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Quantitative Biology

Quant Biol    2013, Vol. 1 Issue (2) : 131-139
Predictive power of cell-to-cell variability
Bochong Li1, Lingchong You1,2,3()
1. Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; 2. Center for Systems Biology, Duke University, Durham, NC 27708, USA; 3. Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA
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Much of our current knowledge of biology has been constructed based on population-average measurements. However, advances in single-cell analysis have demonstrated the omnipresent nature of cell-to-cell variability in any population. On one hand, tremendous efforts have been made to examine how such variability arises, how it is regulated by cellular networks, and how it can affect cell-fate decisions by single cells. On the other hand, recent studies suggest that the variability may carry valuable information that can facilitate the elucidation of underlying regulatory networks or the classification of cell states. To this end, a major challenge is determining what aspects of variability bear significant biological meaning. Addressing this challenge requires the development of new computational tools, in conjunction with appropriately chosen experimental platforms, to more effectively describe and interpret data on cell-cell variability. Here, we discuss examples of when population heterogeneity plays critical roles in determining biologically and clinically significant phenotypes, how it serves as a rich information source of regulatory mechanisms, and how we can extract such information to gain a deeper understanding of biological systems.

Corresponding Author(s): You Lingchong,   
Issue Date: 05 June 2013
 Cite this article:   
Bochong Li,Lingchong You. Predictive power of cell-to-cell variability[J]. Quant Biol, 2013, 1(2): 131-139.
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Fig.1  Inference of regulatory mechanisms from population variability measurements.
(A) Two slightly different transcription regulatory mechanisms generate distinctive distribution of mRNA molecules under stochastic reaction dynamics over a population. Above, the promoter of the gene randomly switches between on and off states. When the promoter is on, mRNA molecules are produced, which in turn undergoes decay. Below, the mRNA molecule (or its protein product) and feedback to the promoter and enhance the transcription rate when it’s on, resulting in a bimodal distribution of the mRNA molecule. By counting RNA molecules in single cells and obtain its variability measure over the population, the regulatory mechanism of transcription may be distinguished between alternative hypotheses. (B) Given a regulatory mechanism, a mathematical model capable of simulating stochastic chemical dynamics (such as stochastic differential equations), whose structure best represents the current understanding of the biologic system, can be constructed. The parameters (or parameter distributions) of the model and hence the biologic system can be more efficiently inferred by comparing the experimentally observed distribution of one or more system components with that generated by the model with varying parameter values and selecting one generating the best matching distribution.
Fig.2  Characterizing population phenotypes with heterogeneity measures.
(A) Cell populations manifest various population level emergent phenotypes, for example, the overall growth rate of various cancer cell populations (of different origins and malignancy) in response to anti-cancer therapeutics. Such phenotypes can be classified into qualitatively different categories, such as good or poor responses to therapeutics shown here. (B) Population phenotypes may correlate with population heterogeneity in certain single-cell attributes, including gene express, surface marker, and signaling molecule co-localization mentioned in the text. That is, similarity in the population heterogeneity measure (shape of the distribution clustering in the distribution feature space) coincides with the classification in population phenotype. Here the heterogeneity measure is represented by 2D joint distributions (red, higher probability density; blue, lower probability density). Consequently, after curating a large database of phenotype-heterogeneity correlations, one can predict population phenotype by measuring population heterogeneity and determining which distribution pattern category the observation belongs to.
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