A Dynamical Model of Genetic Networks for Cell Differentiation

A mathematical model is proposed which is able to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions which drive the phenomenon. On the contrary, cell differentiation is described here as an emergent property of a generic model of the underlying gene regulatory network, and it can therefore be applied to a variety of different organisms. The model points to a peculiar role of cellular noise in differentiation and leads to non trivial predictions which could be subject to experimental testing. Moreover, a single model proves able to describe several different phenomena observed in various differentiation processes.

[1]  D. McDonald,et al.  Lymphatic endothelial cell identity is reversible and its maintenance requires Prox1 activity. , 2008, Genes & development.

[2]  Thomas Vierbuchen,et al.  Direct conversion of fibroblasts to functional neurons by defined factors , 2010, Nature.

[3]  S. Kauffman,et al.  Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. , 2007, Journal of theoretical biology.

[4]  T. Ichisaka,et al.  Induction of Pluripotent Stem Cells From Adult Human Fibroblasts by Defined Factors , 2008 .

[5]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  K. Kaneko,et al.  Adaptive Response of a Gene Network to Environmental Changes by Fitness-Induced Attractor Selection , 2006, PloS one.

[7]  S. Kauffman,et al.  On the dynamics of random Boolean networks subject to noise: attractors, ergodic sets and cell types. , 2010, Journal of theoretical biology.

[8]  Hannah H. Chang,et al.  Transcriptome-wide noise controls lineage choice in mammalian progenitor cells , 2008, Nature.

[9]  Charles E. Taylor,et al.  Artificial Life II , 1991 .

[10]  S. Kauffman,et al.  Noisy attractors and ergodic sets in models of gene regulatory networks. , 2007, Journal of theoretical biology.

[11]  M Villani,et al.  Genetic network models and statistical properties of gene expression data in knock-out experiments. , 2004, Journal of theoretical biology.

[12]  T. Ichisaka,et al.  Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors , 2007, Cell.

[13]  M. Baron,et al.  Reversibility of the differentiated state in somatic cells. , 1993, Current opinion in cell biology.

[14]  Hannah H. Chang,et al.  Noise-Driven Stem Cell and Progenitor Population Dynamics , 2008, PloS one.

[15]  S. Kauffman,et al.  Robustness and evolvability in genetic regulatory networks. , 2007, Journal of theoretical biology.

[16]  J. Raser,et al.  Control of Stochasticity in Eukaryotic Gene Expression , 2004, Science.

[17]  Kunihiko Kaneko,et al.  Life: An Introduction to Complex Systems Biology , 2006 .

[18]  Mads Kærn,et al.  Noise in eukaryotic gene expression , 2003, Nature.

[19]  Noam Shomron,et al.  Canalization of development by microRNAs , 2006, Nature Genetics.

[20]  S. Yamanaka,et al.  Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors , 2006, Cell.

[21]  Morris F. Maduro,et al.  Cell fate specification in the C. elegans embryo , 2010, Developmental dynamics : an official publication of the American Association of Anatomists.

[22]  O. Yli-Harja,et al.  Perturbation avalanches and criticality in gene regulatory networks. , 2006, Journal of theoretical biology.

[23]  Ilya Shmulevich,et al.  Eukaryotic cells are dynamically ordered or critical but not chaotic. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[24]  A. Arkin,et al.  Stochastic mechanisms in gene expression. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Jerrold E. Marsden,et al.  Perspectives and Problems in Nonlinear Science , 2003 .

[26]  Min Ye,et al.  Myeloid or lymphoid promiscuity as a critical step in hematopoietic lineage commitment. , 2002, Developmental cell.

[27]  Shinya Yamanaka,et al.  Elite and stochastic models for induced pluripotent stem cell generation , 2009, Nature.

[28]  M. Ogawa,et al.  Humoral Regulation of Stem Cell Proliferation , 1989, [Rinsho ketsueki] The Japanese journal of clinical hematology.

[29]  Alessandra Giorgetti,et al.  Generation of induced pluripotent stem cells from human cord blood using OCT4 and SOX2. , 2009, Cell stem cell.

[30]  Sui Huang,et al.  Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. , 2007, Developmental biology.

[31]  A. Oudenaarden,et al.  Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences , 2008, Cell.

[32]  M. Greaves,et al.  Multilineage gene expression precedes commitment in the hemopoietic system. , 1997, Genes & development.

[33]  S. Kauffman Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[34]  V. Kuznetsov,et al.  General statistics of stochastic process of gene expression in eukaryotic cells. , 2002, Genetics.

[35]  F. Tang,et al.  Dynamic equilibrium and heterogeneity of mouse pluripotent stem cells with distinct functional and epigenetic states. , 2008, Cell stem cell.

[36]  Roberto Serra,et al.  Noisy random boolean networks and cell differentiation , 2010 .

[37]  Sui Huang Reprogramming cell fates: reconciling rarity with robustness , 2009, BioEssays : news and reviews in molecular, cellular and developmental biology.

[38]  Glenn Vinnicombe,et al.  Noise in Gene Regulatory Networks , 2008, IEEE Transactions on Automatic Control.

[39]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[40]  L. Kadanoff,et al.  Boolean Dynamics with Random Couplings , 2002, nlin/0204062.

[41]  T. Enver,et al.  Forcing cells to change lineages , 2009, Nature.

[42]  L. Wolpert Do we understand development? , 1994, Science.

[43]  P. Swain,et al.  Intrinsic and extrinsic contributions to stochasticity in gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[44]  C. Furusawa,et al.  Chaotic expression dynamics implies pluripotency: when theory and experiment meet , 2009, Biology Direct.

[45]  M. Elowitz,et al.  Functional roles for noise in genetic circuits , 2010, Nature.

[46]  D. Hume,et al.  Probability in transcriptional regulation and its implications for leukocyte differentiation and inducible gene expression. , 2000, Blood.

[47]  C. Lim,et al.  Regulated Fluctuations in Nanog Expression Mediate Cell Fate Decisions in Embryonic Stem Cells , 2009, PLoS biology.