I am currently a post-doctoral researcher working with Jochen Triesch at the Frankfurt Institute for Advanced Studies. My PhD work was undertaken with Janet Wiles at the University of Queensland, where I also did my undergraduate work.
I'm part of Jochen Triesch's theoretical neuroscience group. Other group members are: Arthur Franz, Andreea Lazar, Cristina Savin, and Cornelius Weber.
In my undergraduate degree I majored in computer science with a side-emphasis on cognitive science. My Honours thesis involved investigations into interactions between learning and evolution (i.e., Baldwin-effect related simulations) using evolutionary algorithms operating on a simple neural-network predator-prey model. After my Honours degree, I moved away from evolution and neural-network modelling, and towards abstract network-based modelling of genetic regulatory systems. Over the last few years, I have been working with Boolean network models (both Random Boolean Networks and biologically-based models). My PhD thesis covers much of this work, and my current position applies the methodologies developed in my thesis to a Boolean neural-network model for gene co-expression networks.
The underlying theme to my research is that of computational modelling and the modelling of complex systems; I believe that computational modelling can be used to provide us with insights into the behaviour and causes of behaviour in a wide range of systems. Similarly, complex systems modelling allows us to understand classes of behaviour within systems, and can help to explain the (sometimes unexpected) behavioural regularities often displayed in real-world systems.
Current Curriculum Vitae for Kai Willadsen: [PDF]
For further details on my research, qualifications or similar matters, please feel free to contact me.
Willadsen, K. “Robustness in Boolean Models of Genetic Regulatory Systems”
[Abstract] [PDF] (3.2M PDF file)
My thesis focuses on characterising and understanding robustness in Boolean models of genetic regulatory systems, both in terms of abstract models—specifically the Random Boolean Network model—and in terms of models of real-world regulatory systems. In particular, robustness is defined as the effects of state perturbation in terms of switching between attractors and attractor basins, and is investigated in terms of a system's state-space structure.
Geard, N. and Willadsen, K. (2009). “Dynamical approaches to modeling developmental gene regulatory networks”. Birth Defects Research Part C: Embryo Today: Reviews, 87 (2), 131-142.
10.1002/bdrc.20150
Willadsen, K., Triesch, J. and Wiles, J. (2008). “Understanding robustness in Random Boolean Networks”. In S. Bullock, J. Noble, R. Watson, and M. A. Bedau (eds.) Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, pp. 694-701. MIT Press, Cambridge, MA.
[PDF]
Willadsen, K. and Wiles, J. (2007). “Robustness and state-space structure of Boolean gene regulatory models”. Journal of Theoretical Biology, 249 (4), 749-765.
10.1016/j.jtbi.2007.09.004
Watson, J., Wakabayashi, M., Moore, J., Montoya, A. S., Willadsen, K., Geard, N., Bradley, D. and Wiles, J. (2007). “Computational techniques for modeling complex biological systems”. In The 8th Asia-Pacific Complex Systems Conference (Complex'07).
Geard, N., Willadsen, K. and Wiles, J. (2005). “Perturbation Analysis: A
Complex Systems Pattern”. In H. Abbass, T. Bossamaier, and J. Wiles (eds), Recent Advances in Artificial Life, volume 3 of Advances in Natural Computation, World Scientific. pp. 69-84.
[Abstract] [PDF]
Wiles, J., Geard, N., Watson, J., Willadsen, K., Mattick, J., Bradley, D. and Hallinan, J., (2005). “There's more to a model than code: Understanding and formalizing in silico modeling experience”. In F. Rothlauf et al. (editors) Workshop Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2005, New York, NY: ACM Press. pp. 281-288.
[Abstract] [PDF]
Willadsen, K. and Wiles, J. (2003). “Dynamics of Gene Expression in an Artificial Genome”. In R. Sarker, R. Reynolds, H. Abbass, K.-C. Tan, R. McKay, D. Essam and T. Gedeon (eds), Proceedings of the 2003 Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press. pp. 185-190.
[Abstract] [PDF]
Willadsen, K. and Wiles, J. (2002). “All binary representations are equal:
but some are more equal than others”. In D. B. Fogel, M. A. El-Sharkawi, X. Yao, G. Greenwood, H. Iba, P. Marrow and M. Shackleton (eds), Proceedings of the 2002 Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press. pp. 570-575.
[Abstract] [PDF]
Willadsen, K. and Wiles, J. “Basin coherency as a measure of stability in Random Boolean Networks”. Presented on the 8th March 2005 at the second workshop of the Network Theory Working Group of the ARC Complex Open Systems Research Network.
Talk to FIGSS: PDF
FIAS Summer School 2007 lecture on network theory: PDF
“An Introduction to Graph Theory in Complex Systems Studies” is a project I undertook in semester 2, 2004 for the ARC Centre for Complex Systems. The goal was to provide a brief introduction to graph theory and associated reference materials for complex systems researchers who were unfamiliar with the application of graph theoretic concepts and techniques. Some of the resulting work is available at:
http://www.itee.uq.edu.au/~gtheory/
“Random Boolean Networks” is a small tutorial I produced with Ben Skellett for the ARC Centre for Complex Systems Winter School. The aim of this tutorial was to give an brief overview of the concepts surrounding Random Boolean Networks and their use as a model of complex behaviour in discrete dynamic systems. The (currently unmaintained) tutorial is available at:
http://fias.uni-frankfurt.de/~willadsen/RBN/
Last modified 14 August 2008