MOESM1 of Clostridium thermocellum DSM 1313 transcriptional responses to redox perturbation

Additional file 1. Batch fermentation performance under methyl viologen and hydrogen peroxide initial loadings

MOESM2 of Clostridium thermocellum DSM 1313 transcriptional responses to redox perturbation

Additional file 2. Calculated differential expression and adjusted p values for genes showing significant (adjusted p value < 0.05) differential expression during at least one timepoint of either methyl viologen or hydrogen peroxide exposure

MOESM3 of Clostridium thermocellum DSM 1313 transcriptional responses to redox perturbation

Additional file 3. (A) Adjusted OD600 of batch cultures grown at various initial hydrogen peroxide concentrations. Cultures were grown in MTC media containing 1.1 g/L cellobiose; (B) Chemostat OD600 and measured redox potential before, during and after hydrogen peroxide addition; (C) Detailed view of boxed region indicated in panel (B)

Dynamic control of NFV forwarding graphs with end-to-end deadline constraints

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwarding graph. The model captures uncertainties of the performance of these virtual resources as well as the time-overhead needed to instantiate them. The model is used to derive a service controller for horizontal scaling of the virtual resources as well as an admission controller that guarantees that packets exiting the forwarding graph meet their end-to-end deadline. The Automatic Service and Admission Controller (AutoSAC) developed in this work uses feedback and feedforward making it robust against uncertainties of the underlying infrastructure. Also, it has a fast reaction time to changes in the input

MOESM1 of Clostridium thermocellum LL1210 pH homeostasis mechanisms informed by transcriptomics and metabolomics

Additional file 1. Data logs, substrate and product concentrations, internal and external pH readings, and media formulation for chemostat reactor cultures of C. thermocellum LL1210

MOESM2 of Clostridium thermocellum LL1210 pH homeostasis mechanisms informed by transcriptomics and metabolomics

Additional file 2. Raw and processed read counts, alignment statistics, log2-fold changes in the gene expression, and K-means clusters and GO enrichment of differentially expressed genes from samples taken from C. thermocellum LL1210 cultured in chemostats at pH values 6.98, 6.48, pH 6.24, and pH 6.12 (washout conditions). Gene expression at pH 6.98 was used as a reference for differential expression at lower pH values

MOESM8 of Clostridium thermocellum LL1210 pH homeostasis mechanisms informed by transcriptomics and metabolomics

Additional file 8: Table S4. Strains and primers used in this study

MOESM7 of Clostridium thermocellum LL1210 pH homeostasis mechanisms informed by transcriptomics and metabolomics

Additional file 7. Extracellular amino acid concentrations in media from C. thermocellum LL1210 chemostats that were sampled when pH values were pH 6.48, pH 6.24, pH 6.12, and below. Demonstrations of data homoscedasticity for T tests

MOESM3 of Clostridium thermocellum LL1210 pH homeostasis mechanisms informed by transcriptomics and metabolomics

Additional file 3: Figure S1. Average optical density at wave-length 600nm (A and C) and average terminal pH (B and D) of C. thermocellum LL1210 cultured in 48-well plates. OD600nm readings were taken automatically every 15 min in a microplate spectrophotometer (Biotek Eon, Winooski, VT) kept in an anaerobic chamber. Only 3-h time points are shown. Nine hundred microliters of inoculated medium was mixed with 100 Îźl of uninoculated medium supplemented with spermine, spermidine, or putrescine (polyamines), or arginine (polyamine precursor) so that the final concentration was 100 ÎźM. Initial culture pH was 7.00 (A and B) or 6.75 (C and D). Averages were calculated from at least three biological replicates. Error bars indicate standard deviation and are colored the same as the amendments in the legend. Table S1. Average and standard deviation of maximum and terminal optical densities (600nm) and specific growth rate of C. thermocellum LL1210 cultured in media with and without amendments and having initial pHs of 7.00 and 6.75

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Additional file 5: Figure S3. Differential expression of genes found in Clostridia sporulation cascades. pro-σE processing protease is a stage III sporulation factor. BofA is an inhibitor of the stage IV pro-σK processing protease SpoIVFB. Table S2. Percentage of spherical morphologies 144 and 216 h after inoculation. Figure S4. Substrates and products (A) and the pH (B) after 144 and 216 h of C. thermocellum-mutant fermentations on MOPS-free carbon-replete medium starting with an initial pH of 6.75. Significant differences at α = 0.001 for comparisons with DSM1313 (∆hpt) are indicated with a “*” and comparisons with DSM1313 (∆hpt) and LL1210 are indicated with “**”. Averages were calculated with six biological replicates. Error bars indicate standard deviation