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Team:Groningen/modeling vision
From 2011.igem.org
Vision
We imagine a world in which biologist are able to share their data with the click of a button. A world in which thorough reliable measuring procedures exist that help scientist characterize their parts using a reliable user friendly computing environment. A world in which not just data but also the models supporting that data are shared. A world in which scientist can not only share the burden of modelling but also the burden of computation.
Cumulus is our way of making that world come a little closer. Cumulus has been developed to run both on the cloud and on a local grid by combining the azure platform with windows clients. Considering as a study case the parameter study applications for our particular project.
How we came to building Cumulus
One of the first things we did when we started to model our circuit was to review the most popular modelling tools in the iGEM competition. You can read the results of this review [here]. We decided
The quest for parameters
Even building a simple model of genetic circuit will give the most experienced modellers pause. This is mainly because the behaviours of the different biobrick parts are poorly characterised especially when it comes to parameters. While trying to model our circuit we quickly discovered that most of the parameters we found in literature where very specific to the situation of publication and did not generalize to other circuits. In our opinion this is because scientist are most used to sharing results and not their raw data. If we could find a way to combine all the data available on a part into a single characterisation maybe we could produce some more dependable results.
Scaling up the computation
Cumulus uses a parameter optimization program that is capable of evaluating a single parameter setting in the context of multiple experiments by simulating all the experiments with the same paramters and combining the score lateron. All this simulating and comparing would consume large amounts of computational resources. It would be unreasonable to asume the every scientist can simple amas such gridcomputing facileties for himself. We also wanted to make cumulus an open platform on which everyone can share data. This is why we paralelised cumulus as a cloud aplication
Advantages of Cumulus
The combination of cloud based computation and generic modeling, yields a lot of advantages. Some of them are listed below:
Advantages of a cloud based application
- General public access, everyone can use everyones data, models and results.
- Sharing computational resources enables us the reap the economy of scale.
- The flexibility of a cloud application helps to avoid the resource wast of underutilisation.
Advantages of our generic modelling approach
- Exchangeable models, Share not only you data but also the model which you think describes your parts best.
- Simulate your cells using a simple simulation engine
- Fit the parameters of you simulation to experimental data at the click of a button.
- Genetic algorithms allow cumulus to explore very large parameters spaces.
- Reap the benefits of overlapping experiment, our modelling system allows you to use data from two experiments that share some, but not all, biobrick parts to improve the characterisation of all parts in both experiments.
