Biology subjects

Sui Huang

Publications and source records attributed to Sui Huang.

3 recordsLinked to original sources

bioRxiv2016

Conceptual Confusion: the case of Epigenetics

The observations of phenotypic plasticity have stimulated the revival of epigenetics. Over the past 70 years the term has come in many colors and flavors, depending on the biological discipline and time period. The meanings span from Waddingtons \"epigenotype\" and \"epigenetic landscape\" to the molecular biologists \"epigenetic marks\" embodied by DNA methylation and histone modifications. Here we seek to quell the ambiguity of the name. First we place \"epigenetics\" in the various historical contexts. Then, by presenting the formal concepts of dynamical systems theory we show that the \"epigenetic landscape\" is more than a metaphor: it has specific mathematical foundations. The latter explains how gene regulatory networks produce multiple attractor states, the self-stabilizing patterns of gene activation across the genome that account for \"epigenetic memory\". This network dynamics approach replaces the reductionist correspondence of molecular epigenetic modifications with concept of the epigenetic landscape, by providing a concrete and crisp correspondence.

Systems Biology↗

bioRxiv2016

Single-Cell Gene Expression Profi ling and Cell State Dynamics: Collecting Data, Correlating Data Points and Connecting the Dots

Single-cell analyses of transcript and protein expression profiles - more precisely, single-cell resolution analysis of molecular profiles of cell populations - have now entered the center stage with widespread applications of single-cell qPCR, single-cell RNA-Seq and CyTOF. These high-dimensional population snapshot techniques are complemented by low-dimensional time-resolved, microscopy-based monitoring methods. Both fronts of advance have exposed a rich heterogeneity of cell states within uniform cell populations in many biological contexts, producing a new kind of data that has stimulated a series of computational analysis methods for data visualization, dimensionality reduction, and cluster (subpopulation) identification. The next step is now to go beyond collecting data and correlating data points: to connect the dots, that is, to understand what actually underlies the identified data patterns. This entails interpreting the \"clouds of points\" in state space as a manifestation of the underlying molecular regulatory network. In that way control of cell state dynamics can be formalized as a quasi-potential landscape, as first proposed by Waddington. We summarize key methods of data acquisition and computational analysis and explain the principles that link the single-cell resolution measurements to dynamical systems theory.

Systems Biology↗

bioRxiv2016

Cell fate-decision as high-dimensional critical state transition

Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. However, how progenitor cells overcome the stability of their robust gene expression configuration (attractor) and exit their state remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or the myeloid lineage is preceded by the destabilization of their high-dimensional attractor state and that cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space: decrease of correlation between cells with concomitant increase of correlation between genes as cells approach a tipping point. The detection of \"rebellious cells\" which enter the fate opposite to the one intended corroborates the model of preceding destabilization of the progenitor state. Thus, \"early-warning signals\" associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal tool for analyzing single-cells molecular profiles that goes beyond computational pattern recognition but is based on dynamical systems theory and can predict impending major shifts in cell populations in development and disease.

Systems Biology↗