Collective behavior in gene regulation: The cell is an oscillator, the cell cycle a developmental process

The finding of a genome‐wide oscillation in transcription that gates cells into S phase and coordinates mitochondrial and metabolic functions has altered our understanding of how the cell cycle is timed and how stable cellular phenotypes are maintained. Here we present the evidence and arguments in support of the idea that everything oscillates, and the rationale for viewing the cell as an attractor from which deterministic noise can be tuned by appropriate coupling among the many feedback loops, or regulons, that make up the transcriptional–respiratory attractor cycle. The existence of this attractor also explains many of the dynamic macroscopic properties of the cell cycle and appears to be the timekeeping oscillator in both cell cycles and circadian rhythms. The path taken by this primordial oscillator in the course of differentiation or drug response may involve period‐doubling behavior. Evidence for a relatively high‐frequency timekeeping oscillator in yeast and mammalian cells comes from expression array analysis, and GC/MS in the case of yeast, and primarily from macroscopic measures of phase response to perturbation in the case of mammalian cells. Low‐amplitude, genome‐wide oscillations, a ubiquitous but often unrecognized attribute of phenotype, may be a source of seemingly intractable biological noise in microarray and proteomic studies. These oscillations in transcript and protein levels and the repeated cycles of synthesis and degradation they require, represent a high energy cost to the cell which must, from an evolutionary point of view, be recovered as essential information. We suggest that the information contained in this genome‐wide oscillation is the dynamic code that organizes a stable phenotype from an otherwise passive genome.

[1]  Robert R. Klevecz,et al.  SELF-ORGANIZATION IN BIOLOGICAL TISSUES: ANALYSIS OF ASYNCHRONOUS AND SYNCHRONOUS PERIODICITY, TURBULENCE AND SYNCHRONOUS CHAOS EMERGENT IN COUPLED CHAOTIC ARRAYS , 1992 .

[2]  R. Klevecz,et al.  Amplification and damping of deterministic noise in coupled cellular arrays , 1993 .

[3]  J. Derisi,et al.  Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise , 2006, Nature.

[4]  S. L. Wong,et al.  Towards a proteome-scale map of the human protein–protein interaction network , 2005, Nature.

[5]  Ruedi Aebersold,et al.  Quantitative phosphoproteome analysis using a dendrimer conjugation chemistry and tandem mass spectrometry , 2005, Nature Methods.

[6]  David Lloyd,et al.  Generation and maintenance of synchrony in Saccharomyces cerevisiae continuous culture. , 2003, Experimental cell research.

[7]  R. Klevecz,et al.  Tuning in the transcriptome: basins of attraction in the yeast cell cycle , 2000, Cell proliferation.

[8]  J. Kim,et al.  Geometry of gene expression dynamics , 2002, Bioinform..

[9]  R. Klevecz,et al.  Quantized generation time in mammalian cells as an expression of the cellular clock. , 1976, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Nichole L. King,et al.  Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry , 2004, Genome Biology.

[11]  Phenotypic heterogeneity and genotypic instability in coupled cellular arrays , 1998 .

[12]  Robert R Klevecz,et al.  A rapid genome-scale response of the transcriptional oscillator to perturbation reveals a period-doubling path to phenotypic change , 2006, Proceedings of the National Academy of Sciences.

[13]  Ernest Fraenkel,et al.  Sequence analysis A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data , 2006 .

[14]  E. C. Peters A polymeric solution for enriching the phosphoproteome , 2005, Nature Methods.

[15]  I. Prigogine,et al.  Fluctuations in nonequilibrium systems. , 1971, Proceedings of the National Academy of Sciences of the United States of America.

[16]  B. Hess,et al.  Oscillatory phenomena in biochemistry. , 1971, Annual review of biochemistry.

[17]  A. Kudlicki,et al.  Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes , 2005, Science.

[18]  A. Goldbeter,et al.  Toward a detailed computational model for the mammalian circadian clock , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[19]  D. Searcy Metabolic integration during the evolutionary origin of mitochondria , 2003, Cell Research.

[20]  R. Klevecz,et al.  Quasi‐Exponential Generation Time Distributions From A Limit Cycle Oscillator , 1985, Cell and tissue kinetics.

[21]  J. A. Smith,et al.  Mammalian cell cycles need two random transitions , 1980, Cell.

[22]  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.

[23]  Neal S. Holter,et al.  Fundamental patterns underlying gene expression profiles: simplicity from complexity. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[24]  C. M. Li,et al.  Evolution of the clock from yeast to man by period-doubling folds in the cellular oscillator. , 2007, Cold Spring Harbor symposia on quantitative biology.

[25]  K. Searcy,et al.  A MYCOPLASMA‐LIKE ARCHAEBACTERIUM POSSIBLY RELATED TO THE NUCLEUS AND CYTOPLASM OF EUKARYOTIC CELLS , 1981, Annals of the New York Academy of Sciences.

[26]  Nedra Rogers,et al.  Mammalian , 2007 .

[27]  Alfredo Colosimo,et al.  Gene expression waves , 2007, The FEBS journal.

[28]  Fran Lewitter,et al.  Intragenic tandem repeats generate functional variability , 2005, Nature Genetics.

[29]  A. Goldbeter,et al.  Control of oscillating glycolysis of yeast by stochastic, periodic, and steady source of substrate: a model and experimental study. , 1975, Proceedings of the National Academy of Sciences of the United States of America.

[30]  A. E. Hirsh,et al.  Evolutionary Rate in the Protein Interaction Network , 2002, Science.

[31]  S. Kauffman,et al.  Cellular clocks and oscillators. , 1984, International review of cytology.

[32]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[33]  David Lloyd,et al.  Respiratory oscillations in yeast: clock‐driven mitochondrial cycles of energization , 2002, FEBS letters.

[34]  D. Botstein,et al.  Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[35]  N. Friedman,et al.  Stochastic protein expression in individual cells at the single molecule level , 2006, Nature.

[36]  David Botstein,et al.  Processing and modeling genome-wide expression data using singular value decomposition , 2001, SPIE BiOS.

[37]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[38]  Jeffrey W. Smith,et al.  Stochastic Gene Expression in a Single Cell , 2022 .

[39]  Robert R. Klevecz,et al.  Dynamic architecture of the yeast cell cycle uncovered by wavelet decomposition of expression microarray data , 2000, Functional & Integrative Genomics.

[40]  Sui Huang,et al.  Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles , 2003, Bioinform..

[41]  H. Kitano,et al.  Regulation of yeast oscillatory dynamics , 2007, Proceedings of the National Academy of Sciences.

[42]  D. Murray,et al.  A genomewide oscillation in transcription gates DNA replication and cell cycle. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[43]  J. Warrington,et al.  Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. , 2000, Physiological genomics.