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BIOMD0000000590 - Hermansen2015 - denovo biosynthesis of pyrimidines in yeast

 

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Reference Publication
Publication ID: 26511837
Hermansen RA, Mannakee BK, Knecht W, Liberles DA, Gutenkunst RN.
Characterizing selective pressures on the pathway for de novo biosynthesis of pyrimidines in yeast.
BMC Evol. Biol. 2015; 15: 232
Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA. rhermans@uwyo.edu.  [more]
Model
Original Model: BIOMD0000000590.origin
Submitter: Brian Mannakee
Submission ID: MODEL1512160000
Submission Date: 16 Dec 2015 16:08:30 UTC
Last Modification Date: 16 Feb 2016 16:58:21 UTC
Creation Date: 10 Dec 2015 13:40:40 UTC
Encoders:  Vijayalakshmi Chelliah
   Brian Mannakee
   Ryan Gutenkunst
set #1
bqmodel:isDerivedFrom BioModels Database Rodriguez2005_denovo_pyrimidine_biosynthesis
set #2
bqbiol:hasTaxon Taxonomy Saccharomyces cerevisiae
set #3
bqbiol:encodes KEGG Pathway Pyrimidine metabolism
set #4
bqbiol:isVersionOf Gene Ontology pyrimidine nucleobase biosynthetic process
Notes
Hermansen2015 - denovo biosynthesis of pyrimidines in yeast

This model is described in the article:

Hermansen RA , Mannakee BK , Knecht W , Liberles DA , Gutenkunst RN
BMC Evolutionary Biology. 2015, 15:232

Abstract:

Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes.Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint.We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Model
Publication ID: 26511837 Submission Date: 16 Dec 2015 16:08:30 UTC Last Modification Date: 16 Feb 2016 16:58:21 UTC Creation Date: 10 Dec 2015 13:40:40 UTC
Mathematical expressions
Reactions
r1 r2 r3 r4
r5 r6 r10 r7
utp_degradation r8 ctp_degradation cp_dilution
ca_dilution dho_dilution oro_dilution omp_dilution
ump_dilution udp_dilution utp_dilution ctp_dilution
Physical entities
Compartments Species
compartment cp ca dho
oro omp ump
udp utp ctp
Global parameters
vmax1 [bc] [glu] K_utp
K_q K_bc vmax2 [asp]
[atp] K_atp K_m2 vmax3
K_m3 vmax4 K_m4 vmax5
K_m5 [prpp] vmax6 K_m6
vmax10 K_m10 vmax7 K_m7
g_pyr K_Mp vmax8 K_m8
K_asp d    
Reactions (20)
 
 r1  ↔ [cp];   {utp} , {utp}
 
 r2 [cp] ↔ [ca];   {utp} , {cp} , {utp}
 
 r3 [ca] ↔ [dho];   {ca}
 
 r4 [dho] ↔ [oro];   {dho}
 
 r5 [oro] ↔ [omp];   {oro}
 
 r6 [omp] ↔ [ump];   {omp}
 
 r10 [ump] ↔ [udp];   {ump}
 
 r7 [udp] ↔ [utp];   {udp}
 
 utp_degradation [utp] ↔ ;   {utp}
 
 r8 [utp] ↔ [ctp];   {utp}
 
 ctp_degradation [ctp] ↔ ;   {ctp}
 
 cp_dilution [cp] ↔ ;   {cp}
 
 ca_dilution [ca] ↔ ;   {ca}
 
 dho_dilution [dho] ↔ ;   {dho}
 
 oro_dilution [oro] ↔ ;   {oro}
 
 omp_dilution [omp] ↔ ;   {omp}
 
 ump_dilution [ump] ↔ ;   {ump}
 
 udp_dilution [udp] ↔ ;   {udp}
 
 utp_dilution [utp] ↔ ;   {utp}
 
 ctp_dilution [ctp] ↔ ;   {ctp}
 
Functions (20)
 
 Function for r2 lambda(K_asp, K_m2, K_utp, asp, compartment, cp, utp, vmax2, vmax2*cp*asp/((1+utp/K_utp)*(K_m2+cp)*(K_asp+asp))/compartment)
 
 Function for r1 lambda(K_atp, K_bc, K_q, K_utp, atp, bc, compartment, glu, utp, vmax1, vmax1*bc*glu*atp/((1+utp/K_utp)*(K_atp+atp)*(K_bc+bc)*(K_q+glu))/compartment)
 
 Function for r3 lambda(K_m3, ca, compartment, vmax3, vmax3*ca/(K_m3+ca)/compartment)
 
 Function for r4 lambda(K_m4, compartment, dho, vmax4, vmax4*dho/(K_m4+dho)/compartment)
 
