Cellular responding kinetics based on a model of gene regulatory networks under radiotherapy

Abstract

Radiotherapy can cause DNA damage into cells, triggering the cell cycle arrest and cell apop-tosis through complicated interactions among vital genes and their signal pathways. In order to in-depth study the complicated cellular res- ponses under such a circumstance, a novel mo- del for P53 stress response networks is pro- posed. It can be successfully used to simulate the dynamic processes of DNA damage trans-ferring, ATM and ARF activation, regulations of P53-MDM2 feedback loop, as well as the toxins degradation. Particularly, it has become feasible to predict the outcomes of cellular response in fighting against genome stresses. Consequently, the new model has provided a reasonable framework for analyzing the complicated regu-lations of P53 stress response networks, as well as investigating the mechanisms of the cellular self-defense under radiotherapy

Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties

“…As a critical tumor suppressor gene, p53 plays an important role in maintaining genomic stability and preventing cancer [1] , [2] , [3] . It has the highest mutation frequency in human tumors: over 50% of kinds of tumors have p53 mutations, and over 80% of kinds of tumors involve dysfunctional p53 signaling pathway [4] .…”

Evidence Obtained from anova to Reason Cross-species Infection and Cross-subtype Mutation in Neuraminidases of Influenza A Viruses

“…Many studies from various research laboratories around the world have indicated that mathematical analysis, computational modelling and introducing novel physical concept to solve important problems in biology and medicine, such as protein cleavage site prediction (Chou, 1993, 1996), signal peptide prediction (Chou and Shen, 2007a), protein subcellular location prediction (Chou and Shen, 2007b, 2010), modelling 3D structures of targeted proteins for drug design (Chou, 2004), graph/diagram approach (Chou, 1989, 1990; Althaus et al., 1993; Chou et al., 1994), diffusion‐controlled reaction simulation (Chou and Zhou, 1982; Zhou and Zhong, 1982), cellular responding kinetics (Qi et al., 2010), bio‐macromolecular internal collective motion simulation (Chou, 1987, 1988), identification of GPCR and their types (Chou, 2005; Lin et al., 2009), identification of proteases and their types (Chou and Shen, 2008), protease type prediction (Chou and Shen, 2008), membrane protein type prediction (Chou, 2001; Chou and Shen, 2007c), as well as 15 web‐server predictors summarized in Table 3 of a recent review (Chou and Shen, 2009), can timely provide very useful information and insights for both basic research and drug design and hence are widely welcome by science community. This study was to report evidences obtained from anova to reason cross‐species infection and cross‐subtype mutation in neuraminidases of influenza A viruses in hopes that our findings will be of use for developing new strategies to treat the drug‐resistant influenza viruses.…”

Cellular Automata and Its Applications in Protein Bioinformatics

“…They have modeled many threedimensional structures of proteins important for drug development (see, e.g., [28][29][30]), and helped to understand some subtle action mechanisms (see, e.g., [31,32]). Many studies from various research laboratories around the world have indicated that developing computational prediction tools, conducting computational modeling, and introducing novel physical concept to solve important problems in biology and medicine, such as protein subcellular location prediction [33][34][35], protein structural class prediction [36][37][38][39][40][41], modeling 3D structures of targeted proteins for drug design [42,43], diffusion-controlled reaction simulation [44][45][46][47], cellular responding kinetics [48,49], bio-macromolecular internal collective motion simulation [50][51][52], membrane protein type prediction [53], protein cleavage site prediction [54,55], and signal peptide prediction [56], can provide very useful information and insights for both basic research and drug design and hence are widely welcome by science community. Particularly, the recent outbreak of influenza A virus (subtype H1N1) has posed global concerns.…”