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Team:Toulouse/Modelling
From 2014.igem.org
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| - | Modelling is a tool used to simplify and study systems. We can try to predict behaviour with bibliographic information or information obtained from experiment. | + | Modelling is a tool used to simplify and study systems. We can try to predict behaviour with bibliographic information or information obtained from experiment.</br> |
Our project focuses on the development of our bacteria in tree. The way of bacterial growth in tree seems to be unknown, so we must infer bacillus subtilis behaviour.</p> | Our project focuses on the development of our bacteria in tree. The way of bacterial growth in tree seems to be unknown, so we must infer bacillus subtilis behaviour.</p> | ||
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<p class="texte"> | <p class="texte"> | ||
| - | We make the following hypothesis: | + | We make the following hypothesis:</p> |
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For this we used to cardinal temperature model [4]: </p> | For this we used to cardinal temperature model [4]: </p> | ||
| - | <img style="" src="" alt="cardinal temperature model"> | + | <img style="" src="https://static.igem.org/mediawiki/2014/8/85/Formules_Rosso.png" alt="cardinal temperature model"> |
<p class="texte"> | <p class="texte"> | ||
| - | T: Temperature. | + | T: Temperature.</br> |
| - | µ_opt: optimal growth rate. | + | µ_opt: optimal growth rate.</br> |
| - | µ: growth rate at T. | + | µ: growth rate at T.</br> |
| - | T_max = max temperature supported by bacteria. | + | T_max = max temperature supported by bacteria.</br> |
| - | T_min = min temperature supported by bacteria. | + | T_min = min temperature supported by bacteria.</br> |
| - | T_opt = optimum temperature for the growth. | + | T_opt = optimum temperature for the growth.</br> |
| - | Necessary parameter for this function is minimun temperature T_min and maximum temperature T_max, optimal temperature for the growth T_opt and the optimal growth rate µ_opt. | + | Necessary parameter for this function is minimun temperature T_min and maximum temperature T_max, optimal temperature for the growth T_opt and the optimal growth rate µ_opt.</br> |
| - | T_min: 10°C | + | T_min: 10°C</br> |
| - | T_max: 52°C | + | T_max: 52°C</br> |
| - | T_opt: 37°C | + | T_opt: 37°C</br> |
| - | µ_opt: 8.5968 cfu/d | + | µ_opt: 8.5968 cfu/d</br> |
| - | The optimal growth (µopt) rate is obtained experimentally with a similar birch sap environment. | + | The optimal growth (µopt) rate is obtained experimentally with a similar birch sap environment.</br> |
The growth rate is negative below 10°C ( growth test performed at 10 ° C and 4 ° C under similar conditions for the measurement of μ_opt), survival rate after 24h was 0.3 % at 10°C and null at 4°C. | The growth rate is negative below 10°C ( growth test performed at 10 ° C and 4 ° C under similar conditions for the measurement of μ_opt), survival rate after 24h was 0.3 % at 10°C and null at 4°C. | ||
Conditions apply:</p> | Conditions apply:</p> | ||
| - | If | T<= 4°C -> µ = -1 | + | If | T<= 4°C -> µ = -1</br> |
| - | | 4°C<T<= 10°C -> µ = -0.97 | + | | 4°C<T<= 10°C -> µ = -0.97</br> |
| - | | T > 10°C -> µ = f(T) with f(T) egal to cardinal temperature model. | + | | T > 10°C -> µ = f(T) with f(T) egal to cardinal temperature model.</br> |
<img style="" src="" alt="Figure1"> | <img style="" src="" alt="Figure1"> | ||
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<p class="texte"> A logistic model developed by Hiroshi Fujikawa [5] is used to study bacterial growth.</p> | <p class="texte"> A logistic model developed by Hiroshi Fujikawa [5] is used to study bacterial growth.</p> | ||
| - | General logistics formulas | + | <p class="texte"><center> General logistics formulas</center></p> |
<img style="" src="" alt="General logistics formulas"> | <img style="" src="" alt="General logistics formulas"> | ||
<p class="texte"> | <p class="texte"> | ||
