René Doursat
 PhD, Habil.

Research Scientist & Fmr. Director

Complex Systems Institute, Paris Ile-de-France (ISC-PIF)
French National Center for Scientific Research (CNRS)

Research Unit UPS3611

113, rue Nationale
75013 Paris, France


Edited Proceedings

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Computational models of collective cell behavior in 2D/3D, such as organism development (embryogenesis), tumor growth, or bacterial mats (synthetic biology)
One of the greatest challenges of biology is to create a generic model of multicellular development, in order to unify what Darwin called nature's "endless forms most beautiful", and construe them as variants around a common theme. The variants are the unique genetic (and epigenetic) information of each species; the common theme is the developmental dynamics that this information guides and parametrizes.
While the "modern synthesis" of genetics and evolution focused most of the attention on selection, it is only during the past decade that analyzing and understanding variation by comparing the developmental processes of different species, at both embryonic and genomic levels, became a major concern of evolutionary development, or "evo-devo". To what extent are organisms also the product of self-organized physicochemical developmental processes not necessarily or always controlled by complex underlying genetics?

Before and during the advent of genetics, the study of developmental structures had been pioneered by the "structuralist" school of theoretical biology, which can be traced back to Goethe, D'Arcy Thompson, and Waddington. Later, it was most actively pursued and defended by Kauffman (1993) and Goodwin (1994) under the banner of "self-organization", argued to be an even greater force than natural selection in the production of viable diversity. In particular, the strong morphological properties of biological organisms can be effectively captured by the paradigm of positional information introduced by Wolpert (1969). At an abstract level, the key idea is simply that cells must establish a long-range communication system that allows them to create different parts of the organism in different locations. It is inevitable that some form of positional information should be at work in multicellular organism development, embodied in various ways, be it through passive diffusion of morphogens spreading throughout the tissue and/or cell-to-cell intermediate-messenger signaling. ← Less


Goodwin (1994) How the Leopard Changed its Spots: The Evolution of Complexity. Scribner.
Kauffman (1993) The Origins of Order: Self-Organization and Selection in Evolution. Oxford Univ. Press.
Wolpert (1969) Positional information and the spatial pattern of cellular differentiation. J. Theor. Biol. 25(1):1-47.

MECAGEN – Mechanogenetic Model and Simulation of Biological Morphogenesis  
From cell behavior to tissue deformation: A platform for the computational modeling and simulation of animal early embryogenesis  
Drawing from real data measured on microscopy imaging, my PhD student Julien Delile, under the co-supervision of Nadine Peyriéras (CNRS, Gif-sur-Yvette), designed a realistic model of animal embryogenesis. It is construed as the collective behavior of a myriad of individual cells implemented in an agent-based simulation centered on the mechanic-chemical coupling between cellular and genetic dynamics. The MecaGen platform can run both on a GPGPU array or on a cluster or computing grid.
The aim of the MecaGen platform is to provide a computational modeling and simulation environment for the multiscale dynamics of the early stages of biological morphogenesis. This virtual reconstruction is done under the control of experimental and quantitative reconstructions coming from live imaging (see BIOEMERG below). Here, embryonic development is viewed as an emergent, self-organized phenomenon based on the individual behavior of a large number of cells and their genetically regulated, and regulating, biomechanics.

Measurements are made from 4D imaging (video) observations of the first 15 hours of a model vertebrate's embryogenesis: the zebrafish Danio rerio, from the egg to the beginning of somitogenesis. This project branched out of the integrative biology platform BioEmergences, whose overall objectives are the quantitative multiscale reconstruction of development.

Model and experiments are coupled in a feedback loop, whereby the model is optimized and falsified by experimental trials of gain and loss of function. The goal is to integrate the collective motion of cells and the dynamics of their gene expression underlying the patterning of morphogenetic fields. We also investigate the causal bottom-up link from local cell behavior to global tissue deformation.

Each cell's mechanical behavior is mapped from its molecular and genetic identity. Among these behaviors, we focus in particular on cell intercalation as an active process driving tissue deformation and individual cell migration. We operate this model to explore the different morphogenetic episodes occuring through the first 10 hours of the zebrafish development: cell segmentation, enveloping layer formation, epiboly, internalization and convergence-extension.

For each specific episode, a case study is realized to decipher the respective roles of the different tissues involved. Quantitative measures reconstructed from both the simulated and the experimental data are compared to automatically explore the multi-dimensional parameter spaces of our hypotheses and their interpretation. Various state-of-the-art computational reconstructions are presented, including global 4D (3D + time) displacement fields from in toto data of the developing zebrafish embryos. ← Less

Selected Publications

Delile, J., Herrmann, M., Peyriéras, N. & Doursat, R. (2017) MecaGen: a cell-based computational model of embryogenesis coupling mechanical behavior and gene regulation. Nature Communications 8(13929) 10.1038/ncomms13929. [11 pages]. PAPER

Villoutreix, P., Delile, J., Rizzi, B., Duloquin, L., Savy, T., Bourgine, P.*, Doursat, R.* & Peyriéras, N.* (2016) An integrated modelling framework from cells to organism based on a cohort of digital embryos. Scientific Reports 6(37438) 10.1038/srep37438. [11 pages]. PAPER

