What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast

Llamosi, Artémis; Gonzalez-Vargas, Andres M.; Versari, Cristian; Cinquemani, Eugenio; Ferrari-Trecate, Giancarlo; Hersen, Pascal; Batt, Grégory

doi:10.1371/journal.pcbi.1004706

Cited by 86 publications

(142 citation statements)

References 36 publications

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“…Instead of looking for the sensitivity of the circuit function to parameter variations [34,37] and the parameters best fitting the experimental data[35,36], we focused on uncovering conserved features from the ensemble of RACIPE models. This was carried out by standard statistical learning methods such as hierarchical clustering analysis.…”

Section: Discussionmentioning

confidence: 99%

Interrogating the topological robustness of gene regulatory circuits by randomization

et al. 2017

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One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

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Section: Discussionmentioning

confidence: 99%

Interrogating the topological robustness of gene regulatory circuits by randomization

et al. 2017

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“…At the level of intracellular dynamics, biochemical network noise is the subject of intense research (Bowsher et al, 2013;Thattai and van Oudenaarden, 2001;Zechner et al, 2014;Llamosi et al, 2014). Intrinsic noise in gene expression is one important source of variability over different cells with identical genome, and is at the heart of the ability of microorganisms to survive changing environments, differentiate, and on (Rao et al, 2002;Kaern et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Structural identification of biochemical reaction networks from population snapshot data

Cinquemani

2017

IFAC-PapersOnLine

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In this paper we investigate how randomness in biochemical network dynamics improves identification of the network structure. Focusing on the case of so-called population snapshot data, we set out the problem as that of reconstructing the unknown stoichiometry matrix and rate parameters of the network in the case of state-affine reaction rates. We discuss what additional information is conveyed by the observation of second-order moments of the system species relative to the sole knowledge of their mean profiles. We then illustrate the impact of this additional piece of information in the reconstruction of an unknown network structure by means of a simple numerical example.

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“…On the one hand, the Gillespie algorithm (Fig S2) has been used to model the stochastic dynamics of gene expression caused by low copy number and slow switches between gene states 16 . On the other hand, cells of different size and microenvironment can be modeled by the same rate equations but different kinetic parameters 49 . Our method allows the analysis of both factors, therefore being an invaluable tool to study the nature of variations in a cell population, especially with the advent of single cell techniques.…”

Section: Discussionmentioning

confidence: 99%

Role of noise and parametric variation in the dynamics of gene regulatory circuits

Kohar

2018

Preprint

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Stochasticity in gene expression impacts the dynamics and functions of gene regulatory circuits. Intrinsic noises, including those that are caused by low copy number of molecules and transcriptional bursting, are usually studied by stochastic analysis methods, such as Gillespie algorithm and Langevin simulation. However, the role of extrinsic factors, such as cell-to-cell variability and heterogeneity in microenvironment, is still elusive. To evaluate the effects of both intrinsic and extrinsic noises, we develop a new method, named sRACIPE, by integrating stochastic analysis with random circuit perturbation (RACIPE) method. Unlike traditional methods, RACIPE generates and analyzes an ensemble of mathematical models with random kinetic parameters. Previously, we have shown that the gene expression from random models form robust and functionally related clusters. Under the framework of this randomization-based approach, here we develop two stochastic simulation schemes, aiming to reduce the computational cost without sacrificing the convergence of statistics. One scheme uses constant noise to capture the basins of attraction, and the other one uses simulated annealing to detect the stability of states. By testing the methods on several gene regulatory circuits, we found that high noise, but not large parameter variation, merges clusters together. Our approach quantifies the robustness of a gene circuit in the presence of noise and sheds light on a new mechanism of noise induced hybrid states. We have implemented sRACIPE into a freely available R package.

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What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast

Cited by 86 publications

References 36 publications

Interrogating the topological robustness of gene regulatory circuits by randomization

Interrogating the topological robustness of gene regulatory circuits by randomization

Structural identification of biochemical reaction networks from population snapshot data

Role of noise and parametric variation in the dynamics of gene regulatory circuits

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