One important aspect of computational systems biology
includes the identification and analysis of functional response
networks within large biochemical networks. These functional
response networks represent the response of a biological system
under a particular experimental condition which can be used to
pinpoint critical biological processes.
For this purpose, we have developed a novel algorithm to calculate
response networks as scored/weighted sub-graphs spanned by
k-shortest simple (loop free) paths. The k-shortest simple path
algorithm is based on a forward/backward chaining approach
synchronized between pairs of processors. The algorithm scales
linear with the number of processors used. The algorithm
implementation is using a Linux cluster platform, MPI lam
and mpiJava messaging as well as the Java language for the
application.
The algorithm is performed on a hybrid human network consisting
of 45,041 nodes and 438,567 interactions together with
gene expression information obtained from human cell-lines
infected by influenza virus. Its response networks show the early
innate immune response and virus triggered processes within
human epithelial cells. Especially under the imminent threat of
a pandemic caused by novel influenza strains, such as the current
H1N1 strain, these analyses are crucial for a comprehensive
understanding of molecular processes during early phases of
infection. Such a systems level understanding may aid in the
identification of therapeutic markers and in drug development
for diagnosis and finally prevention of a potentially dangerous
disease
