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Team:Purdue/Modeling
From 2012.igem.org
(Difference between revisions)
| Line 91: | Line 91: | ||
Population Change Rate = | Population Change Rate = | ||
</td><td nowrap="nowrap"> | </td><td nowrap="nowrap"> | ||
| - | <i>dN</i> | + | <i> dN </i> |
</td> | </td> | ||
<td rowspan = "2" nowrap = "nowrap"> | <td rowspan = "2" nowrap = "nowrap"> | ||
| - | = | + | = bN-dN |
</td> | </td> | ||
</tr><tr> | </tr><tr> | ||
<td class="upper_line"> | <td class="upper_line"> | ||
| - | <i>dt</i> | + | <i> dt </i> |
</td> | </td> | ||
</tr><tr> | </tr><tr> | ||
| Line 105: | Line 105: | ||
</p> | </p> | ||
| - | Where | + | Where N is the population of individuals, b is the rate of births or immigration, and d is the rate of deaths or emigration. |
<p> | <p> | ||
| - | To understand | + | To understand these dynamics of population in the context of biofilm growth, we treat the bacterial attachment and detachment as a reversible reaction of the form |
<p> | <p> | ||
<style> | <style> | ||
| Line 118: | Line 118: | ||
<tr> | <tr> | ||
<td rowspan="2" nowrap="nowrap"> | <td rowspan="2" nowrap="nowrap"> | ||
| - | Bacteria Attached < | + | N <sub>Bacteria Attached</sub> < |
</td><td nowrap="nowrap"> | </td><td nowrap="nowrap"> | ||
<i>k<sub>1</sub></i> | <i>k<sub>1</sub></i> | ||
</td> | </td> | ||
<td rowspan = "2" nowrap = "nowrap"> | <td rowspan = "2" nowrap = "nowrap"> | ||
| - | > Bacteria Detached | + | > N <sub>Bacteria Detached</sub> |
</td> | </td> | ||
</tr><tr> | </tr><tr> | ||
| Line 133: | Line 133: | ||
</table> | </table> | ||
</p> | </p> | ||
| + | Where k<sub>1</sub> is the rate constant of detachment and k<sub>2</sub> is the rate constant of attachment. | ||
</h5> | </h5> | ||
Revision as of 19:06, 13 July 2012
Modeling
Levels of Investigation
- How biofilm responds to shear, flow, temp, surface attachment, etc
- How Silica traps the particle during flow
- How Curli adheres and how OmpA-Silicatein Alpha polymerizes Silica/How the silica crystalizes (introduction of Salicylic Acid)
- How protien expression responds to external and metabolic variation (e.g. introduction of IPTG to system)
- How constructs work with each other/controls systems
- How RBS/ Promoter effects protein expression
Distribution of Modeling
Matlab
- Protein Production
- Feed Forward Loop Control Structure
TinkerCell
- Quorum sensing
- Feed Forward Loop
JMP
- Experimental Design and Characterization Experiments
Comsol/Other
- Waterflow and shear force on final system
- Silica formation
Design
- Desired Outcomes of Models
- Levels of Abstractions
- Biofilter response to Environmental conditions
- Shear
- Flow
- Temperature
- Abiotic Surface (Adhesion)
- Formation of Silica Matrix
- BioFilm Development
- Model of Bacterial Growth, Death, Breaking Off
- Expression of Proteins
- Response to addition of IPTG
- Optimal Production rate/expression of Curli and OmpA-Silicatein Alpha protiens
- Control Systems
- Fine Tuning of Protein Expression with RBS/Promoter combination variants
- Platforms
- Considerations and Assumption
- Parameters
Equations
The growth of the Biofilm can be modeled with modified continuous growth models. Traditional models of population growth make use of basic differential equations to measure the change in population over unit time. At the most fundamental level
| Population Change Rate = | dN | = bN-dN |
| dt | ||
To understand these dynamics of population in the context of biofilm growth, we treat the bacterial attachment and detachment as a reversible reaction of the form
| N Bacteria Attached < | k1 | > N Bacteria Detached |
| k2 | ||
Outcomes
| Parameter | Theoretical Value | Experimental Value | Analysis |
| Parameter 1 | ____ | ____ | ____ |
| Parameter 2 | ____ | ____ | ____ |
| Parameter 3 | ____ | ____ | ____ |
| Parameter 4 | ____ | ____ | ____ |
