Team:Evry/pop scale

Introduction

The Enterobactin production model showed us that our bacteria take too much time to produce enterobactins, which disables the flush strategy.
In response, we changed the strategy, by delivering a gel that would block the bacteria in the jejunum.
This model is thus very similar to the flush treatment model, except for the longer time scale, and the computational method.

Goals

Assumptions

• No regulation of the patient's iron absorption
• Constant iron flow in the intestine
• Homogeneous fluid
• The bacterial natural absorption is insignificant compared to the chelation
• The patient ingests 20mg of iron per day (Guideline Daily Amounts)

Materials and methods

This model is based on a cellular automaton algorithm : both the bacteria and the enterobactins are cellular automaton.

The Figure 1 represents the influence between the system's variables.

Figure 2 : Graph of bacteria automaton.

Pgrowth Division Probability Pdeath Death probability age variable rate of bacteria ageing

The Figure 2 represents the bacteriial automaton. It has two functions : the division and the ennterobactin production.
The bacteria produces enterobactins. This production is ruled by an activator. This activator is a step functionn (from 0 to 1) with a K_a threshold, fixed thanks to the sensor model:

The enterobactins chelate iron. For this automoton, we have one rule : One Enterobactin atom can chelate one Iron molecule
Still, the automaton is ruled by an encounter probability: .

Results

Parameter values

Name	Value	Unit	Description	Reference
mu	0.2	s-1	Iron absorption rate by the body
IronQuantity0	4.5.10-9	mol-1	Iron pulse value
Sact	10**-9	m	Duodenum length	[3]
α	0.3	s-1	Duodenum absorption rate	tuned
σ	0.72	s-1	Regulation coefficient	tuned

Models and scripts

This model was made using the Python language. You can download the python script here.

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