Computational systems biology of the cell cycle

被引:49
|
作者
Csikasz-Nagy, Attila
机构
[1] The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Povo-Trento I-38100
关键词
cell cycle; computational modeling; historical review; perspectives; systems biology; XENOPUS-OOCYTE EXTRACTS; M-PHASE CONTROL; MATHEMATICAL-MODEL; SACCHAROMYCES-CEREVISIAE; TRANSCRIPTION FACTORS; PARAMETER-ESTIMATION; BIFURCATION-ANALYSIS; POSITIVE FEEDBACK; MOLECULAR-MODEL; DIVISION CYCLE;
D O I
10.1093/bib/bbp005
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.
引用
收藏
页码:424 / 434
页数:11
相关论文
共 50 条
  • [31] Toward computational systems biology
    Lingchong You
    Cell Biochemistry and Biophysics, 2004, 40 : 167 - 184
  • [32] Computational Inference in Systems Biology
    Macdonald, Benn
    Husmeier, Dirk
    BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2015), PT II, 2015, 9044 : 276 - 288
  • [33] Computational challenges of systems biology
    Finkelstein, A
    Hetherington, J
    Li, LZ
    Margoninski, O
    Saffrey, P
    Seymour, R
    Warner, A
    COMPUTER, 2004, 37 (05) : 26 - +
  • [34] A conversation with ChatGPT on the role of computational systems biology in stem cell research
    Cahan, Patrick
    Treutlein, Barbara
    STEM CELL REPORTS, 2023, 18 (01): : 1 - 2
  • [35] Computational problems in cell biology
    Delisi, Charles
    Vajda, Sandor
    Computing in Science and Engineering, 1 (03): : 26 - 31
  • [36] Computational imaging in cell biology
    Eils, R
    Athale, C
    JOURNAL OF CELL BIOLOGY, 2003, 161 (03): : 477 - 481
  • [37] Computational problems in cell biology
    DeLisi, C
    Vajda, S
    COMPUTING IN SCIENCE & ENGINEERING, 1999, 1 (03) : 26 - 32
  • [38] Systems biology - The dynamics of the cycle
    Bussell, K
    NATURE REVIEWS MOLECULAR CELL BIOLOGY, 2005, 6 (03) : 190 - 190
  • [39] Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology
    Schmidt, H
    Jirstrand, M
    BIOINFORMATICS, 2006, 22 (04) : 514 - 515
  • [40] Cell biology - Cytoskeleton in the cell cycle
    Nakaseko, Y
    Yanagida, M
    NATURE, 2001, 412 (6844) : 291 - 292