Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

Abstract

BackgroundThe development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times.Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies.ResultsThe performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network.The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used.ConclusionsThe new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1452-4) contains supplementary material, which is available to authorized users.

Challenges in the calibration of large-scale ordinary differential equation models

“…Leaving aside the even more complicated task of network inference, parameter optimization and uncertainty analysis are currently the key challenges for large-scale models, for which no satisfactory approaches exist. Due to the high computation times, toolboxes suitable for computing clusters are necessary and have recently been developed (Penas et al, 2017;Schmiester et al, 2019). Moreover, new approaches have to be explored, such as transferring the concept of mini batching from the field of deep learning to optimization (Goodfellow et al, 2016) or MCMC sampling (Seita et al, 2017) of dynamic models, and must be adapted to ODE models.…”

Scatter Search Applied to the Inference of a Development Gene Network

“…As was suggested in [26], (parallel) genetic algorithms are known to be good exploratory search methods as well. Moreover, with respect to our implementation of scatter search, recently a more advanced version of scatter search has become available, called saCeSS (self-adaptive cooperative enhanced scatter search) [51]. It uses multiple instances of scatter search set at different levels of exploration and exploitation, multiple local search methods, and utilizes the small-scale parallelism of current-day workstations.…”

Multimethod optimization in the cloud: A case‐study in systems biology modelling

“…Algorithm 2 shows a schematic simple pseudocode of the proposed saCMM method. By now, two metaheuristics are performed by the islands, the Differential Evolution (DE), implemented with the enhancements described in, and the enhanced Scatter Search (eSS), using the implementation outlined in . Nevertheless, as already pointed before, this paper aims to be a proof‐of‐concept, and the saCMM could be easily extended including more metaheuristics.…”