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.
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.
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
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
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.
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
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
Olivier et al. (2010). Cell lineage reconstruction of early
zebrafish embryos using label free nonlinear microscopy.
Science 329: 967-971.
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
language, Klavins et al. 2012), spatial/amorphous computing
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
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.
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.