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

The Role of the CREB Protein Family Members and the Related Transcription Factors in Radioresistance Mechanisms

In the framework of space flight, the risk of radiation carcinogenesis is considered a "red" risk due to the high likelihood of occurrence as well as the high potential impact on the quality of life in terms of disease-free survival after space missions. The cyclic AMP response element-binding protein (CREB) is overexpressed both in haematological malignancies and solid tumours and its expression and function are modulated following irradiation. The CREB protein is a transcription factor and member of the CREB/activating transcription factor (ATF) family. As such, it has an essential role in a wide range of cell processes, including cell survival, proliferation, and differentiation. Among the CREB-related nuclear transcription factors, NF-κB and p53 have a relevant role in cell response to ionising radiation. Their expression and function can decide the fate of the cell by choosing between death or survival. The aim of this review was to define the role of the CREB/ATF family members and the related transcription factors in the response to ionising radiation of human haematological malignancies and solid tumours.

Multi-scale modeling of cell survival and death mediated by the p53 network: a systems pharmacology framework

The determination of cell fate is a key regulatory process for the development of complex organisms that are controlled by distinct genes in mammalian cells.

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression

The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but also to enhance human disease diagnosis and treatment. Recently, high-throughput techniques have been applied to detect dozens or even hundreds of candidate genes. However, experimental approaches to validate the many candidates are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Therefore, numerous theoretical and computational methods (e.g. network-based approaches) have been developed to prioritize candidate disease genes. Many network-based approaches implicitly utilize the observation that genes causing the same or similar diseases tend to correlate with each other in gene-protein relationship networks. Of these network approaches, the random walk with restart algorithm (RWR) is considered to be a state-of-the-art approach. To further improve the performance of RWR, we propose a novel method named ESFSC to identify disease-related genes, by enlarging the seed set according to the centrality of disease genes in a network and fusing information of the protein-protein interaction (PPI) network topological similarity and the gene expression correlation. The ESFSC algorithm restarts at all of the nodes in the seed set consisting of the known disease genes and their k-nearest neighbor nodes, then walks in the global network separately guided by the similarity transition matrix constructed with PPI network topological similarity properties and the correlational transition matrix constructed with the gene expression profiles. As a result, all the genes in the network are ranked by weighted fusing the above results of the RWR guided by two types of transition matrices. Comprehensive simulation results of the 10 diseases with 97 known disease genes collected from the Online Mendelian Inheritance in Man (OMIM) database show that ESFSC outperforms existing methods for prioritizing candidate disease genes. The top prediction results of Alzheimer's disease are consistent with previous literature reports.

Splitting strategy for simulating genetic regulatory networks

The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions.

Possibility of cross-species/subtype reassortments in influenza A viruses: an analysis of nonstructural protein variations

The reassortment of genetic segments from different host species and from different subtypes of influenza A viruses occurs frequently, which may generate new strains causing flu epidemic or pandemic. However, the underlined mechanisms of reassortment were less addressed from the viewpoint of protein variations. Recently, we used the amino-acid pair predictability as an indicator to convert eight types of influenza A virus proteins into predictable portion of amino-acid pairs, and then applied the models I and II ANOVA to estimate their differences in terms of subtypes and host species. In order to get a full picture, 2729 and 1063 non-structural 1 and 2 proteins of influenza A viruses were analyzed in this study. The results are consistent with those obtained from hemagglutinin, neuraminidase, nucleoprotein, polymerase acidic protein, polymerase basic proteins 1 and 2, and matrix proteins 1 and 2, indicating that inter-species/subtypes variations are smaller than intra-species/subtype ones. Our findings provide statistical evidence that can partially explains why cross-subtype mutation and cross-species infection easily occur during co-infecting of different strains.

Kinetic theory approach to modeling of cellular repair mechanisms under genome stress

Under acute perturbations from outer environment, a normal cell can trigger cellular self-defense mechanism in response to genome stress. To investigate the kinetics of cellular self-repair process at single cell level further, a model of DNA damage generating and repair is proposed under acute Ion Radiation (IR) by using mathematical framework of kinetic theory of active particles (KTAP). Firstly, we focus on illustrating the profile of Cellular Repair System (CRS) instituted by two sub-populations, each of which is made up of the active particles with different discrete states. Then, we implement the mathematical framework of cellular self-repair mechanism, and illustrate the dynamic processes of Double Strand Breaks (DSBs) and Repair Protein (RP) generating, DSB-protein complexes (DSBCs) synthesizing, and toxins accumulating. Finally, we roughly analyze the capability of cellular self-repair mechanism, cellular activity of transferring DNA damage, and genome stability, especially the different fates of a certain cell before and after the time thresholds of IR perturbations that a cell can tolerate maximally under different IR perturbation circumstances.