| - | In our cases, µ depending of the temperature. N corresponds to the bacterial population, Nmin and Nmax are two asymptotes.Parameter "m" is a curvature parameter; With a larger m, the curvature of the deceleration phase with the model is smaller. Parameter n is a parameter related to the period lag. With a larger n, the period of lag is shorter. Nmin is slightly lower than N0, when N is small, close to Nmin, as the initial state (N is equal to N0), Nmin / N is almost equal to 1 so the term ( 1 - ( Nmin / N) ) is less than 1, growth is very slow. If N decrease until reach Nmin the term (1-(Nmin/N)) is equal to 0 thus there are can not be any growth. Similarly when N is equal to Nmax the term (1- (N / Nmax ) ) is equal to 0 and the growth is blocked. | + | In our cases, µ depending of the temperature. N corresponds to the bacterial population, Nmin and Nmax are two asymptotes.Parameter "m" is a curvature parameter; With a larger m, the curvature of the deceleration phase with the model is smaller. Parameter n is a parameter related to the period lag. With a larger n, the period of lag is shorter. Nmin is slightly lower than N0, when N is small, close to Nmin, as the initial state (N is equal to N0), Nmin / N is almost equal to 1 so the term ( 1 - ( Nmin / N) ) is less than 1, growth is very slow. If N decrease until reach Nmin the term (1-(Nmin/N)) is equal to 0 thus there are can not be any growth. Similarly when N is equal to Nmax the term (1- (N / Nmax ) ) is equal to 0 and the growth is blocked.</br> |
To overcome this we labor under two conditions , positive growth and negative growth, so two equations.This led to the writing of this model:</p> | To overcome this we labor under two conditions , positive growth and negative growth, so two equations.This led to the writing of this model:</p> | ||
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<p class="texte"> | <p class="texte"> | ||
| - | with n = 1 and m = 0.5 | + | with n = 1 and m = 0.5</br></br> |
Term (1-(Nmin/N)) is not taken into account when there is growth and (1-(N/Nmax)) is not taken account when there is bacterial decay.</br> | Term (1-(Nmin/N)) is not taken into account when there is growth and (1-(N/Nmax)) is not taken account when there is bacterial decay.</br> | ||
Revision as of 07:45, 11 October 2014
Modelling
To develop a predictive model.
Project > Modelling
Modelling is a tool used to simplify and study systems. We can try to predict behaviour with bibliographic information or information obtained from experiment. Our project focuses on the development of our bacteria in tree. The way of bacterial growth in tree seems to be unknown, so we must infer bacillus subtilis behaviour.
Bacterial Growth
Objective
Bacillus subtilis is a trees endophyte strain, a study [1] showed that Bacillus subtilis could develop and colonize fully a tree and reach a concentration of 10⁵ cells per g of fresh plant. We need to know in which conditions the growth are optimum in a tree and if the weather can stop its development in winter. We work on the Bacillus subtilis growth in function of the temperature during year. Modeling bacterial growth in a tree section generate some difficulties, we need to know distance between two tree extremities (treetops and root) or the speed sap flow which can vary with temperature during the day and seasons, cause of the type of sap (phloem, xylem). Furthermore tree is not a homogeneous system, its roots, trunk and branch do not contain same amount of sap and wood. The average speed of the plane tree sap is 2.4m/h [2] that means that in a day the sap will flow from one end to the other of a tree 30m. Tree is reduced to a bioreactor.
We make the following hypothesis:
- According to the publication of Xianling Ji[1], after 6 months of Bacillus subtilis growth in a tree bacteria cells reach a concentration of 10⁵ cells per gram of fresh plant. Assume that 10⁵ cells / g is the maximum concentration.
- The composition of the phloem is stable. There is no effect of depletion of the medium.
- Only temperature impact on bacterial growth.
- It is believed that there is no leakage of cells.
