# IGEM:IMPERIAL/2007/Dry Lab/Modelling/ID

(Difference between revisions)
 Revision as of 19:35, 16 October 2007 (view source) (→Model 1: Steady-state is attained; limitless energy supply ''(link here to derivation)'')← Previous diff Revision as of 19:36, 16 October 2007 (view source) (→Model 1: Steady-state is attained; limitless energy supply ''(link here to derivation)'')Next diff → Line 32: Line 32: $\frac{d[A]}{dt} = -k_3[A] + k2[LuxR][AHL]- k_4[A][pLux] + k_5[AP]$ $\frac{d[A]}{dt} = -k_3[A] + k2[LuxR][AHL]- k_4[A][pLux] + k_5[AP]$ - $\frac{d[AP]}{dt} = k_4[A][pLux] - k_5[AP] - k_6[AP]$ + $\frac{d[P]}{dt} = -k_4[A][P] + k_5[P]$ + + $\frac{d[AP]}{dt} = k_4[A][pLux] - k_5[AP]$ $\frac{d[GFP]}{dt} = k_6[AP] - \delta_{GFP}[GFP]$ $\frac{d[GFP]}{dt} = k_6[AP] - \delta_{GFP}[GFP]$

# Model Development for Infector Detector

## Formulation of the problem

• Questions to be answered with the approach
• Verbal statement of background
• What does the problem entail?
• Hypotheses employed

## Selection of model structure

• Present general type of model
1. is the level of description macro- or microscopic
2. choice of a deterministic or stochastic (!) approach
3. use of discrete or continuous variables
4. choice of steady-state, temporal, or spatio-temporal description
• determinants for system behaviour? - external influences, internal structure...
• assign system variables

## Our models

• Introduction

We can condition the system in various manners, but for the purposes of our project, Infector Detector, we will seek a formulation which is valid for both constructs considered.

Our initial approach assumed that energy would in unlimited supply, and that our system would eventually reach steady-state (Model 1). Experimentation suggested otherwise; our system needed to be amended. This lead to the development of model 2, an energy-dependent network, where the dependence on energy assumed Hill-like dynamics:

### Model 1: Steady-state is attained; limitless energy supply (link here to derivation)

$\frac{d[LuxR]}{dt} = k_1 + k_3[A] - k2[LuxR][AHL]- \delta_{LuxR}[LuxR]$
$\frac{d[AHL]}{dt} = k_3[A] - k2[LuxR][AHL]- \delta_{AHL}[AHL]$

$\frac{d[A]}{dt} = -k_3[A] + k2[LuxR][AHL]- k_4[A][pLux] + k_5[AP]$

$\frac{d[P]}{dt} = -k_4[A][P] + k_5[P]$

$\frac{d[AP]}{dt} = k_4[A][pLux] - k_5[AP]$

$\frac{d[GFP]}{dt} = k_6[AP] - \delta_{GFP}[GFP]$

### Model 2: Equations developed through steady-state analysis; however due to limited energy supply, we operate in the transient regime

$\frac{d[LuxR]}{dt} = k_1\bigg(\frac{[E]^n}{K_E^n + [E]^n}\bigg) + k_3[A] - k_2[LuxR][AHL]- \delta_{LuxR}[LuxR]$
$\frac{d[AHL]}{dt} = k_3[A] - k_2[LuxR][AHL]- \delta_{AHL}[AHL]$

$\frac{d[A]}{dt} = -k_3[A] + k_2[LuxR][AHL]- k_4[A][P] + k_5[AP]$

$\frac{d[P]}{dt} = -k_4[A][P] + k_5[P]$

$\frac{d[AP]}{dt} = k_4[A][P] - k_5[AP]$

$\frac{d[GFP]}{dt} = k_6[AP]\bigg(\frac{[E]^n}{K_E^n + [E]^n}\bigg) - \delta_{GFP}[GFP]$

$\frac{d[E]}{dt} = -\alpha_{1}k_1\bigg(\frac{[E]^n}{K_E^n + [E]^n}\bigg) - \alpha_{2}k_6[AP]\bigg(\frac{[E]^n}{K_E^n + [E]^n}\bigg)$

where:

[A] represents the concentration of AHL-LuxR complex
[P] represents the concentration of pLux promoters
[AP] represents the concentration of A-Promoter complex
k1, k2, k3, k4, k5, k6 are the rate constants associated with the relevant forward and backward reactions
$\alpha_i \$ represents the energy consumption due to gene transcription. It is a function of gene length.
n is the positive co-operativity coefficient (Hill-coefficient)
$K_E \$ the half-saturation coefficient

The system of equations for the two constructs varies strictly with respect to the value of the parameter k1. Construct 1 possesses a non-zero k1 rate constant, whereas for construct 2, a zero value is assumed.