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Results: 4

1.
Figure 3

Figure 3. From: Variable sizes of Escherichia coli chemoreceptor signaling teams.

Individual receptor-activity dose–response curves (symbols) and corresponding PCA fits (solid lines) for E. coli cells expressing only Tar receptors at high (∼3.6 × native) expression level (see Supplementary information). Cell types include adapting (CheRB+) and non-adapting, engineered cheRcheB mutants (QEEE, QEQE, QEQQ, and QQQQ). CheRB+ cells are adapted either to zero attractant (x symbols) or to 0.1 mM MeAsp (+ symbols). The arrows indicate the adapted activity of 0.2. Measurement of the dose–response curves of CheRB+ cells adapted to 0.1 mM MeAsp required not only addition but also removal of MeAsp. The experimental data were normalized by the inverse amplitude φ−1 (see Materials and methods, and Supplementary Table I).

Robert G Endres, et al. Mol Syst Biol. 2008;4:211-211.
2.
Figure 4

Figure 4. From: Variable sizes of Escherichia coli chemoreceptor signaling teams.

Sizes of receptor signaling teams N (number of receptor dimers per signaling team, panels A and B) and offset energies Δɛ (panels C and D) obtained from PCA fits shown in Figure 3 in the main text and Supplementary Figure 7. The left panels A and C correspond to low (∼1.4 × native) expression of Tar receptors, whereas the right panels B and D correspond to high (∼3.6 × native) expression (see Materials and methods). Cell types include adapting (CheRB+) and non-adapting, engineered cheRcheB mutants (QEEE, QEQE, QEQQ, and QQQQ). For numerical values of parameters and confidence intervals, see Supplementary Table I.

Robert G Endres, et al. Mol Syst Biol. 2008;4:211-211.
3.
Figure 1

Figure 1. From: Variable sizes of Escherichia coli chemoreceptor signaling teams.

Determining sizes of receptor signaling teams from signaling dose–response curves. Signaling-team size N, i.e. the number of receptor dimers per signaling team, may differ depending on the receptor modification state (glutamate (E) or glutamine (Q) at four specific receptor modification sites). Variable signaling-team sizes are illustrated schematically by membrane patches of trimers of dimers (blue circles) with signaling teams of two trimers (N=6 dimers) for Tar{QEEE}, or signaling teams of six trimers (N=18 dimers) for Tar{QQQQ}. When directly fitting the free-energy model of receptor activity to the noisy data, the fitted curves and model parameters vary widely, due to the large error bars (middle panel), preventing quantitative analysis of signaling-team sizes. However, use of principal component analysis (PCA) separates the reproducible variation of the data from the sampling noise, allowing quantitative evaluation of parameters, and revealing a systematic dependence of signaling-team size on receptor modification level in living cells.

Robert G Endres, et al. Mol Syst Biol. 2008;4:211-211.
4.
Figure 2

Figure 2. From: Variable sizes of Escherichia coli chemoreceptor signaling teams.

Illustration of principal component analysis (PCA) applied to dose–response curves of receptor activity to obtain principal modes of data variation. Receptor activity at various concentrations of attractant (MeAsp) was measured through in vivo FRET for E. coli cells expressing only Tar receptors. (A) Measured dose–response curves show large variability, as exemplified by M=7 individual curves for the receptor modification state QEQE in a cheRcheB mutant. (B) Illustration of a scatter plot of data from (A) in a space of dimension D equal to the number of different attractant concentrations (projected onto two dimensions for clarity). Each data point (square) corresponds to one dose–response curve. The average dose–response curve is shown as an open circle. PCA involves diagonalizing the covariance matrix C, where the principal components—eigenvectors νi and eigenvalues λi of C with i=1,…, D—indicate the direction and magnitude of variation of the data. The sum of the eigenvalues equals the total variance of the data. The open square illustrates our practice of leaving out one data point when determining the optimal number of principal components to be included for fitting (see Supplementary Figure 6B).

Robert G Endres, et al. Mol Syst Biol. 2008;4:211-211.

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