A dynamic Turing model of digit patterning : a Turing mechanism modulated by positional information underlies digit specification

The specification of the vertebrate limb skeleton is a classical model to study pattern formation during development. Two different theories have been proposed to explain this process: the Turing mechanism and the Positional Information model. This thesis uses computational modeling to explores to which extent these two theories can be combined to explain digit patterning. The main result of this work is a computational model of digit patterning that suggests that a Turing mechanism modulated by Hox genes and Fgf-signaling underlies digit specification. By comparing simulations and experimental data we show that the Turing mechanism is implemented by Bmps, Sox9 and Wnts. The model shows that a combination of Positional Information and Turing can implement an extremely reliable patterning mechanism and suggests that Fgf-singling coordinates patterning and growthL'especificació de l'esquelet de las extremitats dels vertebrats és un model clàssic per estudiar la formació de patróns durant el desenvolupament. Dues diferents teories van propusarse per explicar aquest procés: el mecanisme de reacció-difusió de Turing i el model de Positional Information. Aquesta tesi utilitza modelos computacionals per explorar si aquestas dues teories es poden combinar per explicar el patron dels dits. El resultat principal és un model computacional que suggereix que un mecanisme de Turing modulat per Hox genes i Fgfs controla l'especificació dels dits. Comparant simulacions amb dades experimentals aconseguim demostrar que el mecanisme de Turing és implementat per Bmps, Sox9 i Wnts. A mes, el model mostra que una combinació de un mecanisme de Turing i Positional Information aconsegueix especificar al patró de manera extremadament fiable i suggereix que els Fgfs coordinen la formació del patró amb el creixement

A dynamic Turing model of digit patterning : a Turing mechanism modulated by positional information underlies digit specification

The specification of the vertebrate limb skeleton is a classical model to study pattern formation during development. Two different theories have been proposed to explain this process: the Turing mechanism and the Positional Information model. This thesis uses computational modeling to explores to which extent these two theories can be combined to explain digit patterning. The main result of this work is a computational model of digit patterning that suggests that a Turing mechanism modulated by Hox genes and Fgf-signaling underlies digit specification. By comparing simulations and experimental data we show that the Turing mechanism is implemented by Bmps, Sox9 and Wnts. The model shows that a combination of Positional Information and Turing can implement an extremely reliable patterning mechanism and suggests that Fgf-singling coordinates patterning and growthL'especificació de l'esquelet de las extremitats dels vertebrats és un model clàssic per estudiar la formació de patróns durant el desenvolupament. Dues diferents teories van propusarse per explicar aquest procés: el mecanisme de reacció-difusió de Turing i el model de Positional Information. Aquesta tesi utilitza modelos computacionals per explorar si aquestas dues teories es poden combinar per explicar el patron dels dits. El resultat principal és un model computacional que suggereix que un mecanisme de Turing modulat per Hox genes i Fgfs controla l'especificació dels dits. Comparant simulacions amb dades experimentals aconseguim demostrar que el mecanisme de Turing és implementat per Bmps, Sox9 i Wnts. A mes, el model mostra que una combinació de un mecanisme de Turing i Positional Information aconsegueix especificar al patró de manera extremadament fiable i suggereix que els Fgfs coordinen la formació del patró amb el creixement

Data-driven modelling of a gene regulatory network for cell fate decisions in the growing limb bud

Parameter optimization coupled with model selection is a convenient approach to infer gene regulatory networks from experimental gene expression data, but so far it has been limited to single cells or static tissues where growth is not significant. Here, we present a computational study in which we determine an optimal gene regulatory network from the spatiotemporal dynamics of gene expression patterns in a complex 2D growing tissue (non-isotropic and heterogeneous growth rates). We use this method to predict the regulatory mechanisms that underlie proximodistal (PD) patterning of the developing limb bud. First, we map the expression patterns of the PD markers Meis1, Hoxa11 and Hoxa13 into a dynamic description of the tissue movements that drive limb morphogenesis. Secondly, we use reverse-engineering to test how different gene regulatory networks can interpret the opposing gradients of fibroblast growth factors (FGF) and retinoic acid (RA) to pattern the PD markers. Finally, we validate and extend the best model against various previously published manipulative experiments, including exogenous application of RA, surgical removal of the FGF source and genetic ectopic expression of Meis1. Our approach identifies the most parsimonious gene regulatory network that can correctly pattern the PD markers downstream of FGF and RA. This network reveals a new model of PD regulation which we call the "crossover model", because the proximal morphogen (RA) controls the distal boundary of Hoxa11, while conversely the distal morphogens (FGFs) control the proximal boundar

