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Team:Imperial College London/Project/Auxin/Results/Modeling
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<p> <b>Fig.2 the difference of the root systems with different values of N and σ</b> | <p> <b>Fig.2 the difference of the root systems with different values of N and σ</b> | ||
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<h3>Lindenmayer system and root growth modeling</h3> | <h3>Lindenmayer system and root growth modeling</h3> | ||
<p> The properties of L-system provides the basic graphic principles to "draw" a root system. | <p> The properties of L-system provides the basic graphic principles to "draw" a root system. | ||
Revision as of 17:00, 12 September 2011
Modeling
Auxin Synthesis Pathway
The production of auxin by bacteria(E.Coli and/or B.Subtilis) is one main module in our project. In order to choose the appropriate RNA promoter with the optimal strength, the auxin production amount is modelled base on the pathway via the intermediate IAM from the precusor tryptophan.
The result of modelling answers the question: " How much auxin can be produced by the genetically modified bacteria with a typical RNA promoter strength? "
The pathway we chose has two steps:[1]
tryptophan-IAM(IaaM gene - tryptophan-2-monooxygenase)-IAM-IAA (IaaH gene-IAM hydrolase)
This pathway has a feedback inhibition mechanism, the production of IAM and IAA inhibits the function of tryptophan-2-monooxygenase, therefore stops the reaction chain. The inhibition of the enzyme activity is competitive, as shown by the equillibium equations below.
E + S ↔ ES → E + P
E + I ↔ EI
Also, the reaction kinetics fits the Michaelis-Menten kinetics model perfectly.
To write up the modelling code,a set of ODEs describing the concentration changes of each chemicals can be used to define the reaction process:
The rate constants of the reactions inside the pathway are required (parameters required: k1,k-1,k3,k-3). All the parameters of the two enzymes involved in this pathway, tryptophan-2-monooxygenase and IAM hydrolase, can be found at the enzyme database Brenda. [2]
Auxin Uptake
Initial a root system
To visualise our modelling result, a root system is demonstrated to show the root growth phenomena(primary root length, branching, root density, etc) in different environmental conditions(external and internal auxin concentration).
Root order:-
Root order describes the branching “generation” of a root system, a root without branching is defined as a zero-order root
A root system starts with a single root tip of a zero-order root. Then the root grows away from the plant stem in a conical way.[3]
Fig.1 a conical approximation of the root system
initial number of root = n0
initial cone base radius = r0
axial insertion angle = α
radial insertion angle = β
To ensure a even distribution of the roots, α and β must be set in the following way:
Tropisms:-
Root growth depends on the environmental factors, such as gravitation, soil heterogeneities, etc.
Therefore, two more variables are defined to describe the plant adaptation:
α:-
how strong the roots direction changes per 1cm growth ?
larger value indicates a more deflected root and a more twisted root system
N:-
the number of trials for the roots to find the optimal angles α and β for the rotation
for the downward movement
N can be any real number, if N = 1.5, if means that N can be either 1 or 2.
Fig.2 the difference of the root systems with different values of N and σ
Lindenmayer system and root growth modeling
The properties of L-system provides the basic graphic principles to "draw" a root system.
The method to model root growth is to create a root system using Matlab.
An L-system is a parallel rewriting system, namely a variant of a formal grammar, most famously used to model the growth processes of plant development, but also able to model the morphology of a variety of organisms.[4]
-recursive nature
-self-similarity
Plant models and natural-looking organic forms are easy to define, as by increasing the recursion level the form slowly 'grows' and becomes more complex.
L-systems are now commonly known as parametric L systems, defined as a tuple.
G = (V, ω, P)
V = a set of symbols containing elements that can be replaced (variables)
ω (start, axiom or initiator) = a string of symbols defining the initial state of the system
P = a set of production rules defining the way variables can be replaced with combinations of constants and other variables.
A production consists of two strings, the predecessor and the successor.
For any symbol A in V which does not appear on the left hand side of a production in P,the identity production A → A is assumed. These symbols are called constants or terminals.
An L-system is context-free if each production rule refers only to an individual symbol and not to its neighbors. Context-free L-systems are thus specified by either a prefix grammar, or a regular grammar.
If there is exactly one production for each symbol, then the L-system is said to be deterministic (a deterministic context-free L-system is popularly called a D0L-system).
If there are several, and each is chosen with a certain probability during each iteration, then it is a stochastic L-system.
Using L-systems for generating graphical images requires that the symbols in the model refer to elements of a drawing on the computer screen. It interprets each constant in an L-system model as a turtle command.
Root Growth
The modelling of auxin uptake will give prediction of the root system development in the following ways:-
"What is the primary root growth rate?"
"What does the root system look like after a certain period of time?"
"How does arabidopsis respond to different auxin concentration?"
... ...
We wrote a program using Matlab to draw the 3D root system based on the principles of Lindenmayer system(turtle commands) and the root growth modelling toolbox developed by Daniel Leitner et al from BOKU(Universität für Bodenkultur Wien, University of Natural Resources and Life Sciences, Vienna).
The values from literature gives the relationship between external auxin concentration and elongation of the roots is 5*10-5 mol/L → 200 µm elongation in 30 mins. The modelling parameter of growth speed is therefore 9.6*10-3 m/day
Fig.3 demonstration of a zeroth order root system
As you can see, this demonstration has a more generalised root shape. Arabidopsis, which does not grow in this way,is used in our project. By observing the real roots grow from the plant, the demonstration is modified to give a more reliable and accurate prediction of the root growth. Arabidopsis has a primary root with zeroth order and it is thicker than the branches. Arabidopsis normally grows to the depth of 20~30cm inside the soil and branches once only. The 3D picture shown below predicts the root growth with different elongation rate(with auxin = 0.46cm/day; without auxin = 0.96cm/day). They can be compared with the photo of real root system.
Fig.4 visalisation of the arabidopsis root system
The root has a growth rate of 0.96cm/day with the external auxin concentration 5x10-5mol/L, however, this data is selected from literature. To get an accurate growth rate which is particularly fitting our project, we decided to do data fitting analysis to the arabidopsis we plant.
data fitting:-
When the arabidopsis samples are planted, we record the root length and number of branches every three day from day 0 to day 9. Then, root length, daily root growth rate and number of branches are plotted against time and auxin concentration. These plots are analysed to give an approximation of the relationship between auxin concentration and root growth. The following graph gives an example of root length against time.
Fig.5 root growth speed decays against time
Fig.6 primary root length(mm) VS time(day) and external auxin concentration(mol/L)
Fig.7 primary root growth rate(mm/day) VS root growth time(day)
Fig.8 number of lateral branch VS external auxin (log)concentration
From Fig.6 and Fig.8, the following conclusion can be make:-
the optimal concentration for primary root growth = 1pM, at this concentration,
the arabidopsis root reached the maximal depth into soil
the optimal concentration for lateral root branching = 1uM-10nM,
at this concentration, the arabidopsis root gained the most lateral branches
We used the data fitting toolbox of Matlab to obtain Fig.7 primary root growth rate(mm/day) VS root growth time(day), the relationship between the growth rate and the auxin concentration can be approximated by Gaussian equation. The abnormality of the 0.1nM curve is due to the two contaminated samples which stopped growing at 7mm after Day 5. Fig.7 is consistent with the prediction of the decay of the root growth speed given by Fig.7.