 Function for r5 lambda(K_m5, compartment, oro, prpp, vmax5, vmax5*oro*prpp/(K_m5+oro*prpp)/compartment)
 
 Function for r6 lambda(K_m6, compartment, omp, vmax6, vmax6*omp/(K_m6+omp)/compartment)
 
 Function for r10 lambda(K_m10, compartment, ump, vmax10, vmax10*ump/(K_m10+ump)/compartment)
 
 Function for r7 lambda(K_m7, compartment, udp, vmax7, vmax7*udp/(K_m7+udp)/compartment)
 
 Function for utp_degradation lambda(K_Mp, compartment, g_pyr, utp, g_pyr*utp/(K_Mp+utp)/compartment)
 
 Function for r8 lambda(K_m8, compartment, utp, vmax8, vmax8*utp/(K_m8+utp)/compartment)
 
 Function for ctp_degradation lambda(K_Mp, compartment, ctp, g_pyr, g_pyr*ctp/(K_Mp+ctp)/compartment)
 
 Function for cp_dilution lambda(compartment, cp, d, d*cp/compartment)
 
 Function for ca_dilution lambda(ca, compartment, d, d*ca/compartment)
 
 Function for dho_dilution lambda(compartment, d, dho, d*dho/compartment)
 
 Function for oro_dilution lambda(compartment, d, oro, d*oro/compartment)
 
 Function for omp_dilution lambda(compartment, d, omp, d*omp/compartment)
 
 Function for ump_dilution lambda(compartment, d, ump, d*ump/compartment)
 
 Function for udp_dilution lambda(compartment, d, udp, d*udp/compartment)
 
 Function for utp_dilution lambda(compartment, d, utp, d*utp/compartment)
 
 Function for ctp_dilution lambda(compartment, ctp, d, d*ctp/compartment)
 
   compartment Spatial dimensions: 3.0  Compartment size: 1.0
 
 cp
Compartment: compartment
Initial concentration: 3.7E-4
 
 ca
Compartment: compartment
Initial concentration: 3.7E-4
 
 dho
Compartment: compartment
Initial concentration: 3.7E-4
 
 oro
Compartment: compartment
Initial concentration: 3.7E-4
 
 omp
Compartment: compartment
Initial concentration: 3.7E-4
 
 ump
Compartment: compartment
Initial concentration: 3.7E-4
 
 udp
Compartment: compartment
Initial concentration: 0.002886
 
 utp
Compartment: compartment
Initial concentration: 0.00666
 
 ctp
Compartment: compartment
Initial concentration: 3.7E-4
 
Global Parameters (30)
 
   vmax1
Value: 3.61602627459517
Constant
 
   [bc]
Value: 1.52264278250403
Constant
 
   [glu]
Value: 0.54620785996429
Constant
 
   K_utp
Value: 1.413855257896
Constant
 
   K_q
Value: 0.05784981576165
Constant
 
   K_bc
Value: 2.3716657188714
Constant
 
   vmax2
Value: 2.44590712912244
Constant
 
   [asp]
Value: 0.0972544685826559
Constant
 
   [atp]
Value: 0.150650172583633
Constant
 
   K_atp
Value: 1.28940553329954
Constant
 
   K_m2
Value: 2.00489353757245
Constant
 
   vmax3
Value: 28.6613123782585
Constant
 
   K_m3
Value: 1.27179181717468
Constant
 
   vmax4
Value: 91.7802875108298
Constant
 
   K_m4
Value: 0.0160033122150611
Constant
 
   vmax5
Value: 5227.49670547203
Constant
 
   K_m5
Value: 0.0195216150005324
Constant
 
   [prpp]
Value: 0.181644900226225
Constant
 
   vmax6
Value: 34.9720846528477
Constant
 
   K_m6
Value: 20.3406449182435
Constant
 
   vmax10
Value: 6.55543523218919
Constant
 
   K_m10
Value: 0.0267841313759584
Constant
 
   vmax7
Value: 5.83104141997666
Constant
 
   K_m7
Value: 0.166382738667754
Constant
 
   g_pyr
Value: 0.198306450999093
Constant
 
   K_Mp
Value: 5.48714446027226
Constant
 
   vmax8
Value: 0.162943604164789
Constant
 
   K_m8
Value: 0.00435621076587497
Constant
 
   K_asp
Value: 0.168308889432487
Constant
 
   d
Value: 0.1
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000590

Curator's comment: (updated: 16 Feb 2016 16:43:39 GMT)

There are no simulation plots in the paper, so Table 3, which is the steady-state concentrations of the metabolites has been reproduced here. The stead-state concentrations were calculated using Copasi v4.15 (Build 95).

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