Key features of turing systems are determined purely by network topology

Turing’s theory of pattern formation is a universal model for self-organization, applicable to many systems in physics, chemistry, and biology. Essential properties of a Turing system, such as the conditions for the existence of patterns and the mechanisms of pattern selection, are well understood in small networks. However, a general set of rules explaining how network topology determines fundamental system properties and constraints has not been found. Here we provide a first general theory of Turing network topology, which proves why three key features of a Turing system are directly determined by the topology: the type of restrictions that apply to the diffusion rates, the robustness of the system, and the phase relations of the molecular species.This research was supported by the ERC advanced grant SIMBIONT (670555) and the Ministerio de Economía y Competitividad (through Centro de Excelencia Severo Ochoa 2013-2017, SEV-2012-0208). X. D. acknowledges support by the ERC-FP7 Grant Swarmorgan (601062). J. S. ackowledges support from ICREA. P. M. and L. M. were supported by ERC Starting Grant QUANTPATTERN (637840)

High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals

The Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form Turing reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems

The fin-to-limb transition as the re-organization of a Turing pattern

A Turing mechanism implemented by BMP, SOX9 and WNT has been proposed to control mouse digit patterning. However, its generality and contribution to the morphological diversity of fins and limbs has not been explored. Here we provide evidence that the skeletal patterning of the catshark Scyliorhinus canicula pectoral fin is likely driven by a deeply conserved Bmp–Sox9–Wnt Turing network. In catshark fins, the distal nodular elements arise from a periodic spot pattern of Sox9 expression, in contrast to the stripe pattern in mouse digit patterning. However, our computer model shows that the Bmp–Sox9–Wnt network with altered spatial modulation can explain the Sox9 expression in catshark fins. Finally, experimental perturbation of Bmp or Wnt signalling in catshark embryos produces skeletal alterations which match in silico predictions. Together, our results suggest that the broad morphological diversity of the distal fin and limb elements arose from the spatial re-organization of a deeply conserved Turing mechanism.This work was supported by the Spanish Ministry of Economy and Competitiveness, through ‘Centro de Excelencia Severo Ochoa 2013–2017’, SEV-2012-0208, and the Plan Nacional grant BFU2010-16428 and BFU2015-68725-P (co-financed by FEDER funds of the European commissions) to K.O., L.M., M.M. and J.S., and by ICREA to J.S. and in part by the Global COE Program ‘Evolving Education and Research Center for Spatio-Temporal Biological Network’ from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and the Program for Leading Graduate Schools ‘Education Academy of Computational Life Sciences’ from the MEXT to K.O. and M.T., the Grant-in-Aid for Scientific Research (B) 25291086 and the Inamori Foundation to M.T

A computational clonal analysis of the developing mouse limb bud

A comprehensive spatio-temporal description of the tissue movements underlying organogenesis would be an extremely useful resource to developmental biology. Clonal analysis and fate mappings are popular experiments to study tissue movement during morphogenesis. Such experiments allow cell populations to be labeled at an early stage of development and to follow their spatial evolution over time. However, disentangling the cumulative effects of the multiple events responsible for the expansion of the labeled cell population is not always straightforward. To overcome this problem, we develop a novel computational method that combines accurate quantification of 2D limb bud morphologies and growth modeling to analyze mouse clonal data of early limb development. Firstly, we explore various tissue movements that match experimental limb bud shape changes. Secondly, by comparing computational clones with newly generated mouse clonal data we are able to choose and characterize the tissue movement map that better matches experimental data. Our computational analysis produces for the first time a two dimensional model of limb growth based on experimental data that can be used to better characterize limb tissue movement in space and time. The model shows that the distribution and shapes of clones can be described as a combination of anisotropic growth with isotropic cell mixing, without the need for lineage compartmentalization along the AP and PD axis. Lastly, we show that this comprehensive description can be used to reassess spatio-temporal gene regulations taking tissue movement into account and to investigate PD patterning hypothesis.The work was funded by the MADRICEL grant from the Madrid Regional Government (S-SAL-0190-2006

Virtual meeting, real and sound science: report of the 17 th Meeting of the Spanish Society for Developmental Biology (SEBD-2020)

The Spanish Society for Developmental Biology (SEBD) organized its 17th meeting in November 2020 (herein referred to as SEBD2020). This meeting, originally programmed to take place in the city of Bilbao, was forced onto an online format due to the SARS-CoV2, COVID-19 pandemic. Although, we missed the live personal interactions and missed out on the Bilbao social scene, we were able to meet online to present our work and discuss our latest results. An overview of the activities that took place around the meeting, the different scientific sessions and the speakers involved are presented here. The pros and cons of virtual meetings are discussed