Cellular Decision Making and Biological Noise: From Microbes to Mammals

Department of Systems Biology-Unit 950, The University of Texas MD Anderson Cancer Center, 7435 Fannin Street, Houston, TX 77054, USA.
Cell (Impact Factor: 33.12). 03/2011; 144(6):910-25. DOI: 10.1016/j.cell.2011.01.030
Source: PubMed

ABSTRACT Cellular decision making is the process whereby cells assume different, functionally important and heritable fates without an associated genetic or environmental difference. Such stochastic cell fate decisions generate nongenetic cellular diversity, which may be critical for metazoan development as well as optimized microbial resource utilization and survival in a fluctuating, frequently stressful environment. Here, we review several examples of cellular decision making from viruses, bacteria, yeast, lower metazoans, and mammals, highlighting the role of regulatory network structure and molecular noise. We propose that cellular decision making is one of at least three key processes underlying development at various scales of biological organization.

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Available from: Alexander van Oudenaarden, Aug 23, 2015
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    • "This field has become particularly active due to its potential medical applications using stem cells systems biology as a means for discovering efficient reprogramming or therapeutic strategies by combining mathematical and computational modeling with experimental techniques (MacArthur et al., 2008, 2009; Roeder and Radtke, 2009; Huang, 2011; Zhou and Huang, 2011). Recently, though, numerous critiques to Waddington's original model have been presented in light of the dynamical plasticity of differentiated cells (see, for example Balázsi et al., 2011; Ferrell, 2012; Furusawa and Kaneko, 2012; Garcia-Ojalvo and Arias, 2012; Sieweke, 2015). In this review, we claim that the formalization of the EL in the context of the study of the dynamical properties of GRNs enables a formal framework which provides the necessary flexibility for a model to be both: (1) consistent with the observed inherent plasticity of developing cells and (2) formally derived from the uncovered regulatory underpinnings of cell-fate regulation. "
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    ABSTRACT: Robust temporal and spatial patterns of cell types emerge in the course of normal development in multicellular organisms. The onset of degenerative diseases may result from altered cell fate decisions that give rise to pathological phenotypes. Complex networks of genetic and non-genetic components underlie such normal and altered morphogenetic patterns. Here we focus on the networks of regulatory interactions involved in cell-fate decisions. Such networks modeled as dynamical non-linear systems attain particular stable configurations on gene activity that have been interpreted as cell-fate states. The network structure also restricts the most probable transition patterns among such states. The so-called Epigenetic Landscape (EL), originally proposed by C. H. Waddington, was an early attempt to conceptually explain the emergence of developmental choices as the result of intrinsic constraints (regulatory interactions) shaped during evolution. Thanks to the wealth of molecular genetic and genomic studies, we are now able to postulate gene regulatory networks (GRN) grounded on experimental data, and to derive EL models for specific cases. This, in turn, has motivated several mathematical and computational modeling approaches inspired by the EL concept, that may be useful tools to understand and predict cell-fate decisions and emerging patterns. In order to distinguish between the classical metaphorical EL proposal of Waddington, we refer to the Epigenetic Attractors Landscape (EAL), a proposal that is formally framed in the context of GRNs and dynamical systems theory. In this review we discuss recent EAL modeling strategies, their conceptual basis and their application in studying the emergence of both normal and pathological developmental processes. In addition, we discuss how model predictions can shed light into rational strategies for cell fate regulation, and we point to challenges ahead.
    Frontiers in Genetics 04/2015; 6. DOI:10.3389/fgene.2015.00160
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    • "For instance, bacteria perceives chemical molecules in their environment and interprets them in order to better estimate environmental conditions and (stochastically) decide their phenotype [24] [1] [23] [27]. Plants detect airborne signals released by other plants, being able to interpret them as attacks of pathogens or herbivores [13] [29]. "
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    DESCRIPTION: We consider the problem of the evolution of a code within a structured population of agents. The agents try to maximise their information about their environment by acquiring information from the outputs of other agents in the population. A naive use of information-theoretic methods would assume that every agent knows how to “interpret” the information offered by other agents. However, this assumes that one “knows” which other agents one observes, and thus which code they use. In our model, however, we wish to preclude that: it is not clear which other agents an agent is observing, and the resulting usable information is therefore influenced by the universality of the code used and by which agents an agent is “listening” to. We further investigate whether an agent who does not directly perceive the environment can distinguish states by observing other agents’ outputs. For this purpose, we consider a population of different types of agents “talking” about different concepts, and try to extract new ones by considering their outputs only.
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    • "Large fluctuations in gene expression levels among individual cells exist in isogenic populations even under constant environmental conditions (Elowitz et al. 2002; Blake et al. 2003; Raser and O'Shea 2004). This gene expression variability, also called noise, is due to stochastic fluctuations at the molecular level and is now recognized as widely involved in major biological phenomena because it can have profound phenotypic consequences (Raj and van Oudenaarden 2008; Balazsi et al. 2011). Stochastic gene expression could be especially advantageous in that it would allow heterogeneous phenotypes to appear among genetically identical cells, enabling a population to contain subpopulations with different behaviors. "
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    ABSTRACT: The increase in phenotypic variability through gene expression noise is proposed to be an evolutionary strategy in selective environments. Differences in promoter-mediated noise between Saccharomyces cerevisiae strains could have been selected for thanks to the benefit conferred by gene expression heterogeneity in the stressful conditions, for instance, those experienced by industrial strains for instance. Here we used a genome-wide approach to identify promoters conferring high noise levels in the industrial wine strain EC1118. Many promoters of genes related to environmental factors were identified, some of them containing genetic variations compared to their counterpart in the laboratory strain S288c. Each variant of eight promoters has been fused to yEGFP and integrated in the genome of both strains. Some industrial variants conferred higher expression associated, as expected, to lower noise, but other variants either increased or decreased expression without modifying variability, so that they might exhibit different levels of transcriptional-mediated noise at equal mean. At different induction conditions giving similar expression for both variants of the CUP1 promoter, we indeed observed higher noise with the industrial variant. Nevertheless, this difference was only observed in the industrial strain, revealing epistasis in the generation of promoter-mediated noise. Moreover, the increased expression variability conferred by this natural yeast promoter variant provided a clear benefit in the face of an environmental stress. Thus modulation of gene expression noise by a combination of promoter modifications and trans-influences might be a possible adaptation mechanism in yeast. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
    Genome Biology and Evolution 03/2015; 7(4). DOI:10.1093/gbe/evv047 · 4.53 Impact Factor
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