PhD ThesisOver the last decade pharmacology has been developing computational
methods to enhance drug development and testing. A computational
method called network pharmacology uses graph analysis
tools to determine protein target sets that can lead on better targeted
drugs for diseases as Cancer. One promising area of network-based
pharmacology is the detection of protein groups that can produce
better e ects if they are targeted together by drugs. However, the
e cient prediction of such protein combinations is still a bottleneck
in the area of computational biology.
The computational burden of the algorithms used by such protein
prediction strategies to characterise the importance of such proteins
consists an additional challenge for the eld of network pharmacology.
Such computationally expensive graph algorithms as the all pairs
shortest path (APSP) computation can a ect the overall drug discovery
process as needed network analysis results cannot be given on
time. An ideal solution for these highly intensive computations could
be the use of super-computing. However, graph algorithms have datadriven
computation dictated by the structure of the graph and this
can lead to low compute capacity utilisation with execution times
dominated by memory latency.
Therefore, this thesis seeks optimised solutions for the real-world
graph problems of critical node detection and e ectiveness characterisation
emerged from the collaboration with a pioneer company in the
eld of network pharmacology as part of a Knowledge Transfer Partnership
(KTP) / Secondment (KTS). In particular, we examine how
genetic algorithms could bene t the prediction of protein complexes
where their removal could produce a more e ective 'druggable' impact.
Furthermore, we investigate how the problem of all pairs shortest
path (APSP) computation can be bene ted by the use of emerging
parallel hardware architectures as GPU- and FPGA- desktop-based
accelerators.
In particular, we address the problem of critical node detection with
the development of a heuristic search method. It is based on a genetic
algorithm that computes optimised node combinations where their removal
causes greater impact than common impact analysis strategies.
Furthermore, we design a general pattern for parallel network analysis
on multi-core architectures that considers graph's embedded properties.
It is a divide and conquer approach that decomposes a graph
into smaller subgraphs based on its strongly connected components
and computes the all pairs shortest paths concurrently on GPU. Furthermore,
we use linear algebra to design an APSP approach based
on the BFS algorithm. We use algebraic expressions to transform the
problem of path computation to multiple independent matrix-vector
multiplications that are executed concurrently on FPGA. Finally, we
analyse how the optimised solutions of perturbation analysis and parallel
graph processing provided in this thesis will impact the drug
discovery process.This research was part of a Knowledge Transfer Partnership (KTP)
and Knowledge Transfer Secondment (KTS) between e-therapeutics
PLC and Newcastle University. It was supported as a collaborative
project by e-therapeutics PLC and Technology Strategy boar