Supporting and supplemental information for the paper "Regulation of Gene Regulation - Smooth Binding with Dynamic Affinity affects Evolvability"

Schematic Protein / Genome / Environment interaction of differentiated cells.
On this web page you can find supporting and supplemental information for the paper "Regulation of Gene Regulation - Smooth Binding with Dynamic Affinity affects Evolvability", presented at the CEC Special Session on Evolving Gene Regulatory Networks, held as part of WCCI 2008. The commented, full Java source code is available as well as results of newer experiments.
You might want to have a look at our related papers. Currently to be found on this page:
What is in the paper?

Schematic drawing of the change of the network structure after one single bit mutation occurs, for (top) the perfect matching and (bottom) the smooth matching condition. Bolder lines represent stronger regulating influences. Note however that specificity factors can complicate this picture by dynamically changing affinities.
Abstract   Understanding the evolvability of simple differentiating multicellular systems is a fundamental problem in the biology of genetic regulatory networks and in computational applications inspired by the metaphor of growing and developing networks of cells. We compare the evolvability of a static network model to a more realistic regulatory model with dynamic structure. In the former model, each regulatory protein-binding site is always influenced by exactly one gene product. In the latter model, binding is only more likely to occur the better the match between site and gene product is (smooth binding) and, in addition, affinity dynamically changes under the action of specificity factors during a cell's lifetime. On evolutionary timescales, this means that often the strength of influences between nodes is perturbed instead of direct changes being made to network connectivity. A main result is that for evolutionary search spaces of increasing sizes evolved performance drops much more strongly in the classical network model as compared to the smooth binding model. This effect was even greater in the case of using smooth binding together with specificity factors.
Check University of Hertfordshire Research Archive for personal copy

Network dynamics visualization with a Java Applet
At the moment only the classic network model with perfact matching is shown here.
Please choose between three networks by selecting from the drop-down menu: 
The source code for this applet can be found in the section "Java code".

Java source code
Please feel free to download the full source code of the simulation, in commented Java. Structuring of the code in quite a few classes hopefully increases readability even more. More code, Linux scripts and Latex templates included for automatic evaluation of results and optional running of simulations in a distributed Condor cluster environment.
Please see the README.TXT file included in the zip archives for details!
This software is distributed under the GNU General Public License (GNU GPL).

More experiments

Outcomes of experiments with added genes and gradual differentiation pressure. GRNs were pre-evolved to achieve one task with 5 genes ("one celled organisms"), than two genes were added and performance at both tasks ("two celled organisms") was used as fitness measure. The leftmost column depicts the environmental stimuli used and the topmost row the desired output behavior for every run. Data cells show the best final deviation for runs with duplication of two genes and addition of two randomly created genes (All values are averaged over 10 runs with 500 generations times 250 individuals each, plus/minus the respective standard deviation). There is no significant difference between duplicating genes and adding random genes - however, our experiments might be too simple for duplication to be useful. On the other hand in nature there is not much de novo generation of genetic material, so duplication might just be a more straightforward way of getting raw material

Reference / Bibtex
Knabe, J. F., Nehaniv, C. L. and Schilstra, M. J. Regulation of Gene Regulation - Smooth Binding with Dynamic Affinity affects Evolvability. In IEEE Congress on Evolutionary Computation (CEC 2008). Proc WCCI 2008, pages 890-896, IEEE Press, 2008. 

  @inProceedings{CECdynamicAffinities,
    author = {Johannes F. Knabe and Chrystopher L. Nehaniv and Maria J. Schilstra},
    title = {Regulation of Gene Regulation - Smooth Binding with Dynamic Affinity affects Evolvability},
    url = {http://panmental.de/CECdynAff},
    booktitle = {IEEE Congress on Evolutionary Computation (CEC 2008). Proc WCCI 2008},
    publisher = {IEEE Press},
    pages = {890--896},
    year = {2008}
  }


2007-12-06; University of Hertfordshire, Hatfield AL10 9AB, UK; Version 1.4 (added "adding genes by duplication versus adding random ones" table)
You might want to visit Johannes Knabe's homepage and other publications as well.