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Open Access Highly Accessed Research article

Regulation of the yeast metabolic cycle by transcription factors with periodic activities

Aliz R Rao1 and Matteo Pellegrini2

Author Affiliations

1 Bioinformatics Interdepartmental Program, University of California, Los Angeles, USA

2 Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, USA

BMC Systems Biology 2011, 5:160  doi:10.1186/1752-0509-5-160

Published: 12 October 2011

Additional files

Additional file 1:

Time-translation matrix with no constraints. Shaded entries show significant interactions between transcription factors, with a significance threshold of 0.5. Entries shaded darker are positive values, lighter are negative values. Italics indicate that the interaction was not included in the graphical representation of the transition matrix (Figure 7), because an interaction with a greater magnitude exists in the opposite direction.

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Additional file 2:

Time-translation matrix with constraint to produce non-negative entries. Shaded entries show significant interactions between transcription factors, with a significance threshold of 0.5.

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Additional file 3:

Model residuals for two phases of the yeast metabolic cycle. Residuals were calculated from A) the transition matrix constrained for non-negative entries and B) the non-constrained transition matrix.

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Additional file 4:

Goodness of fit for multiple linear regression. Estimates of the square root of residual variance, σ, are reported for each time point and were calulated by the MATLAB function robustfit in order to aggregate the residuals into a single measure of predictive power. First, a σ estimate (root-mean-square-error) is calculated from ordinary least squares (σOLS), and a robust estimate of sigma (σrobust) is also calculated. The final estimate of σ is the larger of σrobust and a weighted average of σOLS and σrobust. Note that σ is equal to median absolute deviation (MAD) of the residuals from their median, scaled to make the estimate unbiased for the normal distribution: σ = MAD/0.6745. Also shown are the mean of the residuals at each time point. To put residuals on a comparable scale, they are "studentized," that is, they are divided by an estimate of their standard deviation that is independent of their value.

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Additional file 5:

Autocorrelation function. MATLAB code for calculating the autocorrelation function of transcription factor α-coefficients.

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