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SIMBIONT Report Summary

Project ID: 670555
Funded under: H2020-EU.1.1.

Periodic Reporting for period 1 - SIMBIONT (A data-driven multiscale simulation of organogenesis)

Reporting period: 2015-09-01 to 2017-02-28

Summary of the context and overall objectives of the project

The goal of SIMBIONT is to build the first full computer simulation of limb development. Understanding the genes, pathways and molecular networks which underlie organogenesis has enormous potential impact, both scientifically and medically:

Scientifically, organogenesis is a prime biological example of complex multiscale control. Gene networks and molecular signalling are responsible for controlling cellular decisions, such as which direction a cell should migrate or contract. These cellular activities produce tissue movements, and ultimately large-scale changes in organ shape, but the flow of information is not one-way. As a cell’s position changes during morphogenesis, the range of signals it receives from neighbours also changes, simply as a consequence of these geometric rearrangements. Thus, genes control cells, but cell movements equally control genes, creating a multi-scale feedback loop. A rigorous understanding of the gene networks underlying organogenesis will not be possible without addressing this high-level feedback – a true systems-level question involving feedback control theory – and yet so far it remains a relatively neglected area. It will require integration of many types of quantitative data and sophisticated computer modelling, and thus represents a scientific grand challenge.

Medically, developmental genes and networks are central to: (a) human congenital abnormalities – such as heart defects or polydactyly, (b) cancer - many pathways of interest for tumor control were discovered as examples of de-regulated development, (c) stem cells - the process of organogenesis can be described as the control of large populations of mulitpotent progenitor cells, and most excitingly (d) regenerative medicine - these pathways clearly underlie the promise of tissue and organ regeneration.

The overall goal is thus to address and understand new systems-level questions about (i) the molecular control of organ shape, (ii) coordination of patterning and growth, and (iii) the multiscale robustness of the system. Key predictions of the model will also be tested experimentally (both with mouse mutants, and in vitro perturbations). SIMBIONT will serve as an example for modeling other complex multicellular processes in the future, eg. tissue engineering and regenerative medicine.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

A major achievement of the project so far, is the gathering and computational integration of data on the tissue movements of the developing limb bud over time. A variety of techniques and technologies have been developed and improved for this task (both improvements in 3D mesoscopic imaging, and in new software developments), and we now have a very comprehensive set of data on this biological process. A second central block for SIMBIONT is the generation of a 3D atlas of gene expression patterns, and we have made significant progress in gathering new data, and again also the development of new software for building the atlas. Related to this, we have performed quantitative assessment of cellular proliferation rates across time and space, and this data is now ready for integration into the atlas as well.
On the modelling side, we have made progress both in software developments, and also the exploration of gene circuit models to explain the various biological phenomena. In particular, we have built a new 3D+time Centroid Model, based on GPUs, which is much faster than previous models, allowing us to perform efficient parameter optimisation. We have also created the first simulation which integrates both Proximo-Distal Patterning with Digital Patterning, illustrating for the first time how signalling pathways which act in more than one process act as constraints on the possible design and dynamics of the process.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Despite the clear promise of computer models to help understand complex biological systems, dynamical computer modelling of developmental processes is still rare. The few modelling studies which exist, have focused on basic principles or modules, one-by-one. Eg: interpretation of a single morphogen gradient, GRNs that can create a single defined pattern, or a specific morphogenetic process (such as tissue extension). In reality, morphogenetic processes like this represent complex multiscale feedback control. Gene networks and molecular signalling are responsible for controlling cellular decisions, such as which direction a cell should migrate or contract. These cellular activities produce tissue movements, and ultimately large-scale changes in organ shape.

But the flow of information is not one-way. As a cell’s position changes during morphogenesis, the range of signals it receives from neighbours also changes, simply as a consequence of these geometric rearrangements. Thus, genes control cells, but cell movements equally control genes, creating a multi-scale feedback loop. A rigorous understanding of the gene networks underlying organogenesis will not be possible without addressing this high-level feedback – a true systems-level question involving
feedback control theory – and yet so far it remains a relatively neglected area. It will require integration of many types of quantitative data and sophisticated computer modelling which goes beyond the state-of-the-art. SIMBIONT is thus addressing a scientific grand challenge.
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