Engineering topics

Plante, Ianik

Publications and source records attributed to Plante, Ianik.

39 records · Page 3

NASA NTRS2009

Simulation of TGF-Beta Activation by Low-Dose HZE Radiation in a Cell Culture

High charge (Z) and energy (E) (HZE) nuclei comprised in the galactic cosmic rays are main contributors to space radiation risk. They induce many lesions in living matter such as non-specific oxidative damage and the double-strand breaks (DSBs), which are considered key precursors of early and late effects of radiation. There is increasing evidence that cells respond collectively rather than individually to radiation, suggesting the importance of cell signaling1. The transforming growth factor (TGF ) is a signaling peptide that is expressed in nearly all cell type and regulates a large array of cellular processes2. TGF have been shown to mediate cellular response to DNA damage3 and to induce apoptosis in non-irradiated cells cocultured with irradiated cells4. TFG molecules are secreted by cells in an inactive complex known as the latency-associated peptide (LAP). TGF is released from the LAP by a conformational change triggered by proteases, thrombospondin-1, integrins, acidic conditions and .OH radical5. TGF then binds to cells receptors and activates a cascade of events mediated by Smad proteins6, which might interfere with the repair of DNA. Meanwhile, increasingly sophisticated Brownian Dynamics (BD) algorithms have appeared recently in the literature7 and can be applied to study the interaction of molecules with receptors. These BD computer models have contributed to the elucidation of signal transduction, ligand accumulation and autocrine loops in the epidermal growth factor (EGF) and its receptor (EFGR) system8. To investigate the possible roles of TGF in an irradiated cell culture, our Monte-Carlo simulation codes of the radiation track structure9 will be used to calculate the activation of TFG triggered by .OH produced by low doses of HZE ions. The TGF molecules will then be followed by a BD algorithm in a medium representative of a cell culture to estimate the number of activated receptors.

Plante, Ianik↗

NASA NTRS2009

Development of a GCR Event-based Risk Model

A goal at NASA is to develop event-based systems biology models of space radiation risks that will replace the current dose-based empirical models. Complex and varied biochemical signaling processes transmit the initial DNA and oxidative damage from space radiation into cellular and tissue responses. Mis-repaired damage or aberrant signals can lead to genomic instability, persistent oxidative stress or inflammation, which are causative of cancer and CNS risks. Protective signaling through adaptive responses or cell repopulation is also possible. We are developing a computational simulation approach to galactic cosmic ray (GCR) effects that is based on biological events rather than average quantities such as dose, fluence, or dose equivalent. The goal of the GCR Event-based Risk Model (GERMcode) is to provide a simulation tool to describe and integrate physical and biological events into stochastic models of space radiation risks. We used the quantum multiple scattering model of heavy ion fragmentation (QMSFRG) and well known energy loss processes to develop a stochastic Monte-Carlo based model of GCR transport in spacecraft shielding and tissue. We validated the accuracy of the model by comparing to physical data from the NASA Space Radiation Laboratory (NSRL). Our simulation approach allows us to time-tag each GCR proton or heavy ion interaction in tissue including correlated secondary ions often of high multiplicity. Conventional space radiation risk assessment employs average quantities, and assumes linearity and additivity of responses over the complete range of GCR charge and energies. To investigate possible deviations from these assumptions, we studied several biological response pathway models of varying induction and relaxation times including the ATM, TGF -Smad, and WNT signaling pathways. We then considered small volumes of interacting cells and the time-dependent biophysical events that the GCR would produce within these tissue volumes to estimate how GCR event rates mapped to biological signaling induction and relaxation times. We considered several hypotheses related to signaling and cancer risk, and then performed simulations for conditions where aberrant or adaptive signaling would occur on long-duration space mission. Our results do not support the conventional assumptions of dose, linearity and additivity. A discussion on how event-based systems biology models, which focus on biological signaling as the mechanism to propagate damage or adaptation, can be further developed for cancer and CNS space radiation risk projections is given.

Cucinotta, Francis A.↗

NASA NTRS2009

3D Visualization of Monte-Carlo Simulation's of HZE Track Structure and Initial Chemical Species

Heavy ions biophysics is important for space radiation risk assessment [1] and hadron-therapy [2]. The characteristic of heavy ions tracks include a very high energy deposition region close to the track (<20 nm) denoted as the track core, and an outer penumbra region consisting of individual secondary electrons (6-rays). A still open question is the radiobiological effects of 6- rays relative to the track core. Of importance is the induction of double-strand breaks (DSB) [3] and oxidative damage to the biomolecules and the tissue matrix, considered the most important lesions for acute and long term effects of radiation. In this work, we have simulated a 56Fe26+ ion track of 1 GeV/amu with our Monte-Carlo code RITRACKS [4]. The simulation results have been used to calculate the energy depiction and initial chemical species in a "voxelized" space, which is then visualized in 3D. Several voxels with dose >1000 Gy are found in the penumbra, some located ~0.1 mm from the track core. In computational models, the DSB induction probability is calculated with radial dose [6], which may not take into account the higher RBE of electron track ends for DSB induction. Therefore, these simulations should help improve models of DSB induction and our understanding of heavy ions biophysics.

Plante, Ianik↗