Tracing dynamic biological processes during phase transition

BackgroundPhase transition widely exists in the biological world, such as transformation of cell cycle phases, cell differentiation stages, disease development, and so on. Such a nonlinear phenomenon is considered as the conversion of a biological system from one phenotype/state to another. Studies on the molecular mechanisms of biological phase transition have attracted much attention, in particular, on different genotypes (or expression variations) in a specific phase, but with less of focus on cascade changes of genes' functions (or system state) during the phase shift or transition process. However, it is a fundamental but important mission to trace the temporal characteristics of a biological system during a specific phase transition process, which can offer clues for understanding dynamic behaviors of living organisms.ResultsBy overcoming the hurdles of traditional time segmentation and temporal biclustering methods, a causal process model (CPM) in the present work is proposed to study the biological phase transition in a systematic manner, i.e. first, we make gene-specific segmentation on time-course expression data by developing a new boundary gene estimation scheme, and then infer functional cascade dynamics by constructing a temporal block network. After the computational validation on synthetic data, CPM was used to analyze the well-known Yeast cell cycle data. It was found that the dynamics of the boundary genes are periodic and consistent with the phases of the cell cycle, and the temporal block network indeed demonstrates a meaningful cascade structure of the enriched biological functions. In addition, we further studied protein modules based on the temporal block network, which reflect temporal features in different cycles.ConclusionsAll of these results demonstrate that CPM is effective and efficient comparing to traditional methods, and is able to elucidate essential regulatory mechanism of a biological system even with complicated nonlinear phase transitions.

[1]  B. Séraphin,et al.  TACC1–chTOG–Aurora A protein complex in breast cancer , 2003, Oncogene.

[2]  Alfonso Valencia,et al.  Mutated genes, pathways and processes in tumours , 2010, EMBO reports.

[3]  Gilles Caraux,et al.  PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order , 2005, Bioinform..

[4]  I. Duarte.  Following dynamic biological processes through NMR-based metabonomics: a new tool in nanomedicine? , 2011, Journal of controlled release : official journal of the Controlled Release Society.

[5]  Hyungwon Choi,et al.  Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data , 2010, Molecular systems biology.

[6]  Wei-Mou Zheng,et al.  The analysis of large-scale gene expression correlated to the phase changes of the migratory locust. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[7]  R. Viola,et al.  Co-ordinated gene expression during phases of dormancy release in raspberry (Rubus idaeus L.) buds. , 2007, Journal of experimental botany.

[8]  Damian Szklarczyk,et al.  The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored , 2010, Nucleic Acids Res..

[9]  Trey Ideker,et al.  Protein Networks as Logic Functions in Development and Cancer , 2011, PLoS Comput. Biol..

[10]  Hedi Peterson,et al.  g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments , 2007, Nucleic Acids Res..

[11]  L. Breeden,et al.  Conserved homeodomain proteins interact with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cell cycle. , 2002, Genes & development.

[12]  Xiaoli Li,et al.  Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation , 2011, PloS one.

[13]  Luonan Chen,et al.  Biomolecular Networks: Methods and Applications in Systems Biology , 2009 .

[14]  Hunter B. Fraser,et al.  Using protein complexes to predict phenotypic effects of gene mutation , 2007, Genome Biology.

[15]  Antonio Reverter,et al.  Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks , 2008, Bioinform..

[16]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[17]  Kazuyuki Aihara,et al.  Modeling Biomolecular Networks in Cells: Structures and Dynamics , 2010 .

[18]  Jun S Liu,et al.  Bayesian biclustering of gene expression data , 2008, BMC Genomics.

[19]  A I Saeed,et al.  TM4: a free, open-source system for microarray data management and analysis. , 2003, BioTechniques.

[20]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[21]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[22]  Kazuyuki Aihara,et al.  Modeling Biomolecular Networks in Cells , 2010 .

[23]  Yang Xiang,et al.  Identifying Dynamic Network Modules with Temporal and Spatial Constraints , 2007, Pacific Symposium on Biocomputing.

[24]  William Stafford Noble,et al.  The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle. , 2006, Genes & development.

[25]  Alexandre Specht,et al.  Photochemical tools to study dynamic biological processes , 2009, HFSP journal.

[26]  I. Kohane,et al.  Absolute enrichment: gene set enrichment analysis for homeostatic systems , 2006, Nucleic acids research.

[27]  L. Watson,et al.  Reverse engineering dynamic temporal models of biological processes and their relationships , 2010, Proceedings of the National Academy of Sciences.

[28]  S. Pu,et al.  Up-to-date catalogues of yeast protein complexes , 2008, Nucleic acids research.

[29]  H. Akiyama,et al.  Monitoring for dynamic biological processing by intramolecular bioluminescence resonance energy transfer system using secreted luciferase. , 2004, Analytical biochemistry.

[30]  Piero Fariselli,et al.  eSLDB: eukaryotic subcellular localization database , 2006, Nucleic Acids Res..

[31]  Arlindo L. Oliveira,et al.  Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[32]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[33]  Jun Dong,et al.  Geometric Interpretation of Gene Coexpression Network Analysis , 2008, PLoS Comput. Biol..

[34]  Jesús S. Aguilar-Ruiz,et al.  Shifting and scaling patterns from gene expression data , 2005, Bioinform..

[35]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[36]  Luonan Chen,et al.  Coexpression network analysis in chronic hepatitis B and C hepatic lesions reveals distinct patterns of disease progression to hepatocellular carcinoma. , 2012, Journal of molecular cell biology.

[37]  Kazuyuki Aihara,et al.  Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers , 2012, Scientific Reports.