Dynamics of biological networks: from nodes' dynamics to network evolution

25 and 26 June, 2013
Informatics Forum
University of Edinburgh


Biological systems' dynamics represent a prime example of complex behaviour, providing a powerful motivation and application domain for computational modelling. Of central importance is the learning problem, i.e. how can we reconstruct information about the underlying state of the system from noisy measurements; this is often an essential first step to further modelling and analysis, and to attempts to predict and control system behaviour. The complex behaviour observed in biological dynamics is often the result of a non-trivial network of interactions between individual components of the biological system: this is true at a variety of scales and level of abstractions, from physiology, to neuroscience, to molecular interactions within a single cell.

Modelling of biological networks is often divided into two sub-problems: given the structure of the interaction network (i.e., which components interact with each other), one may be interested in reconstructing the dynamics of the individual nodes from partial observations of (some of) the nodes' states, using techniques from system identification to estimate states and parameters of the system. In this case, the known structure allows researchers to utilize plausible models of system dynamics, often involving a continuous time representation and/ or nonlinearities. On the other hand, much research in the last decade has focused on learning the structure of the underlying interaction network, assuming a greatly simplified model for the dynamics of the nodes (typically an autoregressive linear Gaussian model) and focussing computational power on the combinatorial problem of learning network structure. In most cases, the structure of the network is assumed to be fixed, but recently time-varying networks have been proposed, which enable richer dynamics and may better reflect underlying biological changes in processes such as development or adaptation.

In this workshop, we aim to bring together experts in dynamical systems and structure learning in order to promote cross fertilisation between the two communities, thus generating novel collaborations that will lead to a more integrated and holistic modelling of biological networks. The workshop will be include four keynote talks by leading international experts, as well as shorter contributions from the workshop participants. Plenty of time will be allowed for discussion and definition of the key problems ahead for the community.

Keynote speakers

Dirk Husmeier (Glasgow)
Heinz Koeppl (ETH Zurich)
Olga Troyanskaya (Princeton)
Eric Xing (CMU)


The workshop registration will start at 11:30am on Tuesday, 25 June.

25 June
12:00-13:00: Lunch

13:00-14:00 Keynote lecture: Dirk Husemeier - Bayesian models for inferring gene regulatory networks and processes in systems biology

14:00-14:20 Roberto Visintainer: Distances and Stability in Biological Network Theory
14:20-14:40 Ke Yuan: Capturing rewiring events during network evolution underlying dynamic biological processes
14:40-15:00 Steven Hill: Data-driven inference of causal molecular networks and systematic validation of inference performance

15:00-15:30 Coffee

15:30-16:30 Keynote lecture: Olga Troyanskaya - From functional genomics data to understanding human disease: Modeling tissue-specificity in gene expression and interactions
16:30-17:30 Discussion session

17:30-18:30 Posters and drinks

Dinner 20:00

26. June
09:30-10:30 Keynote lecture: Heinz Koeppl - Statistical Inference of Cellular Behavior from Data

10:30-11:00 Coffee

11:00-11:20 Van Anh Hunynh-Thu: Network reconstruction from time series expression data using tree-based methods
11:20-11:40 Néhémy Lim: Boosting an operator-valued kernel-based model for gene regulatory network inference
11:40-12:00 David Oaken: Optimisation of process algebra model structures using genetic programming

12:00-13:20 Lunch

13:20-13:40 Ian Simpson
13:40-14:00 Chris Oates: Network inference and dynamical prediction using biochemical kinetics

14:00-15:00 Keynote lecture: Eric Xing - Reverse Engineering Evolving Gene Networks Underlying Developing Biological Systems: a principled statistical machine learning approach


Attendance of the workshop is free. At this point we invite contributed talks and posters. Please register and submit your abstract using the following website: Workshop Registration


Guido Sanguinetti (Edinburgh)
Edoardo Airoldi (Harvard Statistics)
Matthias Hennig (Edinburgh)


BBSRC International Partnering Award between Edinburgh and Harvard (to G.S.)
The Scottish Informatics and Computer Science Alliance