Team:Toulouse/Modelling
From 2014.igem.org
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<p class="texte"> | <p class="texte"> | ||
- | <i>Bacillus subtilis</i> is a tree endophyte strain. A study showed that <i> | + | <i>Bacillus subtilis</i> is a tree endophyte strain. A study showed that <i>B. subtilis</i> could develop and fully colonize a tree, reaching a concentration of 10<sup>5</sup> cells per gram of fresh plant. We need to know in which conditions the growth of <i>B. subtilis</i> is optimum in a tree and if the weather can stop its development during winter. Therefore we decided to work on the growthof <i>B. subtilis'</i> in function of the temperature during the year. |
<br>Modeling bacterial growth in a tree section generates some difficulties. We need to know the distance between two tree extremities (treetops and root) or the speed sap flow. However the flow of speed sap can vary with temperature during the day. The composition of sap also varies due to seasons and type of container (phloem, xylem). Furthermore a tree is not an homogeneous system: its roots, trunk and branches do not contain the same amount of sap and wood. <br>The average speed of the plane tree sap is 2.4 m/h, which means that in a day the sap of a 30 m tree will flow from one extremity to the other. We thus reduced the tree to a bioreactor. | <br>Modeling bacterial growth in a tree section generates some difficulties. We need to know the distance between two tree extremities (treetops and root) or the speed sap flow. However the flow of speed sap can vary with temperature during the day. The composition of sap also varies due to seasons and type of container (phloem, xylem). Furthermore a tree is not an homogeneous system: its roots, trunk and branches do not contain the same amount of sap and wood. <br>The average speed of the plane tree sap is 2.4 m/h, which means that in a day the sap of a 30 m tree will flow from one extremity to the other. We thus reduced the tree to a bioreactor. | ||
</p> | </p> | ||
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<ol class="list1"> | <ol class="list1"> | ||
<li> | <li> | ||
- | According to the publication of <b>Xianling Ji</b> (See References), after six months of <i> | + | According to the publication of <b>Xianling Ji</b> (See References), after six months of <i>B. subtilis</i> growth in a tree, bacteria cells reach a concentration of 10<sup>5</sup> cells per gram of fresh plant. We assume that 10<sup>5</sup> cells/g is the maximum concentration. |
</li> | </li> | ||
<li> | <li> | ||
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<p class="texte"> | <p class="texte"> | ||
- | An assessment of the <i> | + | An assessment of the <i>B. subtilis</i> growth in a similar sap was performed in laboratory conditions with optimum growth medium for <i>B. subtilis</i>. The composition sap used was the one from birch sap.<br> |
In these conditions, the growth rate µ is optimal. From this value we can extrapolate a growth curve as a function of temperature. We used the <b>cardinal temperature model</b>: </p> | In these conditions, the growth rate µ is optimal. From this value we can extrapolate a growth curve as a function of temperature. We used the <b>cardinal temperature model</b>: </p> | ||
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<p class="texte"> | <p class="texte"> | ||
- | In practice, temperature variations are certainly lower in trees than outside, especially if roots extend very deep. Composition of the tree sap must also intervene in the growth rate and nutrient content of sap is also temperature dependent. The effects of the decrease of the temperature in winter also induces a fall of the sap and this must also be involved in the disappearance of our strain in the tree. The period of <i> | + | In practice, temperature variations are certainly lower in trees than outside, especially if roots extend very deep. Composition of the tree sap must also intervene in the growth rate and nutrient content of sap is also temperature dependent. The effects of the decrease of the temperature in winter also induces a fall of the sap and this must also be involved in the disappearance of our strain in the tree. The period of <i>B. subtilis</i> growth is certainly affected by the change of temperature, the rise of sap in the trunk and sap composition variations. All these parameters can consequently slow-down or boost the growth rate. |
The modeling work is done with the programming language 'R' script attached (See Annexe). | The modeling work is done with the programming language 'R' script attached (See Annexe). | ||
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<ul> | <ul> | ||
<li class="tree"><p class="texte"> | <li class="tree"><p class="texte"> | ||
