Adaptive Co-Evolutionary Metaheuristic for Sparse Gene Regulatory Network Inference under Noisy RNA-Seq Conditions

Authors

  • Ahmed El-Sharif * Department of Computer Science, Faculty of Computer and Artificial Intelligence, Cairo University, Giza, Egypt.
  • Lina Al-Haddad Department of Computer Science, King Abdullah II School of Information Technology, University of Jordan, Amman, Jordan.
  • Wei Zhang Department of Computer Science and Engineering, Southeast University, Nanjing, China.

https://doi.org/10.48313/maa.v1i4.107

Abstract

Accurate inference of Gene Regulatory Networks (GRNs) from transcriptomic data is central to understanding stress-response mechanisms in agricultural crop species, yet the overdispersed, zero-inflated, and batch-effect-laden nature of RNA-Seq count data poses formidable challenges for existing reconstruction methods. Information-theoretic approaches such as Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) and Context Likelihood of Relatedness (CLR) fail to recover edge directionality, regression-based methods including GENIE3 and Trustful Inference of Gene REgulation using Stability Selection (TIGRESS) incur prohibitive computational costs at genome scale, and Deep-Learning (DL) frameworks require curated labeled training data that are rarely available for non-model crop species. In this paper, we present Adaptive Co-Evolutionary metaheuristic for Gene Regulatory Network inference (ACE-GRN), a novel optimization framework that simultaneously evolves two cooperating populations one encoding network topology as binary adjacency matrices and another encoding edge weights as continuous parameter vectors to reconstruct sparse, directed GRNs directly from noisy RNA-Seq counts. ACE-GRN integrates a Negative Binomial (NB) likelihood model tailored to RNA-Seq overdispersion, a weighted L1 sparsity penalty to enforce biologically realistic network density, a Directed Acyclic Graph (DAG) cycle penalty, and an adaptive parameter control mechanism inspired by reinforcement learning that dynamically balances exploration and exploitation across evolutionary generations. Comprehensive evaluation on DREAM4 and DREAM5 benchmark networks demonstrates that ACE-GRN achieves mean Area Under the Receiver Operating Characteristic curve (AUROC) improvements of 8–12% and mean Area Under the Precision-Recall curve (AUPR) improvements of 14–19% over the best-performing baseline methods. On real agricultural datasets comprising rice (Oryza sativa) drought-stress and wheat (Triticum aestivum) heat-stress transcriptomes, ACE-GRN successfully recovers experimentally validated regulatory modules involving DREB, NAC, and WRKY transcription factor families, while maintaining robust performance under increasing noise levels (retaining approximately 82% AUROC at coefficient of variation 0.7). These results establish ACE-GRN as a powerful and biologically interpretable tool for GRN inference in agricultural genomics.

Keywords:

Metaheuristic optimization, Co-evolutionary algorithm, RNA-Seq, parse network inference, Agricultural genomics

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Published

2024-12-23

Issue

Vol. 1 No. 4 (2024): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

El-Sharif, A. ., Al-Haddad, L. ., & Zhang, W. . (2024). Adaptive Co-Evolutionary Metaheuristic for Sparse Gene Regulatory Network Inference under Noisy RNA-Seq Conditions. Metaheuristic Algorithms With Applications, 1(4), 392-416. https://doi.org/10.48313/maa.v1i4.107

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