Journal: Journal of applied microbiology

Article Title: Metabolic Modeling of Chronic Wound Microbiota Predicts Mutualistic Interactions that Drive Community Composition

doi: 10.1111/jam.14421

Figure Lengend Snippet: The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Article Snippet: The 12-species community model accounted for 16,133 reactions, 13,666 metabolites and 9,713 genes. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1: caption a7 Overview of the community modeling framework. (A) Flow chart showing steps in model development, simulation and analysis. (B) Average species abundances obtained from the model ensemble. (C) r and p values obtained from correlation analysis of the model ensemble abundance data. (D) Significant crossfeeding relationships between Staphylococcus (purple bars) and Pseudomonas (green bars) predicted by model ensemble simulations. table ft1 table-wrap mode="anchored" t5 Table 1: caption a7 Number Strain References Prevalence (%) Relative abundance 1 Staphylococcus aureus subsp aureus USA300 FPR3757 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 63 0.42 2 Pseudomonas aeruginosa NCGM2 S1 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 25 0.13 3 Corynebacterium striatum ATCC 6940 ( Dowd et al., 2008a ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 36 0.11 4 Streptococcus agalactiae A909 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 23 0.07 5 Enterococcus faecalis V583 ( Melendez et al., 2010 ; Tzaneva et al., 2016 ) 17 0.05 6 Finegoldia magna ATCC 29328 ( Dowd et al., 2008a ; Wolcott et al., 2016 ) 25 0.05 7 Anaerococcus vaginalis ATCC 51170 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 24 0.05 8 Stenotrophomonas maltophilia D457 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 19 0.04 9 Prevotella bivia DSM 20514 ( Wolcott et al., 2016 ; Jneid et al., 2017 ) 12 0.03 10 Acinetobacter baumannii AB0057 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 9 0.02 11 Serratia liquefaciens ATCC 27592 ( Alper et al., 1983 ; Bowler et al., 2001 ) 5 0.02 12 Bacteroides fragilis 3 1 12 ( Stephens et al., 2003 ; Jneid et al., 2017 ) 8 0.02 Open in a separate window The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Techniques:

Journal: Journal of applied microbiology

Article Title: Metabolic Modeling of Chronic Wound Microbiota Predicts Mutualistic Interactions that Drive Community Composition

doi: 10.1111/jam.14421

Figure Lengend Snippet: The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Article Snippet: The 12-species community model accounted for 16,133 reactions, 13,666 metabolites and 9,713 genes. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1: caption a7 Overview of the community modeling framework. (A) Flow chart showing steps in model development, simulation and analysis. (B) Average species abundances obtained from the model ensemble. (C) r and p values obtained from correlation analysis of the model ensemble abundance data. (D) Significant crossfeeding relationships between Staphylococcus (purple bars) and Pseudomonas (green bars) predicted by model ensemble simulations. table ft1 table-wrap mode="anchored" t5 Table 1: caption a7 Number Strain References Prevalence (%) Relative abundance 1 Staphylococcus aureus subsp aureus USA300 FPR3757 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 63 0.42 2 Pseudomonas aeruginosa NCGM2 S1 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 25 0.13 3 Corynebacterium striatum ATCC 6940 ( Dowd et al., 2008a ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 36 0.11 4 Streptococcus agalactiae A909 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 23 0.07 5 Enterococcus faecalis V583 ( Melendez et al., 2010 ; Tzaneva et al., 2016 ) 17 0.05 6 Finegoldia magna ATCC 29328 ( Dowd et al., 2008a ; Wolcott et al., 2016 ) 25 0.05 7 Anaerococcus vaginalis ATCC 51170 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 24 0.05 8 Stenotrophomonas maltophilia D457 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 19 0.04 9 Prevotella bivia DSM 20514 ( Wolcott et al., 2016 ; Jneid et al., 2017 ) 12 0.03 10 Acinetobacter baumannii AB0057 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 9 0.02 11 Serratia liquefaciens ATCC 27592 ( Alper et al., 1983 ; Bowler et al., 2001 ) 5 0.02 12 Bacteroides fragilis 3 1 12 ( Stephens et al., 2003 ; Jneid et al., 2017 ) 8 0.02 Open in a separate window The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Techniques:

