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: 32.24). 03/2011; 144(6):910-25. DOI: 10.1016/j.cell.2011.01.030
Source: PubMed


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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    • "Such fluctuations lead to macroscopic effects in a diverse array of processes. In differentiation, the resulting noise plays a central role in cell fate determination and can allow clonal populations of differentiating cells to achieve distinct final states [3] [4]. Noise can also produce spontaneous transitions, whereby it causes a system to switch from one stable state to another, often producing a significant change of phenotype or function. "
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    ABSTRACT: Noise caused by fluctuations at the molecular level is a fundamental part of intracellular processes. While the response of biological systems to noise has been studied extensively, there has been limited understanding of how to exploit it to induce a desired cell state. Here we present a scalable, quantitative method based on the Freidlin-Wentzell action to predict and control noise-induced switching between different states in genetic networks that, conveniently, can also control transitions between stable states in the absence of noise. We apply this methodology to models of cell differentiation and show how predicted manipulations of tunable factors can induce lineage changes, and further utilize it to identify new candidate strategies for cancer therapy in a cell death pathway model. This framework offers a systems approach to identifying the key factors for rationally manipulating biophysical dynamics, and should also find use in controlling other classes of noisy complex networks.
    Physical Review X 09/2015; 5(3). DOI:10.1103/PhysRevX.5.031036 · 9.04 Impact Factor
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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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