New modeling approaches to investigate cell signaling in radiation response
1,2
Plante ,
1,2
Ponomarev
1
Cucinotta
Ianik
Artem L.
and Francis A.
1NASA/JSC, Houston, TX; 2Division of Space Life Sciences, Universities Space Research Association, Houston, TX
Introduction
Tissue or cell culture model
• Following radiation exposure, a flow of information is
exchanged between cells in tissues, and cells not
directly hit are also affected [1].
• These so-called non-targeted effects (NTE) may have
important consequences. Therefore, several elements
should be included in irradiated tissue models:
• Stochastic track structure and dosimetry
• Tissue or cell culture model
• DNA damage and repair models
• Brownian dynamics algorithms for the simulation of
signaling molecules in the micro-environment
• Cell signaling pathways
• Most tissue and cell culture models are based on
Voronoi tessellation (in 2D and 3D)
• A Voronoi cell is the space closest to a given point
(than the other points)
• Some rules are added:
• Diameter limits (min and max)
• Contact energy: harmonic oscillator
• Models derived from microscopic images
can also be used
Signaling molecules: TGFβ
Data for TGFβ
Parameter
D
A
h
ke
kon
kd
R0
Rcell
Ncells
Description
Ligand diffusivity
Culture surface
Height of the extracellular medium
Complex internalization rate constant
Forward binding rate constant
Complex dissociation rate constant
Number of receptors at cell surface
Radius of cells
Number of cells in culture
Image of a cell culture (120 µm x
120 µm) irradiated with 30
56Fe26+ ions, 1 GeV/amu, LET
~150 keV/µm. Dose: ~5 cGy
Top: a Voronoi cell in 2D
Botton: a cell culture simulated with modified Voronoi cells
Value
2.6x10-7 cm2/s
10 cm2
0.2 cm
3 min-1 = 0.05 s-1
(2.3±0.2)x107 M-1s-1
(1.5±0.2)x10-4 s-1
1000
0.0025 cm
100000
Equations of the model
ka
A+B
→
←
ke
(AB) * → AB
kb
∂p(x, t | x 0 )
∂2
= D 2 p(x, t | x 0 )
∂t
∂x
ka: association rate constant,
kd: dissocation rate constant:
ke: signal transduction rate constant.
dp(*, t | x 0 )
= k a p(0, t | x 0 ) − (k d + k e )p(*, t | x 0 )
dt
Brownian dynamics for a particle in a cell culture near cells comprising receptors.
Left: particle in the cell culture. Right: Ligand-receptor interaction near a cell
membrane.
• Free diffusion in the Y and Z directions
• Reflective boundary on top
• Homogenization of the boundary at the bottom surface of
culture (partially absorbing)
• Possible states for a ligand molecule
• Free at position x0
• Bound in the reversible state (*)
• Initiation of signal transduction(**)
Radiation track structure and evolution in time
• BD algorithms (in 3D) describing the
relative diffusion of two radiolytic
species with possible chemical
reactions have been developed [7]
• These algorithms can be used to
simulate the time evolution of the
radiolytic
species
(radiation
chemistry) [8]
• The BD algorithms will be modified to
include the activation of TGFβ
molecules and binding to their
receptors
• The energy deposition by the radiation is highly
dependent on the radiation type and energy and leads
to the formation of the track structure.
• Many radiolytic species (H., .OH,
H2, H2O2, e-aq, etc) are formed
during this process.
• The radiation track structure is
simulated using the Monte-Carlo
code RITRACKS [2].
Brownian dynamics (BD) algorithms
• Among signaling molecules involved in the response of
cells to ionizing radiation, TGFβ is of particular interest
• TGFβ is secreted as an inactive form (the Large Latent
Complex “LLC”) [3]; it is released from the LLC by several
factors, notably the .OH radical [4].
• TGFβ binds to its receptors and initiate several actions
mediated by the SMAD proteins; it has been shown to
suppress apoptosis in irradiated cell culture and also to
mediate cellular response to DNA damage [5].
TGFβ signaling pathways
Simulation of radiation track and dose calculations
Time evolution, in 3D, of a 24-MeV 4He2+, LET~26 keV/µm,
at 10-13, 10-9, 10-7 and 10-6 s. Each dot is a radiolytic species
Conclusion and perspectives
Results: 1 membrane [6]
p(x,t|x0)
Q(t|x0), p(*|x0), p(**,t|x0)
p(x,t|x0): Probability distribution of distances for free particle
Q(t|x0): Survival probability
p(*,t|x0): Reversible binding probability
p(**,t|x0): Signal transduction probability
Conclusion and perspectives
• The simulations will be used to calculate:
• These models and simulations should help
role of cell signaling in the
• The number and position of activated TGFβ understand the
response to ionizing radiation
molecules in a cell culture following irradiation
• The concentration of TGFβ as a function of time
References
• The number and positions of activated receptors
[1] Mothersill, C. and Seymour, C.B. Nat. Rev. Cancer 4, 158–164 (2004).
• These simulations may be useful to understand how [2] Plante, I. and Cucinotta, F.A. New J. Phys. 10, 125020 (2008).
[3] Annes, J. P. et al. J. Cell Science 116, 217-224 (2003).
TGFβ affects DNA repair [5, 9]
[4] Jobling, M. F. et al. Radiat. Res. 166, 839-848 (2006).
• They will eventually be merged with models [5] Ewan, K. B. et al. Cancer Res. 62, 5627-5631 (2002).
[6] Plante, I. and Cucinotta, F. A. Submitted.
combining stochastic radiation track simulations, cell [7] Plante, I. Radiat. Env. Biophys. 50, 389-403 (2011).
culture or tissue models, DNA damage and repair [8] Plante, I. Radiat. Env. Biophys. 50, 405-415 (2011).
[9] Cucinotta, F. A. et al. Radiat. Res. 169, 214-222 (2008).
kinetics, cell signaling pathways and BD algorithms.