From 2011.igem.org

Revision as of 21:19, 17 September 2011

Module 2: Auxin Xpress

Auxin, or Indole 3-acetic acid (IAA), is a plant growth hormone which is produced by several soil bacteria. We have taken the genes encoding the IAA-producing pathway from Pseudomonas savastanoi and expressed them in Escherichia coli. Following chemotaxis towards the roots and uptake by the Phyto Route module, IAA expression will promote root growth with the aim of improving soil stability.

Modelling

1. Introduction

Adequate auxin expression from E.coli with engineering auxin synthesis pathway could efficiently promote root growth of plant, however auxin is poisonous to plant if its concentration is too high. Therefore it is important to predict the auxin expression level with certian promoter, and then adjust the promoter strength to ensure the auxin produced by genetic E.coli will optimally fasten root growth.

Auxin will effect root growth in terms of root length and number of braches. In order to study how different concentrations of auxin effect root growth pattern, modelling tools were combined with wet lab results to predict and visulise the root growth in length and branches of arabidopsis.

2. Modelling of auxin synthesis

2.1 Objective

Determine the auxin expression level of single E.coli with auxin promoter strength 4.536 RNA/min/μg substrate DNA, and then predict the number of bacteria needed to be placed in the seed coat to induce opmtimal root growth

2.2 Description

The genetic auxin pathway involves two genes, IaaM and IaaH, both of which are constitutively expressed. IaaM gene encodes tryptophan-2-monooxygenase (T-2-monase) that catalyzes the conversion of tryptophan (Trp) to indole-3-acetamide (IAM), which is then hydrolyzed to release indole-3-acetic acid (IAA) by the hydrolase iaaH[3]. At the same time the synthesized IAM and IAA will competitively inhibit the enzyme activity of tryptophan-2-monooxygenase, thereby inducing a negative feedback loop on the expression of IAA. The enzymatic reactions involved in the pathway are illustrated in Fig.1 below.

Fig.1[1]: Auxin symtheis pathway

In addition, based on research carried out by University of Colorado[4], the tryptophan is also negatively controlled inside of bacteria; therefore the tryptophan synthesis pathway should be integrated into above model. Furthermore, in order to reduce the numbers of parameters as most of the parameters in above graph are not available, the auxin pathway is then simplified to two Michaelis Menten equations, which are then combined with tryptophan pathway and constitutive gene expression for T-2-monase and iaaH. The whole tryptophan auxin pathway model was described in Equation 1[5], and the paramaters are defined in parameter section.

The assumptions associated with this model are listed below.

(1) We neglect the short transient needed by Trp-T-2-monase (substrate-enzyme(ES) complex), IAM-iaaH(ES complex), IAM- T-2-monase (inhibitor-enzyme(EI) complex) and IAA- T-2-monase (inhibitor-enzyme(EI) complex) and thus to consider these species reach their equilibrium almost instantaneously

(2) The degradation rate of IAA is extremely slow (7 days according to the experiment) compare to bacteria growth rate, therefore we use bacteria growth rate as IAA degradation rate in the model

(3) From early stage of our modeling of auxin pethway, it is believed that the rate determining species for IAA synthesis is IAM not enzyme iaaH since the production of IAM is inhibited by itself and IAA.

2.3 Result and discussion

Fig.2 below represents the standard output of our model. It shows how the concentration of each of the species varies with time. It showing the simulation of the enzymatic reaction for each of the species, with initial concentration of O_F = 1.54×10^-4 µM, M_F = 3.78×10^-4 µM , E = 0.378µM , Trp = 4.1µM and all others = 0 [4].

Fig.2(a):The evolution of IAM vs. time. Fig.2(b): The evolution of IAA.vs.time The graph Fig.2(b))shows IAA concentration is 72.35µM, therefore each bacteria produces 7.24×10^-14 µM at steady state with bacteria volume equals to 10^-15 dm³.From wet lab experiment we know that the optimal concentration of IAA to promote root growth 0.1nM, and the volume of arabidopsis seed with seed coating approximately equals to 4.2×10^-9 m³,[7]. Therefore the number of bacteria required to maximally fasten root growth of bacteria is 5, the value varies due to the variation of seed size of different of plant.

