Bayesian inference of force dynamics during morphogenesis

Ishihara, Shuji; Sugimura, Kaoru

doi:10.1016/j.jtbi.2012.08.017

Cited by 183 publications

(312 citation statements)

References 32 publications

Supporting

Mentioning

300

Contrasting

Order By: Relevance

“…In our previous study, we formulated a Bayesian framework of force inference, in which all the cell junction tensions, differences in pressures among cells, and tissue stress are simultaneously inferred from the observed geometry of cells, up to a scaling factor (supplementary material Appendix S1). We have shown that inferred force and stress values are consistent with those obtained using other methods, such as laser ablation of cortical actin cables, quantification of myosin concentration and photo-elasticity (Nienhaus et al, 2009), and large-scale tissue ablation Ishihara and Sugimura, 2012;Ishihara et al, 2013). The global and noninvasive nature of the Bayesian force-inference method uniquely enables us to quantify space-time maps of force/stress in tissues and to relate the maps to hexagonal cell packing processes.…”

Section: Introductionsupporting

confidence: 68%

“…Preparation of samples of the Drosophila pupal wing and scutum for image collection was conducted as previously described (Shimada et al, 2006;Koto et al, 2009;Ishihara and Sugimura, 2012). Images were acquired using an inverted confocal microscope (FV1000D; Olympus) equipped with an Olympus 60×/NA1.2 SPlanApo water-immersion objective at 25°C unless otherwise noted.…”

Section: Image Collection and Analysismentioning

confidence: 99%

“…Such measurements have shed light on how cell shape and rearrangement are regulated by the activity and/or localization of force-generating molecular machinery within a cell (Hutson et al, 2003;Rauzi et al, 2008;Paluch and Heisenberg, 2009;. A new complementary approach to such subcellular and invasive measurements is force inference from cell shape and 1 connectivity, which offers cellular resolution and is both global and noninvasive (Chiou et al, 2012;Ishihara and Sugimura, 2012). In our previous study, we formulated a Bayesian framework of force inference, in which all the cell junction tensions, differences in pressures among cells, and tissue stress are simultaneously inferred from the observed geometry of cells, up to a scaling factor (supplementary material Appendix S1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The mechanical anisotropy in a tissue promotes ordering in hexagonal cell packing

2013

Self Cite

View full text Add to dashboard Cite

SUMMARYMany epithelial tissues pack cells into a honeycomb pattern to support their structural and functional integrity. Developmental changes in cell packing geometry have been shown to be regulated by both mechanical and biochemical interactions between cells; however, it is largely unknown how molecular and cellular dynamics and tissue mechanics are orchestrated to realize the correct and robust development of hexagonal cell packing. Here, by combining mechanical and genetic perturbations along with live imaging and Bayesian force inference, we investigate how mechanical forces regulate cellular dynamics to attain a hexagonal cell configuration in the Drosophila pupal wing. We show that tissue stress is oriented towards the proximal-distal axis by extrinsic forces acting on the wing. Cells respond to tissue stretching and orient cell contact surfaces with the stretching direction of the tissue, thereby stabilizing the balance between the intrinsic cell junction tension and the extrinsic force at the cell-population level. Consequently, under topological constraints of the two-dimensional epithelial sheet, mismatches in the orientation of hexagonal arrays are suppressed, allowing more rapid relaxation to the hexagonal cell pattern. Thus, our results identify the mechanism through which the mechanical anisotropy in a tissue promotes ordering in cell packing geometry.

show abstract

Section: Introductionsupporting

confidence: 68%

Section: Image Collection and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The mechanical anisotropy in a tissue promotes ordering in hexagonal cell packing

2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two avenues are being pursued. In one, computational methods are used to infer forces, up to a scaling factor, from observed tissue deformations 93 . Another approach that is currently being developed introduces genetically encoded fluorescence resonance energy transfer (FRET)-based force sensors within the tissue As we have argued above, mechanical features could contribute to cell competition caused by a growth differential.…”

Section: Box 2 | a Mechanical Model Of Epitheliamentioning

confidence: 99%

Mechanisms and mechanics of cell competition in epithelia

Vincent

Fletcher²,

Baena‐López

2013

Nat Rev Mol Cell Biol

123

121

View full text Add to dashboard Cite

“…Further, forces on cells increase during phases of proliferation and growth. Our findings may be of relevance in force-inference approaches that estimate forces using segmented microscopy images of epithelial tissues [58][59][60]. Force-inference methods often assume that the measured configuration of cells is in equilibrium and it is unclear to what extent force-inference approaches introduce errors if this is not the case.…”

Section: Discussionmentioning

confidence: 91%

Impact of implementation choices on quantitative predictions of cell-based computational models

Kursawe

Baker

Fletcher

2016

Preprint

View full text Add to dashboard Cite

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

show abstract

Bayesian inference of force dynamics during morphogenesis

Cited by 183 publications

References 32 publications

The mechanical anisotropy in a tissue promotes ordering in hexagonal cell packing

The mechanical anisotropy in a tissue promotes ordering in hexagonal cell packing

Mechanisms and mechanics of cell competition in epithelia

Impact of implementation choices on quantitative predictions of cell-based computational models

Contact Info

Product

Resources

About