand evolution and improve the theoretical bases of evolutionary biology (that so far has not focused much on the nature of variation as such).

Since we want to understand the range of possible phenotypic variations in different animals' development we have decided to use modelling approaches. Our models take as inputs these gene networks and the initial distribution of cells in space (in a given stage in development) and provide as a result the final organ morphology and patterns of gene expression in a given organ (in a given, latter, stage of development).

Each model is simply a mathematical implementation of a hypothesis about how an organ develops. We construct these hypotheses, based on experimental work from other groups, and implement them in a computational model. The advantage of computational models in respect to merely verbal arguments is that the models provide precise quantitative predictions that are more easily to unambigously compare with experimental results (from new experiments aimed at testing the hypothesis). Merely verbal arguments are more difficult to be proven wrong or right and get even difficult to express when the process under study involves a large number of cells in complex movement and communication between them (as it is often the case in development).These easily lead to largely unintuitive dynamics that are hard to analyze without quantitative models. In addition, computational models allow to explore not only the wild-type but also, by variaton in the underlying gene network, the range of possible morphological variants (and how they change through development). The capacity to play with the parameters of the model allows us to actually understand its dynamics. Ultimately, a model is simply a summary of what we think we understand about a system but that allows us to see if the underlying hypothesis could work. That the model works does not imply that the hypothesis is right, further experiments are required, but if the model can not produce the right wild-type it means that the underlying hypothesis is wrong or incomplete. In other words, what we thought we understood, we did not actually understand.

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)

Schema of the tooth model (Salazar-Ciudad and Jernvall, 2002,2010)

Comparing some experiments with what our mechanistic mathematical model of tooth morphogenesis would predict for the same alterations in the model network as in the experiment

Comparison between observed variation in tooth morphology and variation observed in the model by variyng the intensity of one genetic interaction

Turtle caparace with Roland Zimm, Jukka Jernvall and Scott Gilbert's group

Dynamics of carapace scute formation according to a double reaction-diffusion model. (Moustakas-Verho 2014).

Models on the evolution of development

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).

Models on evolution and how it is affected by developmental dynamics

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).

Example of replacement between tooth shapes in a simulation of evolution by selection and realistic developmental mechanisms of tooth shape formation (Figure made by Miquel Marí­n-Riera)

Schema representing how there is a complex relationship between the genetic space (left) and the morphospace (two images at right) and this relationship depends on development. The two images in the right represent the variational properties of a morphodynamic mechanism (up) and a morphostatic mechanism. In morphodynamic mechanisms the developmentally possible morphospace is more diverse (disparate) and less continuous, in the morphospace occupiable by morphostatic mechanisms the morphologies produces are not so diverse (the occupied area is not so spread in the morphospace) but it is occupied in a more continuous manner. This means that for any two given possibles morphologies and intermediate morphology is likely to exist if this morphology is produced by a morphostatic mechanisms but not if it is produced by a morphodynamic one. Adapted from Salazar-Ciudad and Jernvall, 2004.

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).