Journal: Integrating Materials and Manufacturing Innovation

Article Title: A Computational Framework for Material Design

doi: 10.1007/s40192-017-0101-8

Figure Lengend Snippet: Kinetic modeling of Ni-Al-Cr ternary alloys a number density, b average radius, c volume fraction of γ ′; the points are the experimental results from [6, 7, 84] and the dotted lines are the equilibrium Vfγ ′ from TCNI6

Article Snippet: The successful model-validation processes can be used for the next design iteration [ 44 , 64 ]. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Fig. 16 caption a7 a Parameter value origin and data flow in this framework: the physics-based parameters are calculated using CALPHAD method and physics-based models; empirical parameters are obtained from references; b the values of the adjustable parameters During the optimization process, the composition-dependent parameters such as phase transition temperature, equilibrium phase composition, lattice parameters, diffusion coefficients, etc. are obtained using Thermo-Calc TC-API with TCNI6 and NIST Ni-mobility databases.

Techniques:

Journal: Integrating Materials and Manufacturing Innovation

Article Title: A Computational Framework for Material Design

doi: 10.1007/s40192-017-0101-8

Figure Lengend Snippet: Phase diagram of Ni-Al-Cr calculated using Thermo-Calc with TCNI6 database at a 973 K and b 1473 K; the

Article Snippet: The successful model-validation processes can be used for the next design iteration [ 44 , 64 ]. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Fig. 16 caption a7 a Parameter value origin and data flow in this framework: the physics-based parameters are calculated using CALPHAD method and physics-based models; empirical parameters are obtained from references; b the values of the adjustable parameters During the optimization process, the composition-dependent parameters such as phase transition temperature, equilibrium phase composition, lattice parameters, diffusion coefficients, etc. are obtained using Thermo-Calc TC-API with TCNI6 and NIST Ni-mobility databases.

Techniques:

Journal: Integrating Materials and Manufacturing Innovation

Article Title: A Computational Framework for Material Design

doi: 10.1007/s40192-017-0101-8

Figure Lengend Snippet: Comparison of results from calculations using the TCNI6 and Dupin [25] thermodynamic databases: a the mole fraction of γ ′ b the chemical composition in γ ′

Article Snippet: The successful model-validation processes can be used for the next design iteration [ 44 , 64 ]. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Fig. 16 caption a7 a Parameter value origin and data flow in this framework: the physics-based parameters are calculated using CALPHAD method and physics-based models; empirical parameters are obtained from references; b the values of the adjustable parameters During the optimization process, the composition-dependent parameters such as phase transition temperature, equilibrium phase composition, lattice parameters, diffusion coefficients, etc. are obtained using Thermo-Calc TC-API with TCNI6 and NIST Ni-mobility databases.

Techniques:

Journal: Journal of applied microbiology

Article Title: Metabolic Modeling of Chronic Wound Microbiota Predicts Mutualistic Interactions that Drive Community Composition

