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twycross

twycross

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v_1

v_1

S = {40000.0}a0

v_10

{40000.0}a4 = {20.0}c5

v_11

{20.0}c5 = {40000.0}a5

v_12

{40000.0}a5 = {20.0}c6

v_13

{20.0}c6 = {40000.0}a6

v_14

{40000.0}a6 = {20.0}c7

v_15

{20.0}c7 = {40000.0}a7

v_16

{40000.0}a7 = {20.0}c8

v_17

{20.0}c8 = {40000.0}a8

v_18

{40000.0}a8 = {20.0}c9

v_19

v_19

{20.0}c9 = {40000.0}a9

v_2

{40000.0}a0 = {20.0}c1

v_20

{40000.0}a9 = {20.0}c10

v_21

{20.0}c10 = {40000.0}a10

v_22

{40000.0}a10 = {20.0}c11

v_23

{20.0}c11 = {40000.0}a11

v_24

{40000.0}a11 = {20.0}c12

v_25

{20.0}c12 = {40000.0}a12

v_26

{40000.0}a12 = {20.0}c13

v_27

{20.0}c13 = {40000.0}a13

v_28

{40000.0}a13 = {20.0}c14

v_29

{20.0}c14 = {40000.0}a14

v_3

{20.0}c1 = {40000.0}a1

v_30

{40000.0}a14 = {20.0}c15

v_31

{20.0}c15 = {40000.0}a15

v_32

{40000.0}a15 = {20.0}c16

v_33

{20.0}c16 = {40000.0}a16

v_34

{40000.0}a16 = {20.0}c17

v_35

{20.0}c17 = {40000.0}a17

v_36

{40000.0}a17 = {20.0}c18

v_37

{20.0}c18 = {40000.0}a18

v_38

{40000.0}a18 = {20.0}c19

v_39

{20.0}c19 = {40000.0}a19

v_4

{40000.0}a1 = {20.0}c2

v_40

{40000.0}a19 = {20.0}c20

v_41

{20.0}c20 = {40000.0}a20

v_42

{40000.0}a20 = Fvar

v_5

{20.0}c2 = {40000.0}a2

v_6

{40000.0}a2 = {20.0}c3

v_7

{20.0}c3 = {40000.0}a3

v_8

{40000.0}a3 = {20.0}c4

v_9

{20.0}c4 = {40000.0}a4

Parameters

Global parameters

Fixed species

Initial values

Functions and Rules

Assignment rules

Aprotc = 1/(1 + 10^(pHc - pK))

Aaniona = 1 - Aprota

Bc = Aanionc*(-subval/((2.71828^(-subval)) - 1))

Aprota = 1/(1 + 10^(pHa - pK))

Ba = Aaniona*(subval/((2.71828^(subval)) - 1))

subval = (-F*V/(R*T))

Aanionc = 1 - Aprotc

Function definitions

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:

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Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study.

  • Jamie Twycross
  • Leah R Band
  • Malcolm J Bennett
  • John R King
  • Natalio Krasnogor
BMC Syst Biol 2010; 4 : 34
Abstract
BACKGROUND: Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks.
RESULTS: In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system.
CONCLUSIONS: Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models.

No additional notes are available for this model.