- | Xianling Ji, Guobing Lu, Yingping Gai, Chengchao Zheng | + | Xianling Ji, Guobing Lu, Yingping Gai, Chengchao Zheng,and Zhimei Mu. (2008) <b>Biological control against bacterial wilt and colonization of mulberry by an endophytic <i>Bacillus subtilis</i> strain.</b> FEMS Microbiol Ecol 65: 565–573. |
</li></p> | </li></p> | ||
<li class="tree"><p class="texte"> | <li class="tree"><p class="texte"> | ||
- | A. Garnier(1977) <b>Transfert de sève brute dans le tronc des arbres aspects méthodologiques et physiologiques.</b> Ann. Sci. Foresi. 34 (1): 17-45 | + | A. Garnier. (1977) <b>Transfert de sève brute dans le tronc des arbres aspects méthodologiques et physiologiques.</b> Ann. Sci. Foresi. 34 (1): 17-45. |
</li></p> | </li></p> | ||
<li class="tree"><p class="texte"> | <li class="tree"><p class="texte"> | ||
- | Heikki Kallio , Tuija Teerinen , Seija Ahtonen , Meri Suihko , Reino R. Linko (1989) <b>Composition and properties of birch syrup (Betula pubescens).</b> J. Agric. Food Chem 37 (1): 51–54 | + | Heikki Kallio, Tuija Teerinen, Seija Ahtonen, Meri Suihko, and Reino R. Linko. (1989) <b>Composition and properties of birch syrup (<i>Betula pubescens</i>).</b> J. Agric. Food Chem 37 (1): 51–54. |
</li></p> | </li></p> | ||
<li class="tree"><p class="texte"> | <li class="tree"><p class="texte"> | ||
- | L. Rosso, J. R. Lobry | + | L. Rosso, J. R. Lobry, and J. P. Flandrois. (1992) AN <b>Unexpected Correlation between Cardinal Temperatures of Microbial Growth Highlighted by a New Model.</b> J. theor. Biol. 162 : 447-463. |
</li></p> | </li></p> | ||
<li class="tree"><p class="texte"> | <li class="tree"><p class="texte"> | ||
- | Hiroshi Fujikawa (2010) | + | Hiroshi Fujikawa. (2010) <b>Development of a New Logistic Model for Microbial Growth in Foods.</b> Biocontrol of Science Vol 15: 75-80. |
</li></p> | </li></p> | ||
</ul> | </ul> |
Revision as of 03:17, 18 October 2014
Modeling
To develop a predictive model
Results > Modeling
Modeling is a tool used to simplify and study systems. It helps us to predict behavior of biological systems using bibliographic or experimental data.
The following modelisation focuses on the development of a bacterium in trees. The bacterial growth in trees seems to be unknown, thus we must infer Bacillus subtilis' behavior.
Bacterial Growth
Aim
Bacillus subtilis is a tree endophyte strain. A study showed that B. subtilis could develop and fully colonize a tree, reaching a concentration of 10^{5} cells per gram of fresh plant. We need to know in which conditions the growth of B. subtilis is optimum in a tree and if the weather can stop its development during winter. Therefore we decided to work on the growthof B. subtilis' in function of the temperature during the year.
Modeling bacterial growth in a tree section generates some difficulties. We need to know the distance between two tree extremities (treetops and root) or the speed sap flow. However the flow of speed sap can vary with temperature during the day. The composition of sap also varies due to seasons and type of container (phloem, xylem). Furthermore a tree is not an homogeneous system: its roots, trunk and branches do not contain the same amount of sap and wood.
The average speed of the plane tree sap is 2.4 m/h, which means that in a day the sap of a 30 m tree will flow from one extremity to the other. We thus reduced the tree to a bioreactor.
We make the following hypothesis:
- According to the publication of Xianling Ji (See References), after six months of B. subtilis growth in a tree, bacteria cells reach a concentration of 10^{5} cells per gram of fresh plant. We assume that 10^{5} cells/g is the maximum concentration.
- The composition of the phloem is stable. There is no effect of depletion of the medium.
- Only temperature impacts on bacterial growth.
- It is assumed that there is no leakage of cells.
Method
An assessment of the B. subtilis growth in a similar sap was performed in laboratory conditions with optimum growth medium for B. subtilis. The composition sap used was the one from birch sap.
In these conditions, the growth rate µ is optimal. From this value we can extrapolate a growth curve as a function of temperature. We used the cardinal temperature model:
T: Temperature
µ_{opt}: Optimal growth rate
µ: growth rate at temperature T
T_{max}: Maximum temperature supported by bacteria
T_{min}: Minimum temperature supported by bacteria
T_{opt}: Optimum temperature for the growth
Necessary parameters for this function are minimun temperature T_{min} and maximum temperature T_{max}, optimal temperature for the growth T_{opt} and 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 rate (µ_{opt}) is obtained experimentally with a similar birch sap environment.
