Background: Reconstructing gene regulatory networks (GRNs) from expression data plays an important role in\nunderstanding the fundamental cellular processes and revealing the underlying relations among genes. Although\nmany algorithms have been proposed to reconstruct GRNs, more rapid and efficient methods which can handle\nlarge-scale problems still need to be developed. The process of reconstructing GRNs can be formulated as an\noptimization problem, which is actually reconstructing GRNs from time series data, and the reconstructed GRNs\nhave good ability to simulate the observed time series. This is a typical big optimization problem, since the number\nof variables needs to be optimized increases quadratically with the scale of GRNs, resulting an exponential increase\nin the number of candidate solutions. Thus, there is a legitimate need to devise methods capable of automatically\nreconstructing large-scale GRNs.\nResults: In this paper, we use fuzzy cognitive maps (FCMs) to model GRNs, in which each node of FCMs represent\na single gene. However, most of the current training algorithms for FCMs are only able to train FCMs with dozens\nof nodes. Here, a new evolutionary algorithm is proposed to train FCMs, which combines a dynamical multi-agent\ngenetic algorithm (dMAGA) with the decomposition-based model, and termed as dMAGA-FCMD, which is able to\ndeal with large-scale FCMs with up to 500 nodes. Both large-scale synthetic FCMs and the benchmark DREAM4 for\nreconstructing biological GRNs are used in the experiments to validate the performance of dMAGA-FCMD.\nConclusions: The dMAGA-FCMD is compared with the other four algorithms which are all state-of-the-art FCM\ntraining algorithms, and the results show that the dMAGA-FCMD performs the best. In addition, the experimental\nresults on FCMs with 500 nodes and DREAM4 project demonstrate that dMAGA-FCMD is capable of effectively and\ncomputationally efficiently training large-scale FCMs and GRNs.
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