doi: 10.1111/jam.14421

Figure Lengend Snippet: The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Article Snippet: The 12-species community model accounted for 16,133 reactions, 13,666 metabolites and 9,713 genes. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1: caption a7 Overview of the community modeling framework. (A) Flow chart showing steps in model development, simulation and analysis. (B) Average species abundances obtained from the model ensemble. (C) r and p values obtained from correlation analysis of the model ensemble abundance data. (D) Significant crossfeeding relationships between Staphylococcus (purple bars) and Pseudomonas (green bars) predicted by model ensemble simulations. table ft1 table-wrap mode="anchored" t5 Table 1: caption a7 Number Strain References Prevalence (%) Relative abundance 1 Staphylococcus aureus subsp aureus USA300 FPR3757 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 63 0.42 2 Pseudomonas aeruginosa NCGM2 S1 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 25 0.13 3 Corynebacterium striatum ATCC 6940 ( Dowd et al., 2008a ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 36 0.11 4 Streptococcus agalactiae A909 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 23 0.07 5 Enterococcus faecalis V583 ( Melendez et al., 2010 ; Tzaneva et al., 2016 ) 17 0.05 6 Finegoldia magna ATCC 29328 ( Dowd et al., 2008a ; Wolcott et al., 2016 ) 25 0.05 7 Anaerococcus vaginalis ATCC 51170 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 24 0.05 8 Stenotrophomonas maltophilia D457 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 19 0.04 9 Prevotella bivia DSM 20514 ( Wolcott et al., 2016 ; Jneid et al., 2017 ) 12 0.03 10 Acinetobacter baumannii AB0057 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 9 0.02 11 Serratia liquefaciens ATCC 27592 ( Alper et al., 1983 ; Bowler et al., 2001 ) 5 0.02 12 Bacteroides fragilis 3 1 12 ( Stephens et al., 2003 ; Jneid et al., 2017 ) 8 0.02 Open in a separate window The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Techniques:

Journal: Journal of applied microbiology

Article Title: Metabolic Modeling of Chronic Wound Microbiota Predicts Mutualistic Interactions that Drive Community Composition

doi: 10.1111/jam.14421

Figure Lengend Snippet: The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Article Snippet: The 12-species community model accounted for 16,133 reactions, 13,666 metabolites and 9,713 genes. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1: caption a7 Overview of the community modeling framework. (A) Flow chart showing steps in model development, simulation and analysis. (B) Average species abundances obtained from the model ensemble. (C) r and p values obtained from correlation analysis of the model ensemble abundance data. (D) Significant crossfeeding relationships between Staphylococcus (purple bars) and Pseudomonas (green bars) predicted by model ensemble simulations. table ft1 table-wrap mode="anchored" t5 Table 1: caption a7 Number Strain References Prevalence (%) Relative abundance 1 Staphylococcus aureus subsp aureus USA300 FPR3757 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 63 0.42 2 Pseudomonas aeruginosa NCGM2 S1 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 25 0.13 3 Corynebacterium striatum ATCC 6940 ( Dowd et al., 2008a ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 36 0.11 4 Streptococcus agalactiae A909 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 23 0.07 5 Enterococcus faecalis V583 ( Melendez et al., 2010 ; Tzaneva et al., 2016 ) 17 0.05 6 Finegoldia magna ATCC 29328 ( Dowd et al., 2008a ; Wolcott et al., 2016 ) 25 0.05 7 Anaerococcus vaginalis ATCC 51170 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 24 0.05 8 Stenotrophomonas maltophilia D457 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 19 0.04 9 Prevotella bivia DSM 20514 ( Wolcott et al., 2016 ; Jneid et al., 2017 ) 12 0.03 10 Acinetobacter baumannii AB0057 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 9 0.02 11 Serratia liquefaciens ATCC 27592 ( Alper et al., 1983 ; Bowler et al., 2001 ) 5 0.02 12 Bacteroides fragilis 3 1 12 ( Stephens et al., 2003 ; Jneid et al., 2017 ) 8 0.02 Open in a separate window The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Techniques:

Journal: Journal of applied microbiology

Article Title: Metabolic Modeling of Chronic Wound Microbiota Predicts Mutualistic Interactions that Drive Community Composition

