Leveraging quantum computing for dynamic analyses of logical networks in systems biology
Open accessAbstract
The dynamics of cellular mechanisms can be investigated through the analysis of networks. One of the simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase in nodes. We transfer this modeling approach to quantum computing and use the upcoming technique in the field to simulate the resulting networks. Leveraging logic modeling in quantum computing has many benefits, including complexity reduction and quantum algorithms for systems biology tasks. To showcase the applicability of our approach to systems biology tasks, we implemented a model of mammalian cortical development. Here, we applied a quantum algorithm to estimate the tendency of the model to reach particular stable conditions and further revert dynamics. Results from two actual quantum processing units and a noisy simulator are presented, and current technical challenges are discussed.
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Weidner et al. created a quantum circuit from a series of quantum gates using the development network of the mammalian cortical area to model gene regulatory networks.
“To perform modeling of gene regulatory networks, Weidner etal. have created a quantum circuit from a series of quantum gates using the development network of the mammalian cortical area.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [83]
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- Authors
- Weidner, F. M., Schwab, J. D., Wölk, S., Rupprecht, F., Ikonomi, N., Werle, S. D., Hoffmann, S., Kühl, M., & Kestler, H. A.
- Affiliations
- Universität Ulm (DE)
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) (DE)
- Corresponding author
- Kestler, Hans A.
- ORCID
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 4
- Issue
- 3
- Pages
- 100705
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC10028428
- OpenAlex
- W4323835315
- Cited by
- 21
- References
- 58
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 4 · 2023: 4
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- elib (German Aerospace Center) (submittedVersion)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (01ZX1708C)
- International Business Machines Corporation
- BMBF Berlin
- Deutsche Forschungsgemeinschaft (217328187, 450627322, 288342734, SFB 1074, SFB 1506)
- Ministerium fur Wirtschaft Arbeit und Wohnungsbau Baden-Wurttemberg
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Biological Computing
- Keywords
- quantum computing, Boolean networks, systems biology, quantum algorithms, stable states, computational complexity reduction
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