Other than where specified, these are my notes from the IB07 Conference, and not expressions of opinion. Any errors are probably just due to my
own misunderstanding. 🙂
Talk about multi-value networks, high-level petri nets, and the differences with boolean networks. Formal methods are required to model and analyse complex regulatory interactions. Boolean networks offer a good starting point, but are often too simplistic. Multi-value networks (MVNs) are qualitative, and are seen as a middle ground between differential equation models and boolean networks.
He has applied high-level petri net techniques and a wide range of analysis tools. In MVNs, entities assume a range of values (o…n). Each entity has a neighbourhood of other entities that affect it, and the behaviour of each entity is described using state tables. However, we can't really analyse this: that's where Petri nets come in. They have a graphical notation with mathematical semantics and can model choice, synchronization and concurrency. They have an expressive framework with data types and equational description of behaviour. There are a wide range of analysis techniques and tool support, e.g. model checking. Petri nets use a kind of tokenizing system.
Their approach was as follows. They have defined a set of state transition tables that completely define the model. Equational definitions are extracted from these tables, and then a Petri net is constructed. They also use multi-value logic minimalization applied to each state transition table to simplify the information from the tables. Construction of the high-level Petri net begins with a single place for each entity connected to central transition. Transition encodes equational specification of network behaviour. Each placed "x" is connected to the transition node with input arch "x and output arc x".
They showed how this worked through carbon starvation in E.coli. Exponential growth occurs where there is sufficient carbon, but they enter a stationary phase when the carbon is depleted. The model is validated by checking known properties. Then, you can look at dynamic properties. A mutant analysis was also done, where you can "knockout" or overexpress key genes and observe the effect.
Finally, they do a model comparison with the Boolean network equivalent of this model. There are differences, which leads to some interesting questions: how much detail is required in the model? Is the model representable in the boolean domain?
My opinion: A great, interesting talk that flowed well and was easy to understand. Slides were a little overfull, but it didn't detract. A natural speaker.