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Predicting cellular growth from gene expression signatures.

Airoldi EM, Huttenhower C, Gresham D, Lu C, Caudy AA, Dunham MJ, Broach JR, Botstein D, Troyanskaya OG - PLoS Comput. Biol. (2009)

Bottom Line: The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution.We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes.More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods.

View Article: PubMed Central - PubMed

Affiliation: Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

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Schematic of a chemostat.In the chemostat, cells are grown in liquid media [14]. A tank                            contains a large supply of nutrient containing high concentrations of                            all growth factors, but a limited concentration (S0) of the                            controlling growth factor. The nutrient flows continuously into a growth                            tube of limited capacity, where the culture grows. The dynamic behavior                            of the density of the culture (X) and of the concentration of the                            controlling nutrient (S) in the growth tube is summarized with a system                            of Michaelis-Menten differential equations. The desired growth rate is                            attained by manually limiting the concentration of the controlling                            growth factor in the nutrient provided to the cells.
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pcbi-1000257-g002: Schematic of a chemostat.In the chemostat, cells are grown in liquid media [14]. A tank contains a large supply of nutrient containing high concentrations of all growth factors, but a limited concentration (S0) of the controlling growth factor. The nutrient flows continuously into a growth tube of limited capacity, where the culture grows. The dynamic behavior of the density of the culture (X) and of the concentration of the controlling nutrient (S) in the growth tube is summarized with a system of Michaelis-Menten differential equations. The desired growth rate is attained by manually limiting the concentration of the controlling growth factor in the nutrient provided to the cells.

Mentions: In the chemostat, a specific growth rate is maintained by limiting the concentration of a controlling nutrient provided to the cells [14]. Figure 2 illustrates the principle behind the chemostat. A limited concentration (S0 in the tank) of the controlling growth factor is provided in media flowing continuously into a growth tube of limited capacity. Changes in density of the culture, X, and in concentration of the controlling nutrient (S), in the growth tube, are driven by Michaelis-Menten dynamics [15]. In this regime, the growth rate is a function of the concentration of the controlling nutrient, μ = μ(S). In particular, dX/dt = [μ(S)−D] X; at steady state, the density of the culture no longer changes, dX/dt = 0, and the concentration of the controlling growth factor also stabilizes, dS/dt = 0. The growth rate then equals the flow rate set by the experimenter, μ(S*) = D.


Predicting cellular growth from gene expression signatures.

Airoldi EM, Huttenhower C, Gresham D, Lu C, Caudy AA, Dunham MJ, Broach JR, Botstein D, Troyanskaya OG - PLoS Comput. Biol. (2009)

Schematic of a chemostat.In the chemostat, cells are grown in liquid media [14]. A tank                            contains a large supply of nutrient containing high concentrations of                            all growth factors, but a limited concentration (S0) of the                            controlling growth factor. The nutrient flows continuously into a growth                            tube of limited capacity, where the culture grows. The dynamic behavior                            of the density of the culture (X) and of the concentration of the                            controlling nutrient (S) in the growth tube is summarized with a system                            of Michaelis-Menten differential equations. The desired growth rate is                            attained by manually limiting the concentration of the controlling                            growth factor in the nutrient provided to the cells.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2599889&req=5

pcbi-1000257-g002: Schematic of a chemostat.In the chemostat, cells are grown in liquid media [14]. A tank contains a large supply of nutrient containing high concentrations of all growth factors, but a limited concentration (S0) of the controlling growth factor. The nutrient flows continuously into a growth tube of limited capacity, where the culture grows. The dynamic behavior of the density of the culture (X) and of the concentration of the controlling nutrient (S) in the growth tube is summarized with a system of Michaelis-Menten differential equations. The desired growth rate is attained by manually limiting the concentration of the controlling growth factor in the nutrient provided to the cells.
Mentions: In the chemostat, a specific growth rate is maintained by limiting the concentration of a controlling nutrient provided to the cells [14]. Figure 2 illustrates the principle behind the chemostat. A limited concentration (S0 in the tank) of the controlling growth factor is provided in media flowing continuously into a growth tube of limited capacity. Changes in density of the culture, X, and in concentration of the controlling nutrient (S), in the growth tube, are driven by Michaelis-Menten dynamics [15]. In this regime, the growth rate is a function of the concentration of the controlling nutrient, μ = μ(S). In particular, dX/dt = [μ(S)−D] X; at steady state, the density of the culture no longer changes, dX/dt = 0, and the concentration of the controlling growth factor also stabilizes, dS/dt = 0. The growth rate then equals the flow rate set by the experimenter, μ(S*) = D.

Bottom Line: The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution.We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes.More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods.

View Article: PubMed Central - PubMed

Affiliation: Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

Show MeSH
Related in: MedlinePlus