Part 2: Simulating cell motility using CPM ! Shape change and - - PowerPoint PPT Presentation

part 2 simulating cell motility using cpm shape change
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Part 2: Simulating cell motility using CPM ! Shape change and - - PowerPoint PPT Presentation

Part 2: Simulating cell motility using CPM ! Shape change and motility ! Resting cell ! Chemical polarization ! Rear: ! Front: ! (contraction) ! (protrusion) ! Shape change ! What are the overarching questions? ! How is the shape and


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SLIDE 1

Part 2: Simulating cell motility using CPM!

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SLIDE 2

Shape change and motility!

Resting cell!

Chemical polarization! “Front”: !

(protrusion)!

“Rear”: !

(contraction)!

Shape change!

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SLIDE 3

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?!
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SLIDE 4

Types of models!

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

sheets)!

  • Chemical (reactions in deforming domain)!
  • Other (agent-based, filament based, etc)!
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SLIDE 5

Types of models!

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

sheets)!

  • Chemical (reactions in deforming domain)!
  • Other (agent-based, filament based, etc)!
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SLIDE 6

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!

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SLIDE 7

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”!

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SLIDE 8

More recently:!

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

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SLIDE 9

2D cell motility using Potts model formalism!

“Thin sheet”! 2D!

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SLIDE 10

Discretize using hexagonal grid !

  • compute actin density at 6
  • rientations!
  • allow for branching by

Arp2/3!

Cell interior! Cell exterior!

6 Filament

  • rientations!
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SLIDE 11

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!

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SLIDE 12

Protrusion !

Pushing ! actin ends! Rho, Myosin ! contraction! Cell volume ! Too small! Cell volume ! too big!

Cell interior! Cell exterior!

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SLIDE 13

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!

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SLIDE 14

Retraction !

Pushing ! actin ends! Rho, Myosin ! contraction! Cell volume ! Too small! Cell volume ! too big!

Cell interior! Cell exterior!

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SLIDE 15

Each hexagonal site contains:!

6 Filament

  • rientations!

6 barbed end

  • rientations!

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 !

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SLIDE 16

Resting vs stimulated cell

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SLIDE 17

Cdc42 distribution!

Low ! High!

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SLIDE 18

Cdc42, Rac, Rho!

Low ! High!

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SLIDE 19

Cdc42 Rac Rho!

high! low!

Distribution of internal biochemistry

And actin:!

Cdc42 Rac Rho!

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SLIDE 20

Filaments, Arp2/3, Tips!

Low High!

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SLIDE 21

Actin Filaments!

Cytoskeleton!

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SLIDE 22

Turning behaviour!

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

Shallow gradient! Steep gradient!

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SLIDE 23

Turning behaviour!

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

Shallow gradient! Steep gradient!

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SLIDE 24

Variety of shape and motility phenotypes

Rho induced contractility!

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SLIDE 25

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!

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SLIDE 26

What the lipids do: fine tuning!

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SLIDE 27

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

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SLIDE 28

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!

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SLIDE 29

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!

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SLIDE 30

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

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CPM Metropolis:!

  • 1. Choose edge site at random!
  • 2. Propose to extend or retract!
  • 3. Compute new H!
  • 4. If #H < -Hb keep this move!
  • 5. If #H $ -Hb accept move with probability!
  • 6. Iterate over each lattice site randomly !
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Hamiltonian and Energy minimization!

  • Energy of cell interface!
  • of area expansion!
  • of perimeter change !
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Effective forces!

  • Effect of pushing barbed ends!
  • of myosin contraction!
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SLIDE 34

CPM parameters!

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“Temperature”!

  • This parameter governs the fluctuation intensity!
  • Note edge of “cell” thereby fluctuates:!
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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:!
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SLIDE 37

Protrusion speed!

  • Effective speed of protrusion:!
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SLIDE 38

Mean velocity related to fraction f:!

  • Mean velocity = v = f v0!
  • =!
  • f =v / v0!
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SLIDE 39

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

  • CPM formula:!
  • “known” relationship!
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SLIDE 40

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!
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SLIDE 41

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.!

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SLIDE 42

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!