Latest revision as of 21:03, 21 September 2011

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. In summary we decided all the available tools were to constraining and that custom build software was in order.

The quest for parameters

Even building a simple model of a genetic circuit will give the most experienced modellers pause. Mainly because the behaviors of different biobrick parts are poorly characterized. 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 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 characterization maybe we could produce some more robust results.

Scaling up the computation

Cumulus uses a parameter optimization algorithm that is capable of evaluating a single parameter setting in the context of multiple experiments by simulating all the experiments with the same parameters and combining the score later on. All this simulating and comparing would consume large amounts of computational resources. It would be unreasonable to assume that every scientist can simple amass such grid-computing facilities for himself. So we also wanted to make cumulus an open platform on which everyone can share data. This is why we designed cumulus as a cloud enabled application with parallelisation as its core criterium.

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 everyone data, models, data 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 under-utilization.

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 high dimensional parameters spaces.
Reap the benefits of overlapping experiments. Our modelling system allows you to use data from two different experiments that share some, but not all, biobrick parts. Hence we can better improve the characterization of all parts in both experiments.

@@ Line 1: / Line 1: @@
 {{HeaderGroningen2011}}
 =Vision=
-We imagine a world in which biolegist are able to share their data with the click of a button. A world in which torough reliable measuring procedures exist that help scientist caracterise their parts at the click of a button.
+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.
-A world in which not just data but also the models supporting that data
+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 [https://2011.igem.org/Team:Groningen/project_description 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 [https://2011.igem.org/Team:Groningen/modeling_methods here]. In summary we decided all the available tools were to constraining and that custom build software was in order.
-Cumulus is our way of making this world come a littlecloser.
+===The quest for parameters===
+Even building a simple model of a genetic circuit will give the most experienced modellers pause. Mainly because the behaviors of different biobrick parts are poorly characterized. 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 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 characterization maybe we could produce some more robust results.
-==Why we build Cumulus==
+===Scaling up the computation===
-Even building a simpel model of
+Cumulus uses a parameter optimization algorithm that is capable of evaluating a single parameter setting in the context of multiple experiments by simulating all the experiments with the same parameters and combining the score later on. All this simulating and comparing would consume large amounts of computational resources. It would be unreasonable to assume that every scientist can simple amass such grid-computing facilities for himself. So we also wanted to make cumulus an open platform on which everyone can share data. This is why we designed cumulus as a cloud enabled application with parallelisation as its core criterium.
-We quickly discovered that the proportion of
-It is for these reasons that we started work on Cumulus.
 ==Advantages of Cumulus==
-The ad
+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 aplication===
-* General public acces, everyone can use everyones data
-* Sharing computational resources enables us the reap the econmy of scale.
-* The flexibilety of a cloud application helps to avoid the resource wast of underutilisation.
-Share in cheap
-and en enjoy
-===Advantages of our generic modeling approuch===
+===Advantages of a cloud based application===
-* Exchangable models, Share not only you data but also the model wich you think describes the data.
+* General public access, everyone can use everyone data, models, data and results.
-* Simulate your cells unsing a simple [https://2011.igem.org/Team:Groningen/simulation_engine simulation engine]
+* Sharing computational resources enables us the reap the economy of scale.
-* Fit the paramaters of you simulation to exprimental data at the click of a button.
+* The flexibility of a cloud application helps to avoid the resource wast of under-utilization.
-* [https://2011.igem.org/Team:Groningen/genetic_algorithms Genetic algorithms] allow cumulus to explore very large parameters spaces.
-* Reap the benifits of overlapping experiment, our modeling 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.
+===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 [https://2011.igem.org/Team:Groningen/modeling_simulation_engine simulation engine]
+* Fit the parameters of you simulation to experimental data at the click of a button.
+* [https://2011.igem.org/Team:Groningen/modeling_genetic_algorithms Genetic algorithms] allow cumulus to explore very large high dimensional parameters spaces.
+* Reap the benefits of overlapping experiments. Our modelling system allows you to use data from two different experiments that share some, but not all, biobrick parts. Hence we can better improve the characterization of all parts in both experiments.
 {{FooterGroningen2011}}

Team:Groningen/modeling vision

From 2011.igem.org