Method
An assessment of the Bacillus subtilis growth in a similar sap, the birch sap [3] was performed in laboratory conditions with optimum growth medium for Bacillus subtilis. Thus, a growth rate μ opt. From this value we can extrapolate a growth curve as a function of temperature. For this we used to cardinal temperature model [4]:
T: Temperature. µ_opt: optimal growth rate. µ: growth rate at T. T_max = max temperature supported by bacteria. T_min = min temperature supported by bacteria. T_opt = optimum temperature for the growth. Necessary parameter for this function is minimun temperature T_min and maximum temperature T_max, optimal temperature for the growth T_opt and the optimal growth rate µ_opt. T_min: 10°C T_max: 52°C T_opt: 37°C µ_opt: 8.5968 cfu/d The optimal growth (µopt) rate is obtained experimentally with a similar birch sap environment. The growth rate is negative below 10°C ( growth test performed at 10 ° C and 4 ° C under similar conditions for the measurement of μ_opt), survival rate after 24h was 0.3 % at 10°C and null at 4°C. Conditions apply:
If | T<= 4°C -> µ = -1 | 4°CA logistic model developed by Hiroshi Fujikawa [5] is used to study bacterial growth.
In our cases, µ depending of the temperature. N corresponds to the bacterial population, Nmin and Nmax are two asymptotes.Parameter "m" is a curvature parameter; With a larger m, the curvature of the deceleration phase with the model is smaller. Parameter n is a parameter related to the period lag. With a larger n, the period of lag is shorter. Nmin is slightly lower than N0, when N is small, close to Nmin, as the initial state (N is equal to N0), Nmin / N is almost equal to 1 so the term ( 1 - ( Nmin / N) ) is less than 1, growth is very slow. If N decrease until reach Nmin the term (1-(Nmin/N)) is equal to 0 thus there are can not be any growth. Similarly when N is equal to Nmax the term (1- (N / Nmax ) ) is equal to 0 and the growth is blocked. To overcome this we labor under two conditions , positive growth and negative growth, so two equations.This led to the writing of this model:
with n = 1 and m = 0.5 Term (1-(Nmin/N)) is not taken into account when there is growth and (1-(N/Nmax)) is not taken account when there is bacterial decay. Meteorological records of the Toulouse region of 2011-2013 are used to do averages daily temperatures. Thus we can determine B.subtilis growth during a year on Toulouse. This values are obtained for each day by the average on the hightest and the lowest temperature. The density of green wood plane is about 650kg / m³. The average diameter of the trunks of the trees in question is about 0.80m and 15m high. This represents a volume of 30 m³ . The weight of the trunk is therefore 19604kg . Added to this weight the weight of branches, twigs, leaves about 25 % and about 15% of roots (source). The average weight of a tree plane is 27446kg where in inoculated 10mL of bacterial culture at 10⁹cfu/mL, ie 10^10 bacterial cells. This represents 3.64x10² cfu/g of fresh plant (N0).
In our model growth start only from 10°C this product between March and April. This period seems to be suitable to put the strain in the tree. From December the temperature down to be below 4°C, threshold below which bacterias dies.
Discussion
In practice, the temperature variations are certainly lower in tree than outside, especially if roots extend very deep. Composition of the tree sap must also intervene in the growth rate, nutrient content of sap is also temperature dependent. The effects of the decrease of the temperature in winter also involve a fall of the sap and this must also be involved in the disappearance of our strain in the tree. The period of Bacillus subtilis growth is certainly affected by the change in temperature, the rise of sap, its composition variations can consequently slow the growth rate. The modeling work is done with the programming language 'R' script attached.
References
- [1] Xianling Ji, Guobing Lu, Yingping Gai, Chengchao Zheng & Zhimei Mu (2008) Biological control against bacterial wilt and colonization of mulberry by an endophytic Bacillus subtilis strain. FEMS Microbiol Ecol 65: 565–573
- [2] A. Garnier(1977) Transfert de sève brute dans le tronc des arbres aspects méthodologiques et physiologiques. Ann. Sci. Foresi. 34 (1): 17-45 .
- [3] Heikki Kallio , Tuija Teerinen , Seija Ahtonen , Meri Suihko , Reino R. Linko (1989) Composition and properties of birch syrup (Betula pubescens). J. Agric. Food Chem 37 (1): 51–54
- [4] L. Rosso, J. R. Lobry & J. P. Flandrois (1992) AN Unexpected Correlation between Cardinal Temperatures of Microbial Growth Highlighted by a New Model. J. theor. Biol. 162 : 447-463
- [5] Hiroshi Fujikawa (2010), Development of a New Logistic Model for Microbial Growth in Foods. Biocontrol of Science Vol 15: 75-80
Annexe
script tableau des temperatures.
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