Team:Groningen/modeling vision

From 2011.igem.org

(Difference between revisions)
Line 9: Line 9:
==How we came to building Cumulus==
==How we came to building Cumulus==
-
Even building a simpel model of  
+
One of the first things we did when we started to model our circuit was to review the most popular modeling tools in the iGEM compitetion. You can read the results of this review [here].
 +
 
 +
We decided
 +
 
 +
===The quest for paramters===
 +
Even building a simpel model of genetic circuit will give the most experienced modelers pause. This is mainly because the behaviors of the different biobrick parts are poorly characterised especially when it comes to paramters. While trying to model our curcuit 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 charackterisation maybe we could produce some more dependable results.
 +
 
 +
Cumulus uses a parameter optimalisation program that is capable of evaluating a single parameter setting in the context of multiple experiments.
 +
 
 +
 
 +
===Scaling up the computation===
 +
All this simulating and comparing would consume large amounts of computational resources. It is strange to expect from scientists that they
 +
We wanted to make cumulus an open platform on which everyone can share data. This is why we paralelised cumulus as a cloud aplication
-
We quickly discovered that the proportion of
 
-
It is for these reasons that we started work on Cumulus.
 
==Advantages of Cumulus==
==Advantages of Cumulus==

Revision as of 14:21, 20 September 2011


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. A world in which not just data but also the models supporting that data


Cumulus is our way of making this world come a littlecloser.

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 modeling tools in the iGEM compitetion. You can read the results of this review [here].

We decided

The quest for paramters

Even building a simpel model of genetic circuit will give the most experienced modelers pause. This is mainly because the behaviors of the different biobrick parts are poorly characterised especially when it comes to paramters. While trying to model our curcuit 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 charackterisation maybe we could produce some more dependable results.

Cumulus uses a parameter optimalisation program that is capable of evaluating a single parameter setting in the context of multiple experiments.


Scaling up the computation

All this simulating and comparing would consume large amounts of computational resources. It is strange to expect from scientists that they We 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 ad

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

  • Exchangable models, Share not only you data but also the model wich you think describes the data.
  • Simulate your cells unsing a simple simulation engine
  • Fit the paramaters of you simulation to exprimental data at the click of a button.
  • 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.