Motion Planning In Metabolic Pathways Using Probabilistic Roadmap and A* Algorithms

Motion planning and navigation strategies have found useful
applications in many areas such as biological networks. This
work applies motion planning algorithms to search for
biochemically relevant pathways in metabolic pathways. The
choice pathways are represented as graphs with its compounds
as nodes (vertices), and the possible reactions between the
compounds as the edges. The probabilistic roadmap (PRM)
algorithm is then used to construct the roadmap (graph) using
its local planner function while modelling a group of pool
metabolites as obstacles. A* search algorithm queries the
roadmap to get the most relevant (cost effective) path using the
thermodynamic feasibility of the reactions as the weighting
scheme. For ease of testing and evaluation, the system was
implemented using python programming language. Choice
pathways from KEGG database in KGML format (i.e. xml
format for KEGG) were used to test the system, which
revealed that the results were consistent with other pathway
search tools with reasonable performance and can be adopted
for pathfinding problems. Improvements in several areas can
however better optimise the system, example include the
aspect of weighting schemes utilized.