From 2007.igem.org

On June 1st 2007, a group of undergraduate of various disciplines and from around the country assembled at NCBS, to attempt a 'proof of principle' demonstration. Their mission: to assemble and test a 'genetically engineered machine', a complex network assembled from simple biological parts. You will be amazed how much they managed to achieve in just six weeks!
The National Centre for Biological Sciences is a research institute located in Bangalore, India. In 2006, a team of graduate students from NCBS was the first from India to participate in iGEM. This year, we are very excited to compete as an all-undergraduate team of six students, from six different colleges located in four cities around India. Now that the summer is over, we hope hope to take the excitement of iGEM back to our home institutions, and create new teams across India who will participate in future iGEMs.
	[http://www.ncbs.res.in/ National Centre for Biological Sciences, Bangalore]

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Bottom-up biology: A proof of principle

As synthetic biologists, we aim to build biological systems from the bottom-up. The general idea is that, by measuring the behavior of simple parts, we should be able to design and construct complex systems from those parts. But can this be done in practice?

We set out to test this idea rigorously. Our project is designed so that, by measuring the characteristics of relatively simple devices called 'open-loops', we are able to predict the responses of more complex systems called 'closed-loops'. These predictions can be directly tested against the observed responses. If we are successful, it would represent the first experimental validation of this powerful bottom-up design principle.

= How to design systems +

We might try to use detailed mathematical model of these systems. However, even the simplest model will contain many parameters whose values are unknown (for example, we have built a model with three dynamical variables, but 10 dimensionless parameters). We could imagine carefully designing experiments to extract all these parameter values, but once the system becomes moderately large, this becomes impractical. What we need is an approach that scales up easily.

Suppose we are interested in only certain aspects of system behavior, many more approaches become available.

and carefully design experiments to extract the values of all its parameters. However, even the simplest models involve many unknown quantities.