• Aucun résultat trouvé

Gene Regulatory Network Evolution Through Augmenting Topologies

N/A
N/A
Protected

Academic year: 2021

Partager "Gene Regulatory Network Evolution Through Augmenting Topologies"

Copied!
17
0
0

Texte intégral

Figure

Fig. 1. Functioning of a GRN.
Fig. 2. Aligning crossover tries to align the protein before crossing the genomes. The input and output proteins are aligned according to the sensors and actuators they are linked to
Fig. 4. Comparison of the median GRN sizes trained with GRNEAT (in red), GA (in green), and EP (in blue) and GRNEAT components (dashed and dotted lines) on the doubling frequency problem.
Fig. 7. Comparison of the size of the GRNs generated by GRNEAT, GA, and EP on the low-pass filter problem.
+5

Références

Documents relatifs

Therefore we have a décision method for the inclusion of pattern languages in only one of the four cases. Clearly, this does not imply that the other problems are undecidable.

Our findings showed that using a list of common entities and a simple, yet robust set of distributional similarity measures was enough to describe and assess the degree of

In a population of N organisms,having a mean number of genes of M and whose evolution is simulated.. T o ompute the anity of a protein with a given binding site, we align the

Informational masking mainly at the phonological level (Reverse – Noise), that is when the babble contained only partial phonetic information, activated the left primary

The resulting translated keys were used as queries and run against the target language data with Lemur retrieval system.. Date of publication and size are used to further

L’objet de notre essai a été d’étudier l’évolution du pH ruminal, sanguin et urinaire durant le développement d’une acidose latente provoquée sur moutons et lors de

Macroscopic scale: when stochastic fluctuations are neglected, the quantities of molecu- les are viewed as derivable functions of time, solution of a system of ODE’s, called

In other words, for each calculation, we have a unique difference matrix, allowing to compare result of variability for columns (technical coefficients) and for rows