part 2: simulating cell motility using cpm

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Part 2: Simulating cell motility using CPM

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Page 1: Part 2: Simulating cell motility using CPM

Part 2: Simulating cell motility using CPM

Page 2: Part 2: Simulating cell motility using CPM

Shape change and motility

Resting cell

Chemical polarization

“Front”: (protrusion)

“Rear”: (contraction)

Shape change

Page 3: Part 2: Simulating cell motility using CPM

What are the overarching questions?

•  How is the shape and motility of the cell regulated?

•  What governs cell morphology, and why does it differ over different cell types?

•  How do cells polarize, change shape, and initiate motility?

•  How do they maintain their directionality? •  How can they respond to new signals? •  How do they avoid getting stuck?

Page 4: Part 2: Simulating cell motility using CPM

Types of models

•  Fluid-based •  Mechanical (springs, dashpots, elastic

sheets) •  Chemical (reactions in deforming domain) •  Other (agent-based, filament based, etc)

Page 5: Part 2: Simulating cell motility using CPM

Types of models

•  Fluid-based •  Mechanical (springs, dashpots, elastic

sheets) •  Chemical (reactions in deforming domain) •  Other (agent-based, filament based, etc)

Page 6: Part 2: Simulating cell motility using CPM

Marée AFM, Jilkine A, Dawes AT, Greineisen VA, LEK (2006) Bull Math Biol, 68(5):1169-1211.

AFM Maree V Grieneisen

CPM: Stan Marée

Mare ́e AFM, Grieneisen VA, Edelstein-Keshet L (2012) How Cells Integrate Complex Stimuli: The Effect of Feedback from Phosphoinositides and Cell Shape on Cell Polarization and Motility. PLoS Comput Biol 8(3): e1002402. doi:10.1371/journal.pcbi.1002402

Page 7: Part 2: Simulating cell motility using CPM

Signaling “layers”

Actin filaments

Barbed ends Arp2/3 Cell

protrusion

Cdc42 Rac Rho

(uncap)

Myosin Rear retraction

(WASp)

(WAVE) (PIP2) (ROCK)

Represent reaction-diffusion and actin growth/nucleation in a 2D simulation of a “motile cell”

Page 8: Part 2: Simulating cell motility using CPM

More recently:

Mare ́e AFM, Grieneisen VA, Edelstein-Keshet L (2012). PLoS Comput Biol 8(3): e1002402. doi:10.1371/journal.pcbi.1002402

Page 9: Part 2: Simulating cell motility using CPM

2D cell motility using Potts model formalism

“Thin sheet”

2D

Page 10: Part 2: Simulating cell motility using CPM

Discretize using hexagonal grid

•  compute actin density at 6 orientations

•  allow for branching by Arp2/3

Cell interior

Cell exterior

6 Filament orientations

Page 11: Part 2: Simulating cell motility using CPM

Hamiltonian based computation:

Pushing actin ends

Rho, Myosin contraction

Cell volume Too small

Fig: revised & adapted from: Segel, Lee A. (2001) PNAS

Cell interior

Cell exterior

Cell volume too big

Page 12: Part 2: Simulating cell motility using CPM

Protrusion

Pushing actin ends

Rho, Myosin contraction

Cell volume Too small Cell volume

too big

Cell interior

Cell exterior

Page 13: Part 2: Simulating cell motility using CPM

Pushing actin ends

Rho, Myosin contraction

Cell volume Too small

Cell volume too big

Fig: revised & adapted from: Segel, Lee A. (2001) PNAS

Cell interior

Cell exterior

Page 14: Part 2: Simulating cell motility using CPM

Retraction

Pushing actin ends

Rho, Myosin contraction

Cell volume Too small

Cell volume too big

Cell interior

Cell exterior

Page 15: Part 2: Simulating cell motility using CPM

Each hexagonal site contains:

6 Filament orientations

6 barbed end orientations

Cdc42 (active, inactive) Rac (active, inactive) Rho (active, inactive) Arp2/3

PIP, PIP2, PIP3

Actin filaments

Barbed ends Arp2/3 Cell

protrusion

Cdc42 Rac Rho

Myosin Rear retraction

Page 16: Part 2: Simulating cell motility using CPM

Resting vs stimulated cell

Page 17: Part 2: Simulating cell motility using CPM

Cdc42 distribution

Low High

Page 18: Part 2: Simulating cell motility using CPM

Cdc42, Rac, Rho

Low High

Page 19: Part 2: Simulating cell motility using CPM

Cdc42 Rac Rho

high

low

Distribution of internal biochemistry

And actin:

