Periodic Reporting for period 4 - SIMBIONT (A data-driven multiscale simulation of organogenesis)
Reporting period: 2019-04-01 to 2021-08-31
Scientifically, it is a prime biological example of complex multi-scale control – in which macroscopic and microscopic phenomena feedback to control each other. In particular, the macroscopic state of the system (at the organ scale) feeds back to control at least two types of microscopic cellular decisions: Firstly, during development cells constantly make choices about which cell type to adopt (for example bone cells versus muscle cells). Secondly, cells also continually make choices about which physical movements to make – to migrate, contract, divide or die.
At the heart of these decisions are many hundreds of genes and proteins wired together into cellular networks (or “control circuits”). The dynamically changing states of these cellular networks reflect the decisions being made, but this process cannot be understood by molecular analysis alone. 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. Such complex feedback systems can display very non-intuitive behaviour, and we are therefore still far from understanding how organs are reliably constructed. To do so will require integrating many types of 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 (which affects 1 in 500 births), (b) cancer - many pathways of interest for tumor control have been discovered as examples of de-regulated development, (c) stem cells - the process of organogenesis is essentially 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 organ chosen for the SIMBIONT project is the developing limb, because it is the most tractable example of mammalian organogenesis. It was already studied intensively by embryologists in the 1940s and 50s, long before molecular biology entered the field, which encouraged the development of strong conceptual frameworks regarding “organisers” – regions of tissue which secrete diffusible signals to coordinate growth and patterning. Studies of limb development have thus contributed some of the key principles to the field, which remain invaluable today. Since the 1980’s this conceptual framework has been complemented by a wealth of molecular data, and experimental genetics from the mouse. Over 1,800 mutant mouse strains have been documented with defects in limb develoment, and the ability to generate sophisticated genotypes now means that double or even triple limb-specific conditional knock-outs have become increasingly common within the field. Finally, it is clear that both the principles of multi-scale coordination and the specific genes involved will be very relevant to many other organ systems.
Beyond the empirical data-sets, the core goal of SIMBIONT is to create the first full 3D + time computer simulation of limb development, which includes the dynamics of the gene regulatory networks which control the process, all relevant cellular activities, and makes good “macroscopic” predictions of the morphologies – both wildtype and mutant phenotypes. It was clear that linking all of these features together into a single simulation framework would require a new software platform. Our most exciting results of the project so far have been in the creation of just such a new computer program. Named yalla, this software was written in the lab from scratch and designed from day 1 to employ GPUs (graphics processing units), rather than CPUs. In other words, the code of the simulator was written in the GPU-specific language CUDA, such that it is completely dedicated to this task, and runs extremely fast (on either a standard desktop GPU, or a GPU cluster).
For the first publication we demonstrated the power of yalla in simulating proof-of-concept morphogenetic processes. In particular, we programed it to emulate both mesenchymal tissue and also epithelial sheets. In the latter case it employs a cell-based polarity to represent the apical-basal axis. This allows a computationally-efficient method to endow the epithelial sheet with a controlled degree of rigidity (resistance to bending). In the case of mesenchymal tissue, the cells may also exhibit polarity, which can be used to represent “3D tissue polarity”. Since then, we have continued to develop the model, and currently have some very sophisticated simulations representing the different hypotheses of how limb bud morphogenesis occurs.