Abstract
Recent work has indicated that the shape and size of a cell can influence how a cell spreads, develops focal adhesions, and exerts forces on the substrate. However, it is unclear how cell shape regulates these events. Here we present a computational model that uses cell shape to predict the magnitude and direction of forces generated by cells. The predicted results are compared to experimentally measured traction forces, and show that the model can predict traction force direction, relative magnitude, and force distribution within the cell using only cell shape as an input. Analysis of the model shows that the magnitude and direction of the traction force at a given point is proportional to the first moment of area about that point in the cell, suggesting that contractile forces within the cell act on the entire cytoskeletal network as a single cohesive unit. Through this model, we demonstrate that intrinsic properties of cell shape can facilitate changes in traction force patterns, independently of heterogeneous mechanical properties or signaling events within the cell.
| Original language | English (US) |
|---|---|
| Journal | Biophysical Journal |
| Volume | 99 |
| Issue number | 9 |
| DOIs | |
| State | Published - Nov 3 2010 |
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ASJC Scopus subject areas
- Biophysics
Cite this
A predictive model of cell traction forces based on cell geometry. / Lemmon, Christopher A.; Romer, Lewis.
In: Biophysical Journal, Vol. 99, No. 9, 03.11.2010.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A predictive model of cell traction forces based on cell geometry
AU - Lemmon, Christopher A.
AU - Romer, Lewis
PY - 2010/11/3
Y1 - 2010/11/3
N2 - Recent work has indicated that the shape and size of a cell can influence how a cell spreads, develops focal adhesions, and exerts forces on the substrate. However, it is unclear how cell shape regulates these events. Here we present a computational model that uses cell shape to predict the magnitude and direction of forces generated by cells. The predicted results are compared to experimentally measured traction forces, and show that the model can predict traction force direction, relative magnitude, and force distribution within the cell using only cell shape as an input. Analysis of the model shows that the magnitude and direction of the traction force at a given point is proportional to the first moment of area about that point in the cell, suggesting that contractile forces within the cell act on the entire cytoskeletal network as a single cohesive unit. Through this model, we demonstrate that intrinsic properties of cell shape can facilitate changes in traction force patterns, independently of heterogeneous mechanical properties or signaling events within the cell.
AB - Recent work has indicated that the shape and size of a cell can influence how a cell spreads, develops focal adhesions, and exerts forces on the substrate. However, it is unclear how cell shape regulates these events. Here we present a computational model that uses cell shape to predict the magnitude and direction of forces generated by cells. The predicted results are compared to experimentally measured traction forces, and show that the model can predict traction force direction, relative magnitude, and force distribution within the cell using only cell shape as an input. Analysis of the model shows that the magnitude and direction of the traction force at a given point is proportional to the first moment of area about that point in the cell, suggesting that contractile forces within the cell act on the entire cytoskeletal network as a single cohesive unit. Through this model, we demonstrate that intrinsic properties of cell shape can facilitate changes in traction force patterns, independently of heterogeneous mechanical properties or signaling events within the cell.
UR - http://www.scopus.com/inward/record.url?scp=78249241829&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78249241829&partnerID=8YFLogxK
U2 - 10.1016/j.bpj.2010.09.024
DO - 10.1016/j.bpj.2010.09.024
M3 - Article
VL - 99
JO - Biophysical Journal
JF - Biophysical Journal
SN - 0006-3495
IS - 9
ER -