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Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR - Mol. Syst. Biol. (2015)

Bottom Line: The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters.The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell.Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of California at San Diego, La Jolla, CA, USA.

No MeSH data available.


Related in: MedlinePlus

Proteome fractions under growth limitations with respect to the glycerol standard condition, and under growth limitation by expressing useless proteinsProteome fractions φσ for C- and A-limitation under glycerol standard condition are shown as the red and blue circles, respectively, for each of the six sectors; see Supplementary Table S9 for values. All thick lines are model predictions for responses under the glycerol standard condition. Thick solid lines describe responses which are predicted to be unchanged between the glucose and glycerol standard conditions, because these lines do not involve the parameter νC, which has a new value for the new standard condition according to the model. Thick dashed lines describe responses which are predicted to be unique for the glycerol standard condition, due to their dependence on the value of parameter νC. See Supplementary Fig S12 describing the dashed lines for the C-, A-, R-, and S-sectors. For comparison, the four respective proteome responses under glucose standard condition are also shown as thin solid lines. All solid lines are from Fig5. Note that the new value of νC is determined from the growth rate of cells in glycerol standard condition (Supplementary Table S7). Thus, all predictions for the glycerol standard condition were generated with no adjustable parameters. Proteome fractions under growth limitation by LacZ overexpression are shown as the black triangles for the six sectors; see Supplementary Table S9 for values.
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fig07: Proteome fractions under growth limitations with respect to the glycerol standard condition, and under growth limitation by expressing useless proteinsProteome fractions φσ for C- and A-limitation under glycerol standard condition are shown as the red and blue circles, respectively, for each of the six sectors; see Supplementary Table S9 for values. All thick lines are model predictions for responses under the glycerol standard condition. Thick solid lines describe responses which are predicted to be unchanged between the glucose and glycerol standard conditions, because these lines do not involve the parameter νC, which has a new value for the new standard condition according to the model. Thick dashed lines describe responses which are predicted to be unique for the glycerol standard condition, due to their dependence on the value of parameter νC. See Supplementary Fig S12 describing the dashed lines for the C-, A-, R-, and S-sectors. For comparison, the four respective proteome responses under glucose standard condition are also shown as thin solid lines. All solid lines are from Fig5. Note that the new value of νC is determined from the growth rate of cells in glycerol standard condition (Supplementary Table S7). Thus, all predictions for the glycerol standard condition were generated with no adjustable parameters. Proteome fractions under growth limitation by LacZ overexpression are shown as the black triangles for the six sectors; see Supplementary Table S9 for values.

Mentions: Among the 10 parameters of the model, the four values of νσs are dependent on the growth medium, while the φσ,0s as well as the constant f ≈ 0.32 are expected to be medium independent for a given strain. All of the data described so far (summarized in Fig3) were obtained using glucose minimal medium as the standard condition (with the νσs taking on the values ), with each mode of growth limitation corresponding to varying one of the νσs away from . The proteome flux model also makes explicit predictions on the response of the proteome under combinatorial modes of growth limitation, corresponding to varying multiple νσs. The effect of varying multiple νσs can be treated as simply repeating the single mode of growth limitations for different standard conditions. This prediction was tested by repeating the proteomic experiments under C- and A-limitations using a different standard condition, for growth in the glycerol minimal medium (Supplementary Table S8). Compared to the standard condition with glucose minimal medium, the glycerol minimal medium should differ by only the value of νC, which is fixed by the growth rate for the glycerol standard condition (Supplementary Table S7). Using this new value of νC, together with the values of the other nine parameters obtained from the glucose data, the model describes the new data remarkably well (Fig7; Supplementary Table S9). Thus, the model can describe experiments in different standard conditions, an important benchmark for its ability to capture proteome responses to combinatorial limitations.


Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR - Mol. Syst. Biol. (2015)

Proteome fractions under growth limitations with respect to the glycerol standard condition, and under growth limitation by expressing useless proteinsProteome fractions φσ for C- and A-limitation under glycerol standard condition are shown as the red and blue circles, respectively, for each of the six sectors; see Supplementary Table S9 for values. All thick lines are model predictions for responses under the glycerol standard condition. Thick solid lines describe responses which are predicted to be unchanged between the glucose and glycerol standard conditions, because these lines do not involve the parameter νC, which has a new value for the new standard condition according to the model. Thick dashed lines describe responses which are predicted to be unique for the glycerol standard condition, due to their dependence on the value of parameter νC. See Supplementary Fig S12 describing the dashed lines for the C-, A-, R-, and S-sectors. For comparison, the four respective proteome responses under glucose standard condition are also shown as thin solid lines. All solid lines are from Fig5. Note that the new value of νC is determined from the growth rate of cells in glycerol standard condition (Supplementary Table S7). Thus, all predictions for the glycerol standard condition were generated with no adjustable parameters. Proteome fractions under growth limitation by LacZ overexpression are shown as the black triangles for the six sectors; see Supplementary Table S9 for values.
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Related In: Results  -  Collection

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fig07: Proteome fractions under growth limitations with respect to the glycerol standard condition, and under growth limitation by expressing useless proteinsProteome fractions φσ for C- and A-limitation under glycerol standard condition are shown as the red and blue circles, respectively, for each of the six sectors; see Supplementary Table S9 for values. All thick lines are model predictions for responses under the glycerol standard condition. Thick solid lines describe responses which are predicted to be unchanged between the glucose and glycerol standard conditions, because these lines do not involve the parameter νC, which has a new value for the new standard condition according to the model. Thick dashed lines describe responses which are predicted to be unique for the glycerol standard condition, due to their dependence on the value of parameter νC. See Supplementary Fig S12 describing the dashed lines for the C-, A-, R-, and S-sectors. For comparison, the four respective proteome responses under glucose standard condition are also shown as thin solid lines. All solid lines are from Fig5. Note that the new value of νC is determined from the growth rate of cells in glycerol standard condition (Supplementary Table S7). Thus, all predictions for the glycerol standard condition were generated with no adjustable parameters. Proteome fractions under growth limitation by LacZ overexpression are shown as the black triangles for the six sectors; see Supplementary Table S9 for values.
Mentions: Among the 10 parameters of the model, the four values of νσs are dependent on the growth medium, while the φσ,0s as well as the constant f ≈ 0.32 are expected to be medium independent for a given strain. All of the data described so far (summarized in Fig3) were obtained using glucose minimal medium as the standard condition (with the νσs taking on the values ), with each mode of growth limitation corresponding to varying one of the νσs away from . The proteome flux model also makes explicit predictions on the response of the proteome under combinatorial modes of growth limitation, corresponding to varying multiple νσs. The effect of varying multiple νσs can be treated as simply repeating the single mode of growth limitations for different standard conditions. This prediction was tested by repeating the proteomic experiments under C- and A-limitations using a different standard condition, for growth in the glycerol minimal medium (Supplementary Table S8). Compared to the standard condition with glucose minimal medium, the glycerol minimal medium should differ by only the value of νC, which is fixed by the growth rate for the glycerol standard condition (Supplementary Table S7). Using this new value of νC, together with the values of the other nine parameters obtained from the glucose data, the model describes the new data remarkably well (Fig7; Supplementary Table S9). Thus, the model can describe experiments in different standard conditions, an important benchmark for its ability to capture proteome responses to combinatorial limitations.

Bottom Line: The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters.The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell.Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of California at San Diego, La Jolla, CA, USA.

No MeSH data available.


Related in: MedlinePlus