Analog Genetic Encoding Overview Evolution of Analog Electronic Circuits Evolution of Artificial Neural Networks Evolutionary Reverse Engineering Biochemical Networks
overview reverse engineering DREAM benchmark generation student projects

Biomimetic Reverse Engineering of
Gene Regulatory Networks

Daniel Marbach, Claudio Mattiussi, Thomas Schaffter, Dario Floreano

The aim of this project is the exploration of a novel approach to effectively reverse engineer gene regulatory networks from quantitative data, emphasizing an accurate inference with biologically plausible, nonlinear models.

Genetic regulatory networks perform fundamental information processing and control mechanisms that are essential for the survival and correct functioning of biological cells. Reverse engineering and modeling gene networks is a necessary first step in understanding cells at a system level and is expected to have substantial impact on the pharmaceutical and biotech industries in the next decades.

We are currently working on the following research topics:

Journal Papers

[5] Marbach, D., Schaffter, T., Mattiussi, C. and Floreano, D. (2009) Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods. Journal of Computational Biology, 16(2) pp. 229-239. [detailed record] [pdf] [bibtex]
[4] Marbach, D., Mattiussi, C. and Floreano, D. (2009) Combining Multiple Results of a Reverse Engineering Algorithm: Application to the DREAM Five Gene Network Challenge. Annals of the New York Academy of Sciences, 1158 pp. 102-113. [detailed record] [pdf] [bibtex]
[3] Marbach, D., Mattiussi, C. and Floreano, D. (2009) Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks. Annals of the New York Academy of Sciences, 1158 pp. 234-245. [detailed record] [pdf] [bibtex]
[2] Mattiussi, C., Marbach, D., Dürr, P. and Floreano, D. (2008) The Age of Analog Networks. AI Magazine, 29(3) pp. 63--76. [detailed record] [pdf] [bibtex]

Conference Papers

[1] Marbach, D., Mattiussi, C. and Floreano, D. (2007) Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks. Proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2007), pp. 155-165. [detailed record] [pdf] [bibtex]
Published: 30.08.2006/dm      Last update: 23.02.2010/dm

[16 Jun 2009] The LIS releases the gene-network inference challenges of the 4th DREAM conference, to be held in December 2009 at the Broad Institute of MIT and Harvard.

[21 Oct 2008] About 30 teams participate in the In Silico Challenges provided by the LIS for the 3rd DREAM Conference at MIT.

[26 Nov 2007] DREAM2 gene network reverse engineering challenges: the biomimetic approach based on Analog Genetic Encoding (AGE) is best performer in the five-gene network challenge.