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Modelling Tissue Development. Rod Smallwood, Mike Holcombe, Sheila Mac Neil, Rod Hose, Richard Clayton (University of Sheffield), Jenny Southgate (University of York). The social behaviour of cells. How do these individual cells …. … assemble into this complex tissue?.
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Modelling Tissue Development Rod Smallwood, Mike Holcombe, Sheila Mac Neil, Rod Hose, Richard Clayton (University of Sheffield), Jenny Southgate (University of York)
The social behaviour of cells How do these individual cells … … assemble into this complex tissue?
Building an integrative systems biology: the Human Physiome Project • The aim of the Human Physiome Project is to provide a “quantitative description of physiological dynamics and functional behaviour of the intact organism” • it is overseen by the Physiome and Bioengineering Committee of the IUPS (International Union of Physiological Sciences) • projects include the Cardiome (heart), the Endotheliome (lining of blood vessels), Micro-circulation … • … and the Epitheliome – computational modelling of the social behaviour of (epithelial) cells
Where does cell modelling fit into the Physiome Project? The Epitheliome Cellular tissue 10-5m Social model of cell Hunter P, Robbins P, Noble D (2002) The IUPS human physiome project. Eur J Physiol 445 1-9
The social life of the cell is important! • Essential step from single-cell to multi-cellular organisms • Tissues and organs are self-assembling systems • No organising principle above the level of a single cell • so order is an emergent property of cellular interaction • This is a salient feature of biological systems - order emerges as the result of the interaction of large numbers of complex entities Courtesy of Sheila Mac Neil, Sheffield
Screening for epithelial cancers Contraction of skin grafts Wound healing What are the drivers? Courtesy of Dawn Walker & Sheila Mac Neil, Sheffield
What are the common features? • Self assembly/disassembly • Forces between cells • Cell motility • Cell signalling as a result of mechanical forces • Only an empirical understanding of the processes • e.g. differentiation at an air-liquid interface Courtesy of Sheila Mac Neil, Sheffield
From ants to epithelium • Existing models of tissue are either descriptive or derive function from structure • need a predictive model, not a descriptive model • in advance of healing, there is no structure in a wound, so need to develop structure from function • What paradigm can we use to model self-assembly of large numbers of very complex entities? • The basic idea came from workon the social behaviour of ants– we are interested in the socialbehaviour of cells • Two key insights were essential • a mechanism for integratingcellular biology into the‘social model’ • linking the ‘social model’ to aphysical model of the tissuebehaviour Courtesy of Francis Ratnieks, Sheffield
Simulation of monolayer growth Physiological Ca2+ (2mM) Low Ca2+ (0.09mM) NO. CELLS Ca2+ = 2mM Ca2+ = 0.09mM ITERATION NUMBER
in silico wound healing Physiological Ca2+ (2mM) Low Ca2+ (0.09mM)
in vitro wound healing Physiological Ca2+ Low Ca2+ (Cell movie from Gemma Hill, Jack Birch Unit for Molecular Carcinogenesis,University of York)
Major challenges • Developing a ‘realistic’ physical model that is computationally tractable for ~106 cells • Deciding what is important - sparseness (parsimony) • Linking individual cell dynamics to a continuum model of tissue • how does stress at the tissue level affectmechano-transduction at the cytoskeletallevel • how is the signalling resulting from a woundrelated to cellular-level response • Comparing tissue growth in vitro and in silico • how do we validate the computational model Balaban et al 2001 Nature Cell Biology 3 466
Summary • We have developed a proof-of-concept model of the social behaviour of cells • The model shows similar behaviour to urothelial cells grown in vitro • In principle, the model: • can incorporate the biological mechanisms which control cell behaviour • can be scaled up to realistic numbers of cells • In practice, sparseness will be essential! • The model is changing biologists’ thinking and driving biological experiments • Strong validation is essential
Acknowledgements Cell biology: Jenny Southgate (York) Sheila Mac Neil Eva Qwarnstrom Modelling: Mike Holcombe Dawn Walker Steven Wood Engineering: Rod Hose Peter Hunter (Auckland) Funding: Engineering & Physical Sciences Research Council (EPSRC) Higher Education Funding Council for England (HEFCE)
