Quantifying Cell Fate Decisions for Differentiation and Reprogramming of a Human Stem Cell Network: Landscape and Biological Paths

Cellular reprogramming has been recently intensively studied experimentally. We developed a global potential landscape and kinetic path framework to explore a human stem cell developmental network composed of 52 genes. We uncovered the underlying landscape for the stem cell network with two basins of attractions representing stem and differentiated cell states, quantified and exhibited the high dimensional biological paths for the differentiation and reprogramming process, connecting the stem cell state and differentiated cell state. Both the landscape and non-equilibrium curl flux determine the dynamics of cell differentiation jointly. Flux leads the kinetic paths to be deviated from the steepest descent gradient path, and the corresponding differentiation and reprogramming paths are irreversible. Quantification of paths allows us to find out how the differentiation and reprogramming occur and which important states they go through. We show the developmental process proceeds as moving from the stem cell basin of attraction to the differentiation basin of attraction. The landscape topography characterized by the barrier heights and transition rates quantitatively determine the global stability and kinetic speed of cell fate decision process for development. Through the global sensitivity analysis, we provided some specific predictions for the effects of key genes and regulation connections on the cellular differentiation or reprogramming process. Key links from sensitivity analysis and biological paths can be used to guide the differentiation designs or reprogramming tactics.

[1]  Janet Rossant,et al.  Blastocyst lineage formation, early embryonic asymmetries and axis patterning in the mouse , 2009, Development.

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

[3]  Gonçalo Castelo-Branco,et al.  Nanog Overcomes Reprogramming Barriers and Induces Pluripotency in Minimal Conditions , 2011, Current Biology.

[4]  Krishanu Saha,et al.  Technical challenges in using human induced pluripotent stem cells to model disease. , 2009, Cell stem cell.

[5]  Sheng Zhong,et al.  A core Klf circuitry regulates self-renewal of embryonic stem cells , 2008, Nature Cell Biology.

[6]  Jin Wang,et al.  A new mechanism of stem cell differentiation through slow binding/unbinding of regulators to genes , 2012, Scientific reports.

[7]  Ying Guo,et al.  The embryonic stem cell transcription factors Oct-4 and FoxD3 interact to regulate endodermal-specific promoter expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Jin Wang,et al.  Quantifying the Waddington landscape and biological paths for development and differentiation , 2011, Proceedings of the National Academy of Sciences.

[9]  Erkang Wang,et al.  Landscape topography determines global stability and robustness of a metabolic network. , 2012, ACS synthetic biology.

[10]  H. Orland,et al.  Dominant pathways in protein folding. , 2005, Physical review letters.

[11]  H. Qian Cooperativity in cellular biochemical processes: noise-enhanced sensitivity, fluctuating enzyme, bistability with nonlinear feedback, and other mechanisms for sigmoidal responses. , 2012, Annual review of biophysics.

[12]  C. Waddington,et al.  The strategy of the genes , 1957 .

[13]  Carsten Peterson,et al.  Transcriptional Dynamics of the Embryonic Stem Cell Switch , 2006, PLoS Comput. Biol..

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

[15]  Carsten Peterson,et al.  A Computational Model for Understanding Stem Cell, Trophectoderm and Endoderm Lineage Determination , 2008, PloS one.

[16]  N. D. Clarke,et al.  A genome-wide RNAi screen reveals determinants of human embryonic stem cell identity , 2010, Nature.

[17]  Jianhua Xing,et al.  Global Epigenetic State Network Governs Cellular Pluripotent Reprogramming and Transdifferentiation , 2012, 1209.4603.

[18]  Jin Wang,et al.  Potential landscape and flux framework of nonequilibrium networks: Robustness, dissipation, and coherence of biochemical oscillations , 2008, Proceedings of the National Academy of Sciences.

[19]  Ingo Roeder,et al.  Nanog Variability and Pluripotency Regulation of Embryonic Stem Cells - Insights from a Mathematical Model Analysis , 2010, PloS one.

[20]  Hitoshi Niwa,et al.  How is pluripotency determined and maintained? , 2007, Development.

[21]  Erkang Wang,et al.  Potential and flux landscapes quantify the stability and robustness of budding yeast cell cycle network , 2010, Proceedings of the National Academy of Sciences.

[22]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[23]  H. Lehrach,et al.  In silico identification of a core regulatory network of OCT4 in human embryonic stem cells using an integrated approach , 2009, BMC Genomics.

[24]  Sui Huang,et al.  The potential landscape of genetic circuits imposes the arrow of time in stem cell differentiation. , 2010, Biophysical journal.

[25]  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.

[26]  Erkang Wang,et al.  Potential Landscape and Probabilistic Flux of a Predator Prey Network , 2011, PloS one.

[27]  Gábor Balázsi,et al.  Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit , 2012, PLoS Comput. Biol..

[28]  D. Melton,et al.  Extreme makeover: converting one cell into another. , 2008, Cell stem cell.

[29]  Peter G Wolynes,et al.  Stochastic gene expression as a many-body problem , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Jin Wang,et al.  Kinetic paths, time scale, and underlying landscapes: a path integral framework to study global natures of nonequilibrium systems and networks. , 2010, The Journal of chemical physics.

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

[32]  H. Blau,et al.  Nuclear reprogramming to a pluripotent state by three approaches , 2010, Nature.

[33]  P. Ao,et al.  Laws in Darwinian evolutionary theory , 2005, q-bio/0605020.

[34]  Kazuwa Nakao,et al.  Differentiation of embryonic stem cells is induced by GATA factors. , 2002, Genes & development.

[35]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[36]  Megan F. Cole,et al.  Core Transcriptional Regulatory Circuitry in Human Embryonic Stem Cells , 2005, Cell.

[37]  P. Ao Global view of bionetwork dynamics: adaptive landscape. , 2009, Journal of genetics and genomics = Yi chuan xue bao.

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

[39]  Katharine L. C. Hunt,et al.  Path integral solutions of stochastic equations for nonlinear irreversible processes: The uniqueness of the thermodynamic Lagrangian , 1981 .

[40]  Rui Chang,et al.  Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells , 2011, PLoS Comput. Biol..

[41]  C. Verfaillie,et al.  Zic3 enhances the generation of mouse induced pluripotent stem cells. , 2013, Stem cells and development.