To show the differences between our approach of Boolean model creation and classical Boolean models we used a small example network.
Throughout this work, we will use a small example network of epidermal growth factor (EGF) and neuregulin-1 (NRG1; also known as heregulin) signaling that was manually derived from a large-scale network describing signaling through ErbB receptors [ 20].
However, there have been some difficulties in taking the model from examples using a small number of particles to more elaborate and better resolved cases.
For example, using a small, 80-core cluster running OutputCoverage (our tool to generate coverage totals per kilobase region), the use of a combiner means the difference between three minutes and nearly 90 minutes of analysis over the same BAM file (see Table 3), utilizing the same job across the cluster.
For example, entomologists are using a small population of burying beetles on Block Island, R.I., in efforts to re-establish the species, whose range was once from Maine to Texas.
For small motion sequences, for example, Akiyo, using a smaller interval angle has better prediction quality.
(Group C) With regard to diet, there was some appreciation of the need for portion size control, for example, by using a smaller plate.
We use this framework for a small example where components make mutual assumptions about each other's output.
The ideas are illustrated by a small example using UML-like class diagrams for the architecture and Petri net like networks for the component behavior.
