Identifying Biological Feedback Loops Using a Nonparametric Identification Method

摘要

Feedback circuits are crucial dynamic motifs which occur in many intra-cellular and inter-cellular regulatory networks. In this paper, an effective nonparametric identification method, Non-causal Impulse Response Component Method (NIRCM) is developed to identify feedback loops embedded in biological neural networks, which uses only timeseries experimental data. The NIRCM, based on correlation identification and spectral factor analysis, provides a non-causal component criterion for the identification of feedback loops. Significant non-causal components of the impulse response sequences observed in the negative time axis imply an existence of feedback loop. The proposed identification method was applied to several 2-node SRM (Spike Response Model) networks. For these synthetic models, NIRCM correctly implies the existence of feedback loops and shows their effectiveness of feedback loop identifications.