Using sequential Monte Carlo approaches as a design tool in synthetic biology

author: Chris Barnes, Theoretical Systems Biology Group, Imperial College London
published: May 3, 2010,   recorded: March 2010,   views: 4288


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


In many engineering contexts it is easy to state what we want but hard to achieve our desired outcomes. The more potential solutions exist, the harder it becomes to identify optimal solutions. Here we show how this problem can be approached in an approximate Bayesian computation framework. Our approach has the advantage that it builds on the powerful Bayesian model selection formalism, includes sensitivity and robustness analysis at no extra cost, and flexibly incorporates diverse design objectives. We illustrate the performance of this approach in the context of bacterial two-component systems (TCS). These systems enable prokaryotes (and some simple eukaryotes and plants) to sense their environments and adapt their internal state to changing circumstances. We present a detailed analysis of orthodox and unorthodox TCSs and show how we can rationally construct TCS that show robust and optimal response characteristics to different stimuli encountered during bacterial infections or in biotechnological (e.g. biofuels production and bioremediation) applications. We conclude by elaborating on the connections between our approach and maximum-entropy procedures and the advantages over traditional engineering strategies.

See Also:

Download slides icon Download slides: licsb2010_barnes_usm_01.pdf (2.3┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: