Quantitative metrics for bio-modeling algorithm selection.
In this paper, we report our efforts in developing guidelines that are capable of helping researchers to select algorithms in systems biology modeling. We propose a set of metrics based on discrete observable units in terms of key bio-modeling considerations. We accomplish this by (i) reviewing classical metric definitions, (ii) implementing widely used modeling algorithms on a specific case study, and (iii) testing metrics that are a hybrid of classical metrics and key bio-modeling considerations. The modeling algorithms implemented are Michaelis-Menten kinetics, generalized mass action, flux balance analysis, and metabolic control analysis. This work extends our previous work in developing qualitative guidelines to select bio-modeling algorithms. Our results impact systems biology modeling specifically by increasing the level of confidence for users to select bio-modeling algorithms by using quantitative metrics appropriately.
Kaddi
Chanchala
C
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, 30332 USA. gtg538v@mail.gatech.edu
Quo
Chang Feng
CF
Wang
May D
MD
D000465:Algorithms; D000818:Animals; D001671:Biochemistry / Q000379:methods; D002384:Catalysis; D003198:Computer Simulation; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D049490:Systems Biology