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

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.

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

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

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