JWS Online

Model

Name

yugi1

Notes

The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000540) Biomodels notes: The time course of [F1,6BP] based on experimental data, for the varying concentrations of insulin (0.01 nM (blue) and 1 nM (green), as presented in Figure 6C (solid blue and green plot) of the reference publication, is reproduced here. The model as such reproduces the green plot (1 nM insulin). The model with 0.01 nM insulin concentration that was used to obtain the blue plot is provided as additional file (see below). JWS Online curation: This model was curated by reproducing the figures as described in the BioModels Notes. No additional changes were made.

Substance units

None

Time units

None

Volume units

None

Area units

None

Length units

None

Reaction extent units

None

Manuscript information

Reconstruction of insulin signal flow from phosphoproteome and metabolome data.

Katsuyuki Yugi
Hiroyuki Kubota
Yu Toyoshima
Rei Noguchi
Kentaro Kawata
Yasunori Komori
Shinsuke Uda
Katsuyuki Kunida
Yoko Tomizawa
Yosuke Funato
Hiroaki Miki
Masaki Matsumoto
Keiichi I Nakayama
Kasumi Kashikura
Keiko Endo
Kazutaka Ikeda
Tomoyoshi Soga
Shinya Kuroda

Cell Rep 2014; 8 (4): 1171-1183

PubMed: 25131207
DOI: 10.1016/j.celrep.2014.07.021
ISSN: 2211-1247
URL: https://ncbi.nlm.nih.gov/pubmed/25131207

Abstract

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

Simulations using this model 1

Simulation
yugi2014_Fig6C	SED-ML COMBINE Archive

Unit definitions 6

Unit definitions have no effect on the numerical analysis of the model. It remains the responsibility of the modeler to ensure the internal numerical consistency of the model. If units are provided, however, the consistency of the model units will be checked.

Name	Definition
—	1.0 mole
—	1.0 litre
—	1.0 metre^(2.0)
—	1.0 metre
—	1.0 second
—	1.0 dimensionless

Compartments 1

Id	Name	Spatial dimensions	Size
default	—	3.0	1.0 volume

Species 22

Id	Name	Initial quantity	Compartment	Fixed
s1	PFKL	1.0	default	✘
s10	Citrate	17.7476118652	default	✘
s11	sa4_degraded	0.0 <substance_units>/volume	default	✘
s12	ALDO	1.0	default	✘
s13	pPFKL	0.768939345	default	✘
s14	sa8_degraded	0.0 <substance_units>/volume	default	✘
s15	sa7_degraded	0.0 <substance_units>/volume	default	✘
s16	sa5_degraded	0.0 <substance_units>/volume	default	✘
s17	sa6_degraded	0.0 <substance_units>/volume	default	✘
s18	sa9_degraded	0.0 <substance_units>/volume	default	✘
s19	sa10_degraded	0.0 <substance_units>/volume	default	✘
s2	FBPase	1.0	default	✘
s20	sa13_degraded	0.0 <substance_units>/volume	default	✘
s21	sa3_degraded	0.0 <substance_units>/volume	default	✘
s22	F6P_proxy	<assignment rule> substance	default	✔
s3	F6P	14.0774258421	default	✘
s4	F1,6BP	104.07239819	default	✘
s5	PEP	108.094519859	default	✘
s6	Isocitrate	1.79487179487	default	✘
s7	2-oxoglutarate	25.1885369533	default	✘
s8	Malate	68.8788335846	default	✘
s9	F2,6BP	1.0	default	✘

Initial assignments 0

Initial assignments are expressions that are evaluated at time=0. It is not recommended to create initial assignments for all model entities. Restrict the use of initial assignments to cases where a value is expressed in terms of values or sizes of other model entities. Note that it is not permitted to have both an initial assignment and an assignment rule for a single model entity.

Definition

Reactions 11

Id	Name	Reaction Equation and Kinetic Law
re1	—	s22 > s4 s9 / (K_PFKL_f26bp + s9) * (K_PFKL_akg / (K_PFKL_akg + s7)) * (K_PFKL_pep / (K_PFKL_pep + s5)) * (K_PFKL_cit / (K_PFKL_cit + s10)) * (K_PFKL_icit / (K_PFKL_icit + s6)) * (K_PFKL_mal / (K_PFKL_mal + s8)) * (s13 / (K_PFKL_PHOS_S775 + s13)) * (Vf_PFKL * s22 / (K_PFKL_f6p + s22))
re10	—	s13 > s20 -k_pfkl_s775
re11	—	s3 > s21 -k_f6p
re2	—	s4 > s22 K_FBPase_f26bp / (K_FBPase_f26bp + s9) * (s10 / (K_FBPase_cit + s10)) * (Vf_FBPase * s4 / (K_FBPase_f16bp + s4))
re3	—	s4 > s11 k_ALDO * s4
re4	—	s8 > s14 -k_mal
re5	—	s7 > s15 -k_akg
re6	—	s5 > s16 -k_pep
re7	—	s6 > s17 -k_icit
re8	—	s9 > s18 -k_f26bp
re9	—	s10 > s19 -k_cit

Parameters 0 22

Global parameters

Id	Value
K_FBPase_cit	0.0211646 dimensionless
K_FBPase_f16bp	0.104089638 dimensionless
K_FBPase_f26bp	17.51744342 dimensionless
K_PFKL_PHOS_S775	6.283705757 dimensionless
K_PFKL_akg	24661.01154 dimensionless
K_PFKL_cit	41.30426029 dimensionless
K_PFKL_f26bp	1.282443082 dimensionless
K_PFKL_f6p	0.014114844 dimensionless
K_PFKL_icit	1784.508205 dimensionless
K_PFKL_mal	9.544729149 dimensionless
K_PFKL_pep	0.633518366 dimensionless
Vf_FBPase	9.932861302 dimensionless
Vf_PFKL	695063.7194 dimensionless
k_ALDO	0.008187906 dimensionless
k_akg	-3.544494721 substance
k_cit	-0.351935646 substance
k_f26bp	-0.083430336 substance
k_f6p	-0.930115636 dimensionless
k_icit	-0.038210156 substance
k_mal	1.005530417 substance
k_pep	43.99195576 substance
k_pfkl_s775	-0.011384308 substance

Local parameters

Id	Value	Reaction

Rules 0 0 1

Assignment rules

Definition
s22 = s3

Rate rules

Definition

Algebraic rules

Definition

Function definitions 0

Definition

Events 7

Trigger	Assignments
gt(time, 30)	k_cit = 0.306686777; k_f6p = 0.070387129; k_icit = 0.031674208; k_f26bp = -0.003055632; k_mal = 0.502765209; k_pep = -0.301659125; k_pfkl_s775 = 0.006730598; k_akg = 0.369532428
gt(time, 20)	k_pep = -3.619909502; k_pfkl_s775 = 0.006730598; k_mal = 1.206636501; k_akg = 0.226244344; k_cit = 0.377073906; k_f6p = -0.165912519; k_icit = 0.031674208; k_f26bp = -0.032601514
gt(time, 15)	k_akg = 1.085972851; k_cit = 0.452488688; k_f6p = -0.12066365; k_icit = -0.018099548; k_f26bp = 0.100115778; k_mal = 1.809954751; k_pfkl_s775 = 0.006730598; k_pep = -4.826546003
gt(time, 10)	k_akg = 0.36199095; k_cit = 1.085972851; k_f6p = -1.055806938; k_icit = -0.038210156; k_mal = 3.921568627; k_pep = 9.049773756; k_pfkl_s775 = 0.006730598; k_f26bp = -0.050207413
gt(time, 5)	k_akg = 0; k_cit = -0.211161388; k_f6p = 0.271493213; k_f26bp = 0.119075279; k_mal = -0.904977376; k_pep = -6.334841629; k_pfkl_s775 = 0.057596439; k_icit = -0.038210156
gt(time, 2)	k_akg = -1.357466063; k_f6p = 1.357466063; k_icit = -0.038210156; k_f26bp = 0.028924455; k_mal = -1.508295626; k_pep = 7.54147813; k_pfkl_s775 = -0.011384308; k_cit = 0.351935646
gt(time, 45)	k_akg = 0.369532428; k_cit = 0.306686777; k_icit = 0.031674208; k_f26bp = -0.003055632; k_mal = 0.502765209; k_pep = -0.301659125; k_pfkl_s775 = 0.00673059831429; k_f6p = 0.070387129