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Updated: Aug 5, 2026
Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors
Published on: September 13, 2018
Quantum computing-assisted validation of a conserved macrophage suppression module shared by ASFV and PEDV
Jongwon Byun1,2, Hakjin Kim2,3, Changhee Chandler Pyo4
1Quantum AI Bio Research Laboratory (KJQI-JQL), AI-Bio Solution Team, Futuristic Animal Resource and Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Cheongju, Chungbuk, 28116, Republic of Korea.
African swine fever virus (ASFV) and porcine epidemic diarrhea virus (PEDV) share a common module that suppresses pig immune cells. Quantum computing validated this network, showing its potential for biological research.
Area of Science:
- Virology and Immunology
- Computational Biology and Quantum Computing
Background:
- African swine fever virus (ASFV) and porcine epidemic diarrhea virus (PEDV) are significant swine pathogens.
- Both viruses suppress macrophage-mediated immune responses in pigs, despite differing in viral biology and cellular tropism.
Purpose of the Study:
- To identify a conserved macrophage suppression module shared by ASFV and PEDV.
- To evaluate quantum computing as an independent framework for validating biological networks.
Main Methods:
- Integrated analysis of public gene expression datasets identified shared downregulated genes between ASFV and PEDV infections.
- A 20-gene core network module was selected and formulated as a 20-qubit Quadratic Unconstrained Binary Optimization (QUBO) problem.
- Community detection and network validation were performed using the Quantum Approximate Optimization Algorithm (QAOA) on quantum simulators and hardware, compared against classical methods.
Main Results:
- A conserved macrophage suppression module shared by ASFV and PEDV was successfully identified.
- QAOA on a protein-protein interaction network reproduced the brute-force optimum, demonstrating successful network validation.
- Performance of QAOA varied with network density and circuit depth, highlighting the impact of network topology and NISQ hardware limitations.
Conclusions:
- The study reveals a conserved immune evasion strategy employed by ASFV and PEDV.
- Quantum computing offers a novel and independent framework for validating complex biological networks.
- Network topology is a critical factor influencing the performance of quantum algorithms on current quantum hardware.