doi: 10.1111/jam.14421

Figure Lengend Snippet: The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Article Snippet: The 12-species community model accounted for 16,133 reactions, 13,666 metabolites and 9,713 genes. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1: caption a7 Overview of the community modeling framework. (A) Flow chart showing steps in model development, simulation and analysis. (B) Average species abundances obtained from the model ensemble. (C) r and p values obtained from correlation analysis of the model ensemble abundance data. (D) Significant crossfeeding relationships between Staphylococcus (purple bars) and Pseudomonas (green bars) predicted by model ensemble simulations. table ft1 table-wrap mode="anchored" t5 Table 1: caption a7 Number Strain References Prevalence (%) Relative abundance 1 Staphylococcus aureus subsp aureus USA300 FPR3757 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 63 0.42 2 Pseudomonas aeruginosa NCGM2 S1 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 25 0.13 3 Corynebacterium striatum ATCC 6940 ( Dowd et al., 2008a ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 36 0.11 4 Streptococcus agalactiae A909 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 23 0.07 5 Enterococcus faecalis V583 ( Melendez et al., 2010 ; Tzaneva et al., 2016 ) 17 0.05 6 Finegoldia magna ATCC 29328 ( Dowd et al., 2008a ; Wolcott et al., 2016 ) 25 0.05 7 Anaerococcus vaginalis ATCC 51170 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 24 0.05 8 Stenotrophomonas maltophilia D457 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 19 0.04 9 Prevotella bivia DSM 20514 ( Wolcott et al., 2016 ; Jneid et al., 2017 ) 12 0.03 10 Acinetobacter baumannii AB0057 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 9 0.02 11 Serratia liquefaciens ATCC 27592 ( Alper et al., 1983 ; Bowler et al., 2001 ) 5 0.02 12 Bacteroides fragilis 3 1 12 ( Stephens et al., 2003 ; Jneid et al., 2017 ) 8 0.02 Open in a separate window The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Techniques:

Journal: Journal of applied microbiology

Article Title: Metabolic Modeling of Chronic Wound Microbiota Predicts Mutualistic Interactions that Drive Community Composition

doi: 10.1111/jam.14421

Figure Lengend Snippet: The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Article Snippet: The 12-species community model accounted for 16,133 reactions, 13,666 metabolites and 9,713 genes. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1: caption a7 Overview of the community modeling framework. (A) Flow chart showing steps in model development, simulation and analysis. (B) Average species abundances obtained from the model ensemble. (C) r and p values obtained from correlation analysis of the model ensemble abundance data. (D) Significant crossfeeding relationships between Staphylococcus (purple bars) and Pseudomonas (green bars) predicted by model ensemble simulations. table ft1 table-wrap mode="anchored" t5 Table 1: caption a7 Number Strain References Prevalence (%) Relative abundance 1 Staphylococcus aureus subsp aureus USA300 FPR3757 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 63 0.42 2 Pseudomonas aeruginosa NCGM2 S1 ( Melendez et al., 2010 ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 25 0.13 3 Corynebacterium striatum ATCC 6940 ( Dowd et al., 2008a ; Rhoads et al., 2012 ; Wolcott et al., 2016 ) 36 0.11 4 Streptococcus agalactiae A909 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 23 0.07 5 Enterococcus faecalis V583 ( Melendez et al., 2010 ; Tzaneva et al., 2016 ) 17 0.05 6 Finegoldia magna ATCC 29328 ( Dowd et al., 2008a ; Wolcott et al., 2016 ) 25 0.05 7 Anaerococcus vaginalis ATCC 51170 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 24 0.05 8 Stenotrophomonas maltophilia D457 ( Rhoads et al., 2012 ; Wolcott et al., 2016 ) 19 0.04 9 Prevotella bivia DSM 20514 ( Wolcott et al., 2016 ; Jneid et al., 2017 ) 12 0.03 10 Acinetobacter baumannii AB0057 ( Rhoads et al., 2012 ; Jneid et al., 2017 ) 9 0.02 11 Serratia liquefaciens ATCC 27592 ( Alper et al., 1983 ; Bowler et al., 2001 ) 5 0.02 12 Bacteroides fragilis 3 1 12 ( Stephens et al., 2003 ; Jneid et al., 2017 ) 8 0.02 Open in a separate window The 12 species included in the chronic wound community model along with the prevalences and normalized average abundances of the associated genera from ( Wolcott et al., 2016 ).

Techniques: