JWS Online

mcauley1

mcauley1

 Detail
 Download

Reactions

reaction_1

Ingestion

species_1 > species_2

reaction_10

Billary Cholesterol Release

species_7 > species_2

reaction_11

Hepatic Cholesterol Synthesis

species_12 > species_7

reaction_12

Hepatic Cholesterol Storage

species_7 > species_13

reaction_13

Release of Stored Cholesterol

species_13 > species_7

reaction_14

Hepatic Nascent HDL Synthesis

species_16 > species_10

reaction_15

VLDL Cholesterol Formation

species_7 > species_17

reaction_16

Hepatic LDLR Synthesis

species_19 > species_18

reaction_17

Hepatic LDL Receptor Degradation

species_18 > species_20

reaction_18

VLDL Cholesterol ReUptake

species_17 > species_7

reaction_19

IDL Cholesterol Formation

species_17 > species_21

reaction_2

Intestinal Cholesterol Synthesis

species_3 > species_2

reaction_20

IDL Cholesterol ReUptake

species_21 > species_7

reaction_21

LDL Cholesterol Formation

species_21 > species_23

reaction_22

Receptor Dependent Hepatic Uptake

species_23 > species_7

reaction_23

Receptor Independent Hepatic Uptake

species_23 > species_7

reaction_24

Receptor Dependent Peripheral Uptake

species_23 > species_11

reaction_25

Receptor Independent Peripheral Uptake

species_23 > species_11

reaction_26

Peripheral LDLR Synthesis

species_26 > species_25

reaction_27

Peripheral LDL Receptor Degradation

species_25 > species_27

reaction_28

Peripheral Cholesterol Storage

species_11 > species_28

reaction_29

Release of Stored Peripheral Cholesterol

species_28 > species_11

reaction_3

Bile Salt Release

species_4 > species_5

reaction_30

Peripheral Steroid Production

species_11 > species_29

reaction_31

HDL Cholesterol Formation

species_11 + species_10 > species_30

reaction_32

Peripheral Cholesterol Synthesis

species_32 > species_11

reaction_33

CETP Mediated Transfer To VLDL

species_30 > species_17

reaction_34

CETP Mediated TransferTo LDL

species_30 > species_23

reaction_35

Reverse Cholesterol Transport

species_30 > species_7

reaction_4

Bile Salt Return

species_5 > species_4

reaction_5

Bile Salt Excretion

species_5 > species_6

reaction_6

Bile Salt Synthesis

species_7 > species_4

reaction_7

Cholesterol Absorption

species_2 > species_7

reaction_8

Cholesterol Excretion

species_2 > species_8

reaction_9

Intestinal Nascent HDL Synthesis

species_9 > species_10

Parameters

Global parameters
reaction_1
reaction_10
reaction_11
reaction_12
reaction_13
reaction_14
reaction_15
reaction_16
reaction_17
reaction_18
reaction_19
reaction_2
reaction_20
reaction_21
reaction_22
reaction_23
reaction_24
reaction_25
reaction_26
reaction_27
reaction_28
reaction_29
reaction_3
reaction_30
reaction_31
reaction_32
reaction_33
reaction_34
reaction_35
reaction_4
reaction_5
reaction_6
reaction_7
reaction_8
reaction_9

Fixed species

Initial values

Functions and Rules

Events

Constraints

Note that constraints are not enforced in simulations. It remains the responsibility of the user to verify that simulation results satisfy these constraints.


Species:

Reactions:

Middle-click: pin/unpin nodes
Shift-click: pool/unpool species
Right-click: context menu
Apply alternate model layout to overlapping elements in current model:

log scales

y-axis min/max

x-axis min/max

A whole-body mathematical model of cholesterol metabolism and its age-associated dysregulation.

  • Mark T Mc Auley
  • Darren J Wilkinson
  • Janette J L Jones
  • Thomas B L Kirkwood
BMC Syst Biol 2012; 6 : 130
Abstract
BACKGROUND: Global demographic changes have stimulated marked interest in the process of aging. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion.
RESULTS: The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34 mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116 mg/dL.
CONCLUSIONS: Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism.
The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000434) Biomodels notes: Figure 1b of the reference publication has been reproduced here. The figure in the paper has been generated using MathSBML. The author has generated the SBML file using Copasi, and finds that with the same intial conditions and parameter sets, LDLC enters a slightly higher steady statem, when running the simulation using Copasi. This is reflected in this curation figure, generated using SBML odeSolver. The model was simulated using SBML odeSolver and the plot was generated using Gnuplot. JWS Online curation: This model was curated by reproducing the figures as described in the BioModels Notes. A global parameter 'multiplier' was added, and added to reaction 1. To reproduce Fig 1B the values for 'multiplier' needs to be set to 0.6667, 1, 1.3333, 1.6667, 2 and 2.3333 for DC values of 200, 300, 400, 500, 600 and 700 mg/day respectively.