Nick Monk, Sheffield/Nottingham – wants to develop a formalism for multicellular models of plant roots. There are many model types out there – they’re all encoding the same thing: the way cells interact with each other and with the environment. He’s familiar with this type of problem via the history of dealing with reaction kinetics. We need to write down information about reaction kinetics in a simulation-independent manner. Therefore they need to write down the multicell models in a way that does not depend on the simulation environment. For reaction kinetics, it was fairly straightforward to do this as there was already a good list of terms describing reaction kinetics.
For cell behaviours, when we talk about them we tend to talk about them in a subjective / qualitative level. Humans using their pattern recognition skills to identifying the behaviours – there are no real quantitative metrics for determining behaviour. What would be most useful is a way to abstract out information from images of cells that would allow us to determine the behaviours they’re exhibiting.
If we generate time-course image data, what are we going to do with them? Therefore we need a way to annotate these images == the annotation case study. They want to have a session on multicellular modelling standards at the next international systems biology conference (ICSB, Edinburgh summer 2010).
Then Rusty Lansford (CalTech) described a set of images he had put up on the screens. They’ve generated some modified quail (FP_expressing Tg quail) that they’re using – the eggs are easy to work with. They put different fluorescent proteins into different quail, and then breed them together. He had a very nice video of quail development with endothelial cells marked. Brighter cells are those about to enter M phase. There are also some great “4D” video that track the movements of the cells to form tha aorta. They’re pretty confident that they can follow cell division and cell orientation and shortly cell polarity. They’re happy to know what interests other people would be interested in terms of data and they’ll collect it for your models. Some words he used was: rolling, flow, differentiating, kiss-and-fuse, formation of organs and more. Many of the terms were subcellular and others were higher up (e.g. organs or tissue-level).
Nadine Peryeras (CNRS) from then discussed the Embryonics EC project, which reconstructs the cell lineage tree as the core of the “embryome”. They looked at 4 organisms, including the zebrafish. When computationally determining the cell shape, you (virtually) cut the embryo into bits to figure out the size. They have a number of algorithmic strategy for determining the position of each cell in the xyz axes. They can convert from total cell number to cell density if they have volume information. They have a video of the zebrafish virtual embryo, where the color shows the direction of migration. Very nice.
There were two other presentations about what their use-cases would be, but I was working on the list of terms from CBO.
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!