A Mathematical Framework for Cellular Repair Mechanisms under Genomic Stress Based on Kinetic Theory Approach

Generally, a cell can trigger its self-defense mechanism in response to genomic stress under acute perturbations from outer environment. To investigate the dynamic kinetics of cellular repair mechanisms in fighting against genomic stress, a mathematical model of representing and analyzing DNA damage generation and repair process is proposed under acute Ion Radiation (IR) by using the Kinetic Theory of Active Particles (KTAP). In this paper, we focus on describing a mathematical framework of Cellular Repair System (CRS). We also present the dynamic processes of Double Strand Breaks (DSBs) and Repair Protein (RP) generating, DSB-protein complexes (DSBCs) synthesizing, and toxins accumulating under continuous radiation time.

Modeling DNA double-strand break repair kinetics as an epiregulated cell-community-wide (epicellcom) response to radiation stress

The multicellular signaling model (MULTISIG1) was recently introduced to simulate the kinetics of repair of DNA double-strand breaks (DSBs) that were induced in confluent (non-dividing) cultured cells by a very low radiation dose where at most a single induced DSB would be expected in a given cell nucleus. The repair kinetics was modeled as representing what is now called an epigenetically-regulated (epiregulated) cell-community-wide (epicellcom) response to radiation stress. DSB repair initiation is assumed to require a threshold number of cells with DSBs participating in intercellular stress-response signaling. The MULTISIG1 model is extended in this study to apply to moderate doses where several DSBs can occur on the same DNA molecule. The repair of multiple breaks on the same molecule is treated as sequential stochastic events. For cells of differing genetic characteristics and epigenetic statuses, relationships are provided for evaluating the relative susceptibility (RS) for DSB induction, relative repair capacity (RRC) for DSB repair, and relative epiapoptosis capacity (REC), for epigenetically regulated apoptosis. The modified MULTISIG1 model is used to characterize the expected repair kinetics for confluent, human lung fibroblasts (MRC-5 line) briefly exposed in vitro to 90-kV x-rays. Possible application of the model to biological dosimetry is also discussed.

Application of density similarities to predict membrane protein types based on pseudo-amino acid composition

Cell membranes provide integrity of living cells. Although the stability of biological membrane is maintained by the lipid bilayer, membrane proteins perform most of the specific functions such as signal transduction, transmembrane transport, etc. Then it is plausible membrane proteins being attractive drug targets. In this article, based on the concept of using the pseudo-amino acid composition to define a protein, three different density similarities are developed for predicting the membrane protein type. The predicted results showed that the proposed approach can remarkably improve the accuracy, and might become a useful tool for predicting the other attributes of proteins as well.

Some remarks on protein attribute prediction and pseudo amino acid composition

With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased explosively. In contrast, the pace is much slower in determining their biological attributes. As a consequence, the gap between sequence-known proteins and attribute-known proteins has become increasingly large. The unbalanced situation, which has critically limited our ability to timely utilize the newly discovered proteins for basic research and drug development, has called for developing computational methods or high-throughput automated tools for fast and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. Actually, during the last two decades or so, many methods in this regard have been established in hope to bridge such a gap. In the course of developing these methods, the following things were often needed to consider: (1) benchmark dataset construction, (2) protein sample formulation, (3) operating algorithm (or engine), (4) anticipated accuracy, and (5) web-server establishment. In this review, we are to discuss each of the five procedures, with a special focus on the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.

Analysis of protein pathway networks using hybrid properties

Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on different organisms, at both the genetic and metabolic levels, has been accumulated and systematically stored in various specific databases, such as KEGG, ENZYME, BRENDA, EcoCyc and MetaCyc. These data have made it feasible to address such an essential problem. In this paper, we have analyzed known regulatory pathways in humans by extracting different (biological and graphic) features from each of the 17,069 protein-formed systems, of which 169 are positive pathways, i.e., known regulatory pathways taken from KEGG; while 16,900 were negative, i.e., not formed as a biologically meaningful pathway. Each of these protein-forming systems was represented by 352 features, of which 88 are graph features and 264 biological features. To analyze these features, the "Minimum Redundancy Maximum Relevance" and the "Incremental Feature Selection" techniques were utilized to select a set of 22 optimal features to query whether a protein-forming system is able to form a biologically meaningful pathway or not. It was found through cross-validation that the overall success rate thus obtained in identifying the positive pathways was 79.88%. It is anticipated that, this novel approach and encouraging result, although preliminary yet, may stimulate extensive investigations into this important topic.