BIOEMERG – BioEmergences: Reconstructing the Physiome of Model Organisms  
An integrative biology platform allowing the exhaustive detection and measurement of multicellular dynamics from in vivo observations  
Since 2007, I have been contributing to the scientific and technical direction of a team working on European (FP6/7) and French (ANR) projects about animal morphogenesis, including Embryomics (ended 2009) and BioEmergences (continued). These initiatives were launched by Nadine Peyriéras (CNRS) and Paul Bourgine (Ecole Polytechnique), and have pioneered the design of methods and algorithms for reconstructing the complete dynamics of multicellular development observed by microscopy. The workflow runs on a computing grid, partly via the OpenMOLE platform developed at ISC-PIF.
The BioEmergences platform allows biologists to produce and annotate time-lapse shots of organism development, while mathematicians and computer scientists process these images to "reconstruct" and model collective cell dynamics. This interdisciplinary collaborative effort has resulted in sophisticated software tools capable of automatically handling large amounts of 4D (3D+time) imaging data by a workflow of segmentation and tracking algorithms.
The BioEmergences workflow is especially designed to process voxel-based movies from embryos, which are engineered to highlight cell membranes and nuclei via the expression of fluorescent proteins (FPs). In the end, it provides a reconstruction equivalent to a "digital embryo" represented by a cell lineage tree annotated with quantitative measurements of membrane and nucleus shapes (Olivier et al. 2010).

The algorithmic workflow for reconstructing digital embryos is summarized in the side diagram. Raw data is composed of a temporal series of 3D images representing cell nuclei and membranes, which are automatically reconstructed from 2D stacks and stored in the BioEmergences database. Data is sent to a computation grid through a Web interface and the output results are stored in the database. Digital reconstructions can be viewed, corrected, validated and annotated through a visualization interface.
Raw images of nuclei and membranes (cyan layer) are first filtered by an edge-preserving smoothing method. Cell positions are then extracted from the local maxima of this simplified images versions, and assimilated to the nuclei's positions. They are used to initialize both the segmentation and cell tracking tasks (green layers). The final output is composed of the cell lineage tree and the nuclei/membranes segmentation (red layer). ← Less


Olivier et al. (2010). Cell lineage reconstruction of early zebrafish embryos using label free nonlinear microscopy. Science 329: 967-971.


Faure, E. et al. (2016) A workflow to process 3D+time microscopy images of developing organisms and reconstruct their cell lineage. Nature Communications 7(8674).

Castro-González, C. et al. (2014) A digital framework to build, visualize and analyze a gene expression atlas with cellular resolution in zebrafish early embryogenesis. PLoS Computational Biology 10(6): e1003670. PAPER

SYNBIOTIC – Synthetic Biological Systems: From Design to Compilation  
Translating the principles of biological morphogenesis into a stack of formal programming languages compiled and implemented in a (virtual or real) synthetic biological substrate or "bioware"  
The SynBioTIC project, whose WP1 I lead with my collaborator Taras Kowaliw and postdoc Jonathan Pascalie, proposes to design and develop tools to literally "compile" (as in programming languages) the overall behavior of a population of cells (bacteria) into processes local to each entity (one bacterium). The motivation is to exploit the collective properties of a cellular population to create artificial biosystems that can meet various needs in the fields of health care, nanotechnology, energy and chemistry.
Synthetic biology is an emerging scientific discipline that promotes a standardized design and manufacturing of biological system components without natural equivalents. It is currently in search of design principles to achieve a reliable and secure level of functionality from reusable biological parts (e.g. BioBricks, Knight 2003). Beyond genetic engineering problems, which require the development of dedicated software tools, computer scientists identify this challenge with systems design, such as large software systems and electronic circuits.

In this context, the SynBioTIC project aims at developing formalisms and computer tools making possible the specification of a global spatial behavior and its description by a tower of languages. Each language at a given level addresses distinct features. Its set of instructions can be "compiled" into the lower level, and ultimately down to the final bioware into a cellular regulation network (genetic and signalization network and metabolic pathways). This approach, similar to hardware (silicon compilation) should fill a gap between high-level descriptions of a system and low-level physical requirements.
This long-term core research project is part of the "unconventional / natural computing" family at the interface between computer science and biological engineering. It relies on the recent advances of synthetic biology along with the development of new approaches such as spatially explicit modeling (Gro language, Klavins et al. 2012), spatial/amorphous computing (MGS language, Giavitto & Michel 2002; Proto language, Beal & Bachrach 2006), and morphogenetic engineering, to deal with new classes of applications characterized by the emergence of a global behavior in a large population of cells that are irregularly located and dynamically interconnected.

While most of the studies in this field seek to design, characterize and validate reusable elementary biological components (BioBricks), SynBioTIC is positioned upstream by assuming this problem solved. Its goal is to enable the use of population-level behavior of bacteria to create artificial biosystems that can meet various needs in application domains such as health, nanotechnology, energy or chemistry. ← Less


Beal & Bachrach (2006) IEEE Intell. Sys. 21(2): 10-19.

Klavins et al. (2012). ACS Synthetic Biology.

Giavitto & Michel (2002) Fund. Informaticae 49: 107-129.

Knight (2003) Tech Rep., MIT Synthetic Biology Group.

Selected Publication

Pascalie, J., Potier, M., Kowaliw, T., Giavitto, J.-L.*, Michel, O., Spicher, A. & Doursat, R. (2016) Developmental design of synthetic bacterial architectures by morphogenetic engineering. ACS Synthetic Biology 5(8): 842-861. PAPER