Rafik Salama’s PhD thesis

Rafik Salama’s PhD thesis and abstract have now been posted for public view on the University of Birmingham web site:



Computational biology approaches for studying gene regulatory network discovery and modelling


The advent of next generation sequencing has increased the gap between genome sequence data and knowledge, enhancing the need for faster means to fill this gap. The development of efficient computational biology methods to handle this gap has never been so important. Gene regulatory networks in particular have been studied widely for their role in controlling cellular behaviour, resulting in manifold phenotypic characteristics. In this thesis, I present novel techniques contributing to the discovery of gene regulatory network connections, through enhanced binding site prediction, binding site multiple sequence alignment and binding site specificity. Another major advantage of computational biology is the ability to simulate the behaviour of gene regulatory networks, in order to study the governing dynamics of such networks. In this thesis, I also introduce a new modelling language bringing computational modelling capabilities into the biological domain to simplify the process of writing a model that can be simulated in silico. I have proved through this work that: first, the devised computational biology techniques can provide cheap yet powerful and efficient techniques to study gene regulatory networks; and second, the techniques presented have novel superiority over current research in this domain.


3 thoughts on “Rafik Salama’s PhD thesis

  1. Hi Julie-Ameline,

    Yes, I think you should be able to download it from the link to the etheses at Birmigham (I’ve just made it active).


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s