CECAM workshop: The self-organised cytoplasm

Lausanne, Switzerland
The cytoplasm and nucleoplasm of cells has a highly dynamic mesoscale structure. We define the mesoscale as being from of order a hundred nanometres to the size of the cell, which is typically of order 10 micrometres for eukaryote cells. Recent work has shown that we have been dramatically underestimating the extent of this mesoscale self-organisation, and its role in key processes of the cell function. This unique workshop will bring together the interdisciplinary group of scientists needed to advance this field.

We break our outline of the state of the art into 4 sections: 1) Functional liquid droplets in cells, 2) Self-organised transport, forces and flow in cells, 3) Protein solution phase behaviour in vitro and in silico, and 4) Statistical physics and fluid mechanics of active matter.

In each area we will try and set out the state of the art, and indicate how we think this can be advanced by the collaborations we propose to encourage in the workshop.

1) Functional liquid droplets in cells. The cytoplasm is very far from uniform and structureless. Liquid-droplet-like structures have been studied in many cells. Examples are P granules (Brangwynne, 2009), stress granules (Ruggieri, 2012), the puncta of cell-signalling proteins such as Dishevelled (Fiedler 2011; Sear, 2007), and droplets of the anti-viral protein TRIM5alpha that appear to bind to HIV capsids (Campbell, 2007). Motor transport and droplets can operate in tandem, for example, droplets can be transported by motors (Campbell, 2007; Brangwynne, 2009). These self-organised droplet-like structures are known to be functional (disrupting them can alter function). But how most of this self-organisation contributes to function is unknown. Modern experimental techniques can now provide quantitative information on key properties such as turnover rates, but simple models and computer simulations are lacking. Systems biology approaches are needed for many aspects of cell behaviour, but these use ODEs or in some cases multiple static compartments (Lloyd-Lewis, 2013). Here, improved modelling of the droplets would allow us to advance the state of the art in systems biology approaches to these systems. There are models and simulations on phase separation controlled by active processes dating back to the 1990s (Glotzer, 1995) but these have not been used to model data from cell biology experiments. There are also ‘whole cell’ models – models with an explicit spatially-resolved whole cell volume. They have been applied to study systems with protein clusters (DePristo, 2009). But so far these have only been applied to bacteria (DePristo, 2009), not to the much larger and more complex, and hence much more computationally demanding, eukaryote cells.

2) Self-organised transport, forces and flow in cells. Cells contain a dynamic cytoskeleton, with actively polymerising and depolymerising actin filaments and microtubules, each with molecular motors moving along them. These have been shown by cell biologists to be used by the cell to localise multiple entities in the cytoplasm, such as mRNA (Holt, 2009) and P granules (Brangwynne, 2009). This is typically by towing a cargo that is directly attached to the motor, but motors can also induce flows (Ganguly, 2012), which in turn affect transport and localisation (Trong, 2012). All these processes dramatically affect the rates at which protein molecules encounter each other in the cytoplasm. However, almost all modelling in systems biology assumes a well-mixed uniform cytoplasm in which molecules encounter each other at a rate that scales as the number of protein molecules divided by the total volume of the cytoplasm (Lloyd-Lewis, 2013). In many cases active processes associated with the cytoskeleton mean that this is not correct. A systems biology approach is essential to model many key aspects of cell behaviour such as proliferation and differentiation but the ODE models which are the state of the art here are clearly very crude approximations. More realistic models of the behaviour are required, and they need to be rooted in an understanding of the basic physics of the self-organised cytoplasm, to ensure that they are generalisable and robust.

3) Protein solution phase behaviour in vitro and in silico. Protein solutions often undergo liquid/liquid phase separation, forming liquid droplets. How this relates to protein interactions has been studied (Voets, 2012; Zhang, 2012). This experimental work on protein solutions has inspired theoretical and computer simulation work to calculate not only the phase behaviour of models of proteins, but also non-equilibrium phenomena such as gelation (Rovigatti, 2013; Dorsaz, 2011). This combination of computer simulation and experiment has resulted in a good basic understanding of the interactions of at least some simple proteins in solution, and how these interactions control behaviour such as the formation of droplets. This is now ready to be extended to the in vivo situation. But to do this, collaborations with cell biologists are required.

4) Statistical physics and fluid mechanics of active matter. This is a rapidly advancing field of physics. Our understanding of the fundamentals of the behaviour of these systems has matured greatly over the last 10 years; see the review of Marchetti et al. (2012). This has required the development of advanced mesoscopic simulation tools. The knowledge developed in past decades is now ready to be applied to a wider range of problems in cell biology. In particular we believe that it is now ready to contribute to improving the models of dynamic cell processes in systems biology models. For example, to replace simple second order reaction ODEs with more accurate expressions for reactions in the dynamic self-organised cytoplasm. Also, computer simulations have recently been applied to study the collective dynamics of transport on a crowded cytoskeleton (Neri, 2013), and interesting results such as the spontaneous formation of dynamic heterogeneties have been found. But these results have not yet been applied directly to biological problems such as mRNA transport and localisation in cells.

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