The growth rate is negative below 10°C (according to growth tests 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 applied:
If__| T<= 4°C -> µ = -1
____| 4°C
____| T > 10°C -> µ = f(T) with f(T) egal to cardinal temperature model.
Figure 1: bacterial growth (µ) as a function of temperature
A logistic model developed by Hiroshi Fujikawa (See References) is used to study bacterial growth.
General logistics formulas:
In our case, the growth rate µ depends on the temperature.
N corresponds to the bacterial population, N_{min} and N_{max} are two asymptotes.
The parameter m is a curvature parameter. Larger m is, smaller is the curvature of the deceleration phase with the model.
The parameter n is a parameter related to the period lag. Larger n is, shorter is the period of lag.
N_{min} is slightly lower than N_{0}. When N is small at the initial state (N = N_{0}) i.e. N is close to N_{min}, N_{min}/N is almost equal to 1. Therefore the term (1-(N_{min}/N)) is nearly 0 and the growth is very slow.
If N decreases until it reaches N_{min}, the term (1-(N_{min}/N)) is equal to 0. Therefore the growth is null.
Similarly when N is equal to N_{max}, the term (1-(N/N_{max})) is equal to 0 and the growth is blocked.
To overcome this, we worked under two conditions: positive and negative growth. Theses conditions can be translated in two equations. This leads to the writing of this model:
with n = 1 and m = 0.5
The term (1-(Nmin/N)) is not taken into account when there is growth.
The term (1-(N/Nmax)) is not taken into account when there is bacterial decay.
Meteorological records of the Toulouse region during the years 2011-2013 are used to calculate average daily temperatures. Thus we can determine B. subtilis growth in a tree located in Toulouse during a year. This values are obtained for each day by the average on the highest and the lowest temperature.
The density of green wood plane is about 650 kg/m³. The average diameter of the trunks of the concerned trees is about 0.80 m and they are 15 m high. This represents a volume of 30 m³. Therefore the weight of the trunk is 19.604 kg.
We need to add to this weight the weight of branches, twigs, about 25% of leaves and about 15% of roots (source-FR).
The average weight of a tree plane is 27,446kg. We inoculated 10 mL of bacterial culture at 10^{9}cfu/mL, i.e. 10^{10} bacterial cells. This represents 3.64x10^{2}cfu/g of fresh plant (N0).
Figure 2: (black) Bacillus subtilis growth curve during one year (N is cell quantity by g of fresh plant). (red) average temperature. (blue) threshold at 10°C.
In our model, growth starts only from 10°C, which happens between March and April. This period seems to be the most suitable to administer the strain in the tree. Starting in December the temperature decreases below 4°C, corresponding to the threshold below which bacteria starts to die.
Discussion
In practice, temperature variations are certainly lower in trees than outside, especially if roots extend very deep. Composition of the tree sap must also intervene in the growth rate and nutrient content of sap is also temperature dependent. The effects of the decrease of the temperature in winter also induces a fall of the sap and this must also be involved in the disappearance of our strain in the tree. The period of B. subtilis growth is certainly affected by the change of temperature, the rise of sap in the trunk and sap composition variations. All these parameters can consequently slow-down or boost the growth rate. The modeling work is done with the programming language 'R' script attached (See Annexe).
References
Xianling Ji, Guobing Lu, Yingping Gai, Chengchao Zheng,and Zhimei Mu. (2008) Biological control against bacterial wilt and colonization of mulberry by an endophytic Bacillus subtilis strain. FEMS Microbiol Ecol 65: 565–573.
A. Garnier. (1977) Transfert de sève brute dans le tronc des arbres aspects méthodologiques et physiologiques. Ann. Sci. Foresi. 34 (1): 17-45.
Heikki Kallio, Tuija Teerinen, Seija Ahtonen, Meri Suihko, and Reino R. Linko. (1989) Composition and properties of birch syrup (Betula pubescens). J. Agric. Food Chem 37 (1): 51–54.
L. Rosso, J. R. Lobry, and J. P. Flandrois. (1992) AN Unexpected Correlation between Cardinal Temperatures of Microbial Growth Highlighted by a New Model. J. theor. Biol. 162 : 447-463.
Hiroshi Fujikawa. (2010) Development of a New Logistic Model for Microbial Growth in Foods. Biocontrol of Science Vol 15: 75-80.
Annexe
To download the script and the table Click Here