3.Auxin uptake and root growth

3.1 Objective

1.Create a graphic program to demonstrate the growing process of the Arabidopsis root system, based on the principles of Lindenmayer system and plant physiology.

2. Use data fitting tools to develope relations between auxin concentration and growth rate and number of brances

3.2 Description

3.2.1 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).

3.2.2 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.[10]

Fig.1 a conical approximation of the root system

3.2.3 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:[10]

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

3.2.4 Lindenmayer system and root growth modeling

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, due to the two main properties: recursive nature and self-similarity.[11]

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.

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.

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.

3.2.5 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 modified an MATLAB program developed by Daniel Leither[6] research group demonstrate 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).[12]

3.2.6 Data fitting

Apart from the growth rate parameters we took from the literature, we analyzed the raw data from wetlab to give more accurate and suitable prameters for our own project.

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.

3.3 Result and Discussion

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.[9]

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 = 0.96cm/day; without auxin = 0.46cm/day[10]). 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.

The data fitting 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. [8]

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.

4. Parameters

* Link to the Registry Page:

[http://partsregistry.org/Main_Page]

5. Matlab code

6. Reference

[1] Hutcheson, S.W., Kosuge, T., (1985), Regulation of 3-Indoleacetic Acid Production in Pseudomonas syringae pv savastanoi: PURIFICATION AND PROPERTIES OF TRYPTOPHAN 2-MONOOXYGENASE, ‘The Journal of Biological Chemistry’, 260(10), pp.6281-6287

[2] Saepen,S., Vanderleyden, J., Remans, R., (2007), Indole-3-acetic acid in microbial and microorganism-plant signaling, ‘FEMS Microbiology Reviews’, 31(4), pp.425-448

[3] Y.Cheng, C.Dai, Y.Zhao (2006), Auxin biosynthesis by the YUCCA flavin monooxygenases controls the formation of floral organs and vascular tissues in arabidopsis’ Genes&Dev.2006.20;1790-199. Doi: 10.1101/gad.1415106

[4]http://mathbio.colorado.edu/mediawiki/index.php/MBW:Modelling_the_Tryptophan_Operon

[5] Santillan, M., Mackey, M.C., (2001), Dynamic regulation of the tryptophan operon: A modeling study and comparison with experimental data, ‘PNAS’, 98(4), pp.1364-1369

[6] http://www.ccdb.ualberta.ca/CCDB/cgi-bin/STAT_NEW.cgi

[7] http://www.seedgenes.org/Tutorial.html

[8] Leitner, D., Klepsch, S., Bodner, G., Schnepf, A., (2009), A dynamic root system growth model based on L-Systems: Tropisms and coupling to nutrient uptake from soil, ‘Plant and Soil’, 332(1-2),pp.177-192

[9] Rayle, D.L., Evans, M.L., Hertel, L., (1970), Action of Auxin on Cell Elongation, ’ Proceedings of the National Academy of Sciecnce’, 65(1), pp.184-191

[10] Beemster, G.T.S., Baskin, T.I., (1998) Analysis of Cell Division and Elongation Underlying the Developmental Acceleration of Root Growth in Arabidopsis thaliana, ‘Plant Physiology’, 116(4), pp.1515-1526

[11] HU, H., CAO, X., LIN ,B., (2003), Three Dimensional Lindenmayer System, ‘The Journal of Engineering Graphics’, 2003-03

[12] Leitner, D., Schnepf, A., Klepsch, S., Roose, T., (2009), Comparison of nutrient uptake between 3-dimensional simulation and an averaged root system model, ‘Plant Biosystems’, 144(2), pp.443-447

[13]Brenda: The Comprehensive Enzyme Information System (http://www.brenda-enzymes.org/)

[14] http://www.jbs.org/content/246/22/6956.full.pdf