Trends in global warming and evolution of nucleoproteins from influenza A viruses since 1918

Global warming affects not only the environment where we live, but also all living species to different degree, including influenza A virus. We recently conducted several studies on the possible impact of global warming on the protein families of influenza A virus. More studies are needed in order to have a full picture of the impact of global warming on living organisms, especially its effect on viruses. In this study, we correlate trends in global warming with evolution of the nucleoprotein from influenza A virus and then analyse the trends with respect to northern/southern hemispheres, virus subtypes and sampling species. The results suggest that global warming may have an impact on the evolution of the nucleoprotein from influenza A virus.

Dynamic modeling of cellular response to DNA damage based on p53 stress response networks

Under acute perturbations from the outside, cells can trigger self-defensive mechanisms to fight against genome stress. To investigate the cellular response to continuous ion radiation (IR), a dynamic model for p53 stress response networks at the cellular level is proposed. The model can successfully be used to simulate the dynamic processes of double-strand breaks (DSBs) generation and their repair, switch-like ataxia telangiectasia mutated (ATM) activation, oscillations occurring in the p53-MDM2 feedback loop, as well as toxins elimination triggered by p53 stress response networks. Especially, the model can predict the plausible outcomes of cellular response under different IR dose regimes.

Logical modelling of cell cycle control in eukaryotes: a comparative study

Dynamical modelling is at the core of the systems biology paradigm. However, the development of comprehensive quantitative models is complicated by the daunting complexity of regulatory networks controlling crucial biological processes such as cell division, the paucity of currently available quantitative data, as well as the limited reproducibility of large-scale experiments. In this context, qualitative modelling approaches offer a useful alternative or complementary framework to build and analyse simplified, but still rigorous dynamical models. This point is illustrated here by analysing recent logical models of the molecular network controlling mitosis in different organisms, from yeasts to mammals. After a short introduction covering cell cycle and logical modelling, we compare the assumptions and properties underlying these different models. Next, leaning on their transposition into a common logical framework, we compare their functional structure in terms of regulatory circuits. Finally, we discuss assets and prospects of qualitative approaches for the modelling of the cell cycle.

Organisms can essentially be classified according to two codon patterns

We recently classified 23 bacteria into two types based on their complete genomes; "S-type" as represented by Staphylococcus aureus and "E-type" as represented by Escherichia coli. Classification was characterized by concentrations of Arg, Ala or Lys in the amino acid composition calculated from the complete genome. Based on these previous classifications, not only prokaryotic but also eukaryotic genome structures were investigated by amino acid compositions and nucleotide contents. Organisms consisting of 112 bacteria, 15 archaea and 18 eukaryotes were classified into two major groups by cluster analysis using GC contents at the three codon positions calculated from complete genomes. The 145 organisms were classified into "AT-type" and "GC-type" represented by high A or T (low G or C) and high G or C (low A or T) contents, respectively, at every third codon position. Reciprocal changes between G or C and A or T contents at the third codon position occurred almost synchronously in every codon among the organisms. Correlations between amino acid concentrations (Ala, Ile and Lys) and the nucleotide contents at the codon position were obtained in both "AT-type" and "GC-type" organisms, but with different regression coefficients. In certain correlations of amino acid concentrations with GC contents, eukaryotes, archaea and bacteria showed different behaviors; thus these kingdoms evolved differently. All organisms are basically classifiable into two groups having characteristic codon patterns; organisms with low GC and high AT contents at the third codon position and their derivatives, and organisms with an inverse relationship.

A mathematical model of P53 gene regulatory networks under radiotherapy

P53, a vital anticancer gene, controls the transcription and translation of a series of genes, and implement the cell cycle arrest and cell apoptosis by regulating their complicated signal pathways. Under radiotherapy, cell can trigger internal self-defense mechanisms in fighting against genome stresses induced by acute ion radiation (IR). To simulate the investigating of cellular responding acute IR at single cell level further, we propose a model of P53 gene regulatory networks under radiotherapy. Our model can successfully implement the kinetics of double strand breaks (DSBs) generating and their repair, ataxia telangiectasia mutated (ATM) activation, as well as P53-MDM2 feedback regulating. By comparing simulations under different IR dose, we can try to find the optimal strategy in controlling of IR dose and therapy time, and provide some theoretical analysis to obtain much better outcome of radiotherapy further.