matlab-ode-inference is a repository to study Biologically inspired dynamical systems with mechanistic models (such as ODEs). Inferring parameters of a mechanistic models involves answering a lot of questions:
- How can we incorporate prior information about parameters and models into our modeling framework?
- How does the choice of the model affect the estimates of the inferred parameters?
- How can we use a Bayesian approach (for example, MCMC type samplers) to model these systems?
We want to compare different methods and techniques on simple in-silico of experimental datasets.
First clone the mcmcstat package and add it to Matlab's path.
git clone git@github.com:mjlaine/mcmcstat.git
git clone git@github.com:B2-Bayesian-for-Biology/matlab-ode-inference.git
Once you have cloned the repository you can look at a simple example of modeling a growth-curve of cells driven by resources, modeled by an ODE.
How to tune the adaptation step of a Delayed rejection? TO DO
- Should the data be log transformed?
- Should some of the parameters be sampled in the log scale?
- Language and implementation: MAtlab -- mcmcstat -- very slow! But transparent!
- ODE solver: ode45.
- Error Model: inv-gamma; assumped independence of points, multiplicative likelihood, compared log(data) with log(model)
- Convergence tests: Gelman rubin, Autocorrelation of chains, geweke.
- Post processing: burn = 3000 -- need burning for DRAM-MCMC, need atleast 10 chain thinning for.
Here's the convention we used (for now).
- Initial condition fixed from data for P0 and varied for N0
- flags: Logtransformed = 1 means log(Data) and log(model) compared.
- flags: Logprior = 1, LogNormal Priors used for N0 and Qs
- You can find chains saved in results
- You can find plots saved in figures
- Current contributions by Raunak Dey at UMD.
- Part of the big-project led by David Talmy at UTK.
- Please feel free to reach out to me at my email