A dynamic model for the p53 stress response networks under ion radiation

Abstract

P53 controls the cell cycle arrest and cell apoptosis through interaction with the downstream genes and their signal pathways. To stimulate the investigation into the complicated responses of p53 under the circumstance of ion radiation (IR) in the cellular level, a dynamic model for the p53 stress response networks is proposed. The model can be successfully used to simulate the dynamic processes of generating the double-strand breaks (DSBs) and their repairing, ataxia telangiectasia mutated (ATM) activation, as well as the oscillations occurring in the p53-MDM2 feedback loop.

A Hybrid Model for Tumor Radiotherapy Based on Kinetic Theory and Gene-Environment Network

“…In this simulation, the initial number of resulting DSBs per time scale is dealt as proportional number generated by Poisson random function with a mean of 35x, in which x is the strength of IR dose [5], [6]. First, we apply IR=3Gy as an example from these radiotherapy strategies above.…”

“…Currently, some combined approaches of information science, control theory, and system biology are invoking new ideas on the investigation of complicated mechanisms of bio-system at single cell level [5]. Some theoretical methods have been proposed to investigate cellular self-defensive mechanism under genome stress, such as Gene Regulatory Network (GRN) models [6]. In addition, Gene-Environment Network (GEN) model, a novel mathematical framework, has been widely investigated by using the kinetic theory of active particle (KTAP) [7]- [9].…”

A Framework for Modeling the Cellular Defending Mechanisms Against Genome Stress Under Radiotherapy

“…Under the genome stresses, many efforts have been made to enhance P53-mediated transcription through some models [58,59] [9][10][11][12]. However, the interactions in a real system would make these models [60] extremely complicated.…”

“…Compared with the previous models [9][10][11][12], the current model contains more vital components, such as oncogenes, ARF and mP53, as well as their related regulating pathways. In the DSBs generation and repair module, the acute IR induces DSBs stochastically and forms DSB-protein complexes (DSBCs) at each of the damage sites after interacting with the DNA repair proteins [2,3].…”

Organisms can essentially be classified according to two codon patterns

“…Cellular automaton images (Wolfram 1984(Wolfram , 2002 have also been used to represent biological sequences (Xiao et al 2005b), to predict protein subcellular localization (Xiao et al 2006b), predict transmembrane regions in proteins (Diao et al 2008), predict the effect on replication ratio by HBV virus gene missense mutation (Xiao et al 2005a), and to study hepatitis B viral infections (Xiao et al 2006a). Graphic approaches have been used recently to represent DNA sequences (for example, Qi et al 2007b), investigate p53 stress response networks (Qi et al 2007a), analyze the network structure of the amino acid metabolism (Shikata et al 2007), study cellular signaling networks (Diao et al 2007) and proteomics (González-Díaz et al 2008), and for a systematic biology analysis of the Drosophila phagosome (Stuart et al 2007). …”