Cdc42 Rac Rho

Page 20: Part 2: Simulating cell motility using CPM

Filaments, Arp2/3, Tips

Low High

Page 21: Part 2: Simulating cell motility using CPM

Actin Filaments

Cytoskeleton

Page 22: Part 2: Simulating cell motility using CPM

Turning behaviour

http://theory.bio.uu.nl/stan/keratocyte/

Shallow gradient

Steep gradient

Page 23: Part 2: Simulating cell motility using CPM

Turning behaviour

http://theory.bio.uu.nl/stan/keratocyte/

Shallow gradient

Steep gradient

Page 24: Part 2: Simulating cell motility using CPM

Variety of shape and motility phenotypes

Rho

indu

ced

cont

ract

ility

Page 25: Part 2: Simulating cell motility using CPM

Effect of shape

•  cell can repolarize whether or not its shape is allowed to evolve

•  when shape is dynamic, reaction to new stimuli is much more rapid

Page 26: Part 2: Simulating cell motility using CPM

What the lipids do: fine tuning

Page 27: Part 2: Simulating cell motility using CPM

. PLoS Comput Biol 8(3): e1002402. doi:10.1371

Page 28: Part 2: Simulating cell motility using CPM

Pushing barbed ends: extension

Mare ́e AFM, Grieneisen VA, Edelstein-Keshet L (2012) How Cells Integrate Complex Stimuli: The Effect of Feedback from Phosphoinositides and Cell Shape on Cell Polarization and Motility. PLoS Comput Biol 8(3): e1002402. doi:10.1371/journal.pcbi.1002402

Page 29: Part 2: Simulating cell motility using CPM

Pushing barbed ends: retraction

Pushing barbed ends: extension . PLoS Comput Biol 8(3): e1002402. doi:10.1371

Mare ́e AFM, Grieneisen VA, Edelstein-Keshet L (2012) How Cells Integrate Complex Stimuli: The Effect of Feedback from Phosphoinositides and Cell Shape on Cell Polarization and Motility. PLoS Comput Biol 8(3): e1002402. doi:10.1371/journal.pcbi.1002402

Page 30: Part 2: Simulating cell motility using CPM

From Jun Allard’s Lecture 5: (Simulating membrane mechanics)

Page 31: Part 2: Simulating cell motility using CPM

CPM Metropolis:

1.  Choose edge site at random 2.  Propose to extend or retract 3.  Compute new H 4.  If ΔH < -Hb keep this move 5.  If ΔH ≥ -Hb accept move with probability

6.  Iterate over each lattice site randomly

Page 32: Part 2: Simulating cell motility using CPM

Hamiltonian and Energy minimization

•  Energy of cell interface •  of area expansion •  of perimeter change

Page 33: Part 2: Simulating cell motility using CPM

Effective forces

•  Effect of pushing barbed ends •  of myosin contraction

Page 34: Part 2: Simulating cell motility using CPM

CPM parameters

Page 35: Part 2: Simulating cell motility using CPM

“Temperature”

•  This parameter governs the fluctuation intensity

•  Note edge of “cell” thereby fluctuates:

Page 36: Part 2: Simulating cell motility using CPM

Relationship between v and b: edge protrusion and barbed end density

•  Consider case of no capping, no branching •  Suppose fraction (1-f) barbed ends pushing,

and fraction f are not. •  Probability to extend and to retract:

Page 37: Part 2: Simulating cell motility using CPM

Protrusion speed

•  Effective speed of protrusion:

Page 38: Part 2: Simulating cell motility using CPM

Mean velocity related to fraction f:

•  Mean velocity = v = f v0

•  =

•  f =v / v0

Page 39: Part 2: Simulating cell motility using CPM

CPM Parameters T and Hb “tuned” to known relationship of v to b

•  CPM formula:

•  “known” relationship

Page 40: Part 2: Simulating cell motility using CPM

CPM Pluses

•  Reasonably “easy” fast computations allow for more detailed biochemistry

•  Captures fluctuations well •  Can be tuned to behave like thermal-ratchet

based protrusion •  Easily extended to multiple interacting cells

Page 41: Part 2: Simulating cell motility using CPM

CPM minuses

•  Mechanical forces not explicitly described •  Interpretation of CPM parameters less direct •  No representation of fluid properties of cell

interior, exterior •  Controversy of application of Metropolis

algorithm to non-equilibrium situations.

Page 42: Part 2: Simulating cell motility using CPM

Comparative study

•  CPM Mechanical cells

Andasari V, Roper RT, Swat MH, Chaplain MAJ (2012) Integrating Intracellular Dynamics Using CompuCell3D and Bionetsolver: Applications to Multiscale Modelling of Cancer Cell Growth and Invasion. PLoS ONE 7(3): e33726. doi:10.1371/journal.pone.0033726