Journal
ROYAL SOCIETY OPEN SCIENCE
Volume 5, Issue 8, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsos.180384
Keywords
approximate Bayesian computation; cell spreading; experimental design; cell migration; cell proliferation
Categories
Funding
- UK's Engineering and Physical Sciences Research Council (EPSRC) through a studentship at the Systems Biology programme of the University of Oxford's Doctoral Training Centre [EP/G03706X/1]
- Australian Research Council [DP170100474]
- Royal Society
- Leverhulme Trust
- UK's Biotechnology and Biological Sciences Research Council (BBSRC) [BB/R000816/1]
- BBSRC [BB/R000816/1] Funding Source: UKRI
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To better understand development, repair and disease progression, it is useful to quantify the behaviour of proliferative and motile cell populations as they grow and expand to fill their local environment. Inferring parameters associated with mechanistic models of cell colony growth using quantitative data collected from carefully designed experiments provides a natural means to elucidate the relative contributions of various processes to the growth of the colony. In this work, we explore how experimental design impacts our ability to infer parameters for simple models of the growth of proliferative and motile cell populations. We adopt a Bayesian approach, which allows us to characterize the uncertainty associated with estimates of the model parameters. Our results suggest that experimental designs that incorporate initial spatial heterogeneities in cell positions facilitate parameter inference without the requirement of cell tracking, while designs that involve uniform initial placement of cells require cell tracking for accurate parameter inference. As cell tracking is an experimental bottleneck in many studies of this type, our recommendations for experimental design provide for significant potential time and cost savings in the analysis of cell colony growth.
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