"
Team:TU Munich/Modeling/Methods
From 2012.igem.org
(→Bayesian Inference) |
(→Bayes' Rule) |
||
| Line 34: | Line 34: | ||
=== Bayes' Rule === | === Bayes' Rule === | ||
| - | [[File:TUM12_Bayes.jpg| | + | [[File:TUM12_Bayes.jpg|||center|]] |
== Monte Carlo Methods == | == Monte Carlo Methods == | ||
Revision as of 19:56, 6 September 2012
Contents |
Modeling Methods
Responsible: Fabian Froehlich
Why Mathematical Models
To be able to predict the behavior of a given biological system, one has to create a mathematical model of the system. The model is usually generated according to the Law of Mass Action[reference] and then simplified by assuming certain reactions to be fast. This model then could e.g. facilitate optimizations of bio-synthetic pathways by regulating the relative expression levels of the involved enzymes.
Why are we not necessarily interested in the best fit
To create a model that produces quantitative predictions, one needs to tune the parameters to fit experimental data. This procedure is called inference and is usually accomplished by computing the least squares[reference] approximation of the model to the experimental data with respect to the parameters.
There are several difficulties with this approach: In the case of a non-convex least squares error function several local minima may exist and optimizations algorithms will struggle to find all of them. This means one might not be able to find the best fit or even a biologically reasonable fit.
Another issue with this approach that it gives no information on the shape of the error function in the neighborhood of the obtained fit. Although it is possible to obtain information about the curvature by computing the hessian of the error function, but this can be very computationally intensive if no analytical expression is given and only provides local information.
This poses problems if the error function is very flat. Then in the neighborhood of the set of best fit parameters, a broad range of parameters exist, that produce very similar results. This however drastically reduces the significance of the best fit parameters
What is the benefit of assuming a stochastic distribution of parameters
Usually biological systems exhibit some sort of stochasticity. If the behavior of a sufficiently large amount of cells is observed, it is justifiable to take only the mean value into consideration and thereby assume a deterministic behavior. On the one hand this means we can describe the system with ordinary and partial differential equations instead of stochastic differential equations, which reduces the computational cost of simulating the system. On the other hand this means that we lose the information we could infer from the variance of our measurements as well as higher order moments.
Assuming that parameter are distributed according to some density allows us, to deduce information from variance of measurements, while still describing the system in a deterministic fashion.
Bayesian Inference
Bayes' Rule
Monte Carlo Methods
Sensitivity Analysis
Vision
