Abstract
Motivation Many calculations in computational biology necessitate a use of a probabilistic optimisation protocol to determine a set of parameters that capture the system at a desired state in the configurational space. Here, we developed a flexible optimisation engine in R that can be plugged to any, simple or complex, modelling initiative through a few lucid interfacing functions, to perform a seamless optimisation with rigorous parameter sampling.
Results Optimus features an acceptance ratio simulated annealing, acceptance ratio replica exchange, and adaptive thermoregulation, thus driving a Monte Carlo optimisation process in a flexible manner, through constrained acceptance frequency but unconstrained adaptive pseudo temperature regiments. We show the applicability of our R optimiser to a wide variety of problems spanning data analyses and computational biology tasks.
Availability and Implementation Optimus is written and implemented in R, and is freely available from the http://github.com/SahakyanLab/Optimus repository.
Contact aleksandr.sahakyan{at}imm.ox.ac.uk
Supplementary Information Supplementary information with more details, tutorials, and developer instructions is available.
The Optimus software logo depicting two gears (“Op”) that drag the system “s”, trapped in a “u” minimum, through a rough solution landscape into a more favourable solution with a deeper pseudo-energy minimum (“p”).
Competing Interest Statement
The authors have declared no competing interest.
