Name
Abstract | ||
|---|---|---|
With the rise of systems biology, the systematic analysis and construction of behavioral mechanisms in both natural and artificial biochemical networks has become a vital part of un- derstanding and predicting the inner workings of intracellu- lar signaling networks. As a modeling platform, artificial chemistries are commonly adopted to study and construct artificial reaction network motifs that exhibit complex com- putational behaviors. Here, we present a genetic algorithm to evolve networks that can compute elementary mathemat- ical functions by transforming initial input molecules into the steady state concentrations of output molecules. More specifically, the proposed algorithm implicitly guarantees mass conservation through an atom based description of the molecules and reaction networks. We discuss the adopted ap- proach for the artificial evolution of these chemical networks, evolve networks to compute the square root function. Finally, we provide an extensive deterministic and stochastic analysis of a core square root network motif present in these result- ing networks, confirming that the motif is indeed capable of computing the square root function. |
Year | Venue | Keywords |
|---|---|---|
2008 | ALIFE | steady state,mass conservation,genetic algorithm,artificial evolution,system biology,stochastic analysis,network motif |
Field | DocType | Citations |
Network motif,Function (mathematics),Evolutionary algorithm,Computer science,Systems biology,Stochastic process,Artificial intelligence,Square root,Conservation of mass,Machine learning,Genetic algorithm | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anthony M. L. Liekens | 1 | 19 | 4.91 |
| h m m ten eikelder | 2 | 1 | 0.72 |
| m n steijaert | 3 | 6 | 1.16 |
| Peter A. J. Hilbers | 4 | 100 | 12.73 |