Evolutionary change in % optimality versus initial % optimality of the ancestor across data sets for BM/S.

<p>Error bars represent standard errors between evolved populations.</p

Evolution of metabolic fluxes and measures of optimality and predictability.

<p>We consider three ways to analyze changes in metabolism that relate an ancestor (Anc, blue) to an evolved isolate (E_i, green) in regard to an FBA-predicted optimum (Opt, red). A) Evolution of metabolic fluxes can be evaluated from the perspective of changes in proximity to the theoretical maximum for a given optimality criterion (Δ% Optimality). B) A vector of flux ratios defines a position in multi-dimensional flux space. One can then consider the relative Euclidian distance of a given evolved population in this space from its optimum (D_EO) compared to that of an ancestor from its optimum (D_AO; plotted as log(D_EO/D_AO)). C) At the most detailed level, one can compare the FBA-predicted value for a given flux ratio versus that observed via ¹³C labeling.</p

Measures of optimality and predictability after adaptation of gene knockouts on glucose for ∼600–800 generations.

<p>A,B) The % optimality of the ancestor (black) and evolved isolates (grey); C,D) distance to optimal flux distribution for FBA-predictions based upon BM/S (A,C) or ATP/S (B,D).</p

Evolved changes in central carbon metabolism for the LTEE populations after 50,000 generations of adaptation on glucose.

<p>A) The flux pathways measured for the LTEE lines are denoted with numbers and red arrows. The genes knocked out in the knockout data set and the entry point of lactate into the network are both indicated. B) A heat map of the difference between evolved and ancestral flux ratios from the LTEE populations. The right side indicates flux ratios predicted for the ancestral line according to each optimality criterion. The number of the flux ratio corresponds to the numbered pathways in A. Single asterisks denote significant changes as calculated by ANOVA, double asterisks are also significant by Tukey-HD.</p

Major approaches to test of FBA predictions depending upon whether there was known selection under experimental conditions and whether there was direct measurement of internal fluxes.

<p>Major approaches to test of FBA predictions depending upon whether there was known selection under experimental conditions and whether there was direct measurement of internal fluxes.</p

Additional file 2: of Microbial diversity in individuals and their household contacts following typical antibiotic courses

This file contains Figures S1–S9. (PDF 1247 kb

Additional file 1: Table S1. of Microbial diversity in individuals and their household contacts following typical antibiotic courses

Study subjects. (PDF 275 kb

PONDRing Abp1p and Las17p

<div><p>(A) The PONDR VL-XT prediction for Abp1p is plotted along with bars representing the positions of the ADF domain (N-terminal orange bar, structure 1HQZ), the SH3 domain (C-terminal orange bar, structure 1JO8), a poly-proline region (green bar), a predicted α-MoRF (blue bar), known phosphorylation sites (black hash marks), and regions critical for Arp2/3 activation (purple bars).</p><p>(B) The PONDR VL-XT prediction for Las17p is plotted along with bars representing the positions of the WASP homology domain 1 (N-terminal orange bar), WASP homology domain 2 (C-terminal orange bar), poly-proline regions (green bars), and a predicted α-MoRF (blue bar). The number of interaction partners associated with a given region [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020100#pcbi-0020100-b039" target="_blank">39</a>] is indicated in the numbered boxes.</p></div