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.