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Team:NTNU Trondheim/Modeling
From 2011.igem.org
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= Modeling = | = Modeling = | ||
| - | To describe the biological reactions and process as the bacteria turns red | + | To describe and understand the biological reactions and process, as the bacteria turns red under stress we developed multiple mathematical and statistical models. As the most basic model we used a system om ordinary differential equations (ODE), this is a fully deterministic model, describing the change in concentration for all molecules involved. The process for a bacteria to go from normal to glowing red is involved, and one can think of this as a stochastic process, that at each step, the process can ether succeed or fail, with a given probability. This gives rise to a Bayesian model. |
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| + | Combining these two models will result in a system of stochastic differential equations (SDE), which will be solved using numerical algorithms. As a last model we will explore the relationship between variants of stress and fluoricene intensity using (non)-conventional regression. | ||
| + | == Model Introduction == | ||
| + | At the hart of the modeling lies biological consistency and data integration. The modeling will be focused on interpretation simplicity and data consistency. That is to develop models that can be easily interpreted by biologist and mathematicians, but the models should also strive to describe that which is observed at the laboratory. | ||
| + | The two main ways to model biological systems. One way is deterministic, the other is stochastic. In this project we will attempt to approach to problems in both ways. Using a deterministic model, with fixed parameters, and a stochastic model to integrate data more dynamically. | ||
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== The Models== | == The Models== | ||
| + | Four basic models where constructed, which will be described below. | ||
=== Systems of ODE === | === Systems of ODE === | ||
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=== Bayesian Hierarchy === | === Bayesian Hierarchy === | ||
| - | We then wish to model the reliability for the observations... That is the probability of false positive/negative results | + | We then wish to model the reliability for the observations... That is the probability of false positive/negative results P(RTF = 1|stress) -- NO |
| - | P(RTF = 1|stress) | + | |
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| - | + | === Stochastic Differential equations === | |
| + | This is AWESOME | ||
| - | === Linear | + | === Linear/ non-linear Regression === |
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== Model Validation == | == Model Validation == | ||
| - | + | No data yet. | |
== References == | == References == | ||
Revision as of 14:16, 16 July 2011
Modeling
To describe and understand the biological reactions and process, as the bacteria turns red under stress we developed multiple mathematical and statistical models. As the most basic model we used a system om ordinary differential equations (ODE), this is a fully deterministic model, describing the change in concentration for all molecules involved. The process for a bacteria to go from normal to glowing red is involved, and one can think of this as a stochastic process, that at each step, the process can ether succeed or fail, with a given probability. This gives rise to a Bayesian model.
Combining these two models will result in a system of stochastic differential equations (SDE), which will be solved using numerical algorithms. As a last model we will explore the relationship between variants of stress and fluoricene intensity using (non)-conventional regression.
Model Introduction
At the hart of the modeling lies biological consistency and data integration. The modeling will be focused on interpretation simplicity and data consistency. That is to develop models that can be easily interpreted by biologist and mathematicians, but the models should also strive to describe that which is observed at the laboratory.
The two main ways to model biological systems. One way is deterministic, the other is stochastic. In this project we will attempt to approach to problems in both ways. Using a deterministic model, with fixed parameters, and a stochastic model to integrate data more dynamically.
The Models
Four basic models where constructed, which will be described below.
Systems of ODE
Bayesian Hierarchy
We then wish to model the reliability for the observations... That is the probability of false positive/negative results P(RTF = 1|stress) -- NO
Stochastic Differential equations
This is AWESOME
Linear/ non-linear Regression
∫
Model Validation
No data yet.
References
