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IEEE Transactions on Molecular Biological and Multi-Scale Communications

Quantum Computation by Biological Systems

Mario D’Acunto

Published May 1, 2023• 7 citations
Publisher

Abstract

Most problems and limitations associated with classical computing are eliminated in quantum computing. Despite the current methods of quantum computing which have to deal with the non-secondary problem of decoherence induced by the coupling of the system with the environment, biological systems use quantum physics at high temperature and in highly noise environments. As a consequence, taking inspiration from how DNA, enzymes and other biomolecules exploit quantum properties could help us find methods of quantum computation that could bypass the problems encountered in non-biological systems. In this paper, we shortly review bio-inspired qubits systems and how endonuclease restriction enzymes exploit quantum physics to solve searching problem, i.e., the identification of small sequences (4-6 nucleotides bases) in DNA (approximately 1 million nucleotides bases) complexes and its implication in developing universal quantum gates. The possible implications for quantum computation of this restriction enzyme feature is then briefly described

DOI
10.1109/tmbmc.2023.3272230

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• Development of the quantum algorithm 'QuBio.py' for searching and analyzing genetic information. • Successful detection of a 50-nucleotide target sequence within a 3,022-nucleotide database using a 127-qubit simulator on the IBM quantum platform. • Securing flexibility that allows up to a 30% mismatch rate... Heralding a new horizon for synthetic biology and new drug development. [Quantum Biology Society] A "quantum computing"-based gene search algorithm has emerged, capable of analyzing exponentially increasing biological data at ultra-high speeds, surpassing the limitations of conventional computers. A study titled "GENETIC ENGINEERING THROUGH QUANTUM CIRCUITS: CONSTRUCTION OF CODES AND ANALYSIS OF GENETIC ELEMENTS BIOBLOQU" was recently published on bioRxiv, a preprint repository for biological sciences. The research team, including Patrícia Verdugo Pascoal, presented an innovative model that explores and manipulates genetic information utilizing quantum mechanics. ■ Translating the Four DNA Bases into Quantum Language... The Evolution of 'Grover's Algorithm' Modern biology generates approximately 1 Gb of sequencing data per hour, but analyzing this data using classical computing methods faces significant limitations. Existing database search methods require computation time proportional to the data size (O(N)), but the quantum-based 'Grover's algorithm' applied by the researchers can significantly reduce this to the O(√N}) level. In particular, a key feature of this study is that it adapted the algorithm to process the four nucleotides (A, T, G, C) that make up DNA, rather than simple binary symbols, thereby enabling it to operate efficiently on more complex datasets. ■ Detecting the Cas9 Target Sequence with 127 Qubits... Creation of Synthetic Genome (BioBloQu) Based on the 127-qubit simulator of the IBM cloud platform, the research team developed its own quantum algorithm code named 'QuBio.py'. Through this algorithm, they succeeded in finding a specific 50-nucleotide target DNA sequence within a genetic database consisting of up to 3,022 nucleotides. A conserved functional domain of the Cas9-like nucleotide sequence found in Brazilian biomes was used as the target sequence. Notably, this quantum algorithm permits a mismatch rate of up to 30% during the sequence comparison process. This enables flexible and rapid analysis in situations where sequence variations are expected, such as homologous gene analysis or evolutionary studies. Furthermore, based on these search results, the team succeeded in constructing a synthetic structure called a 'Quantum Biological Block (BioBloQu)' inside minimal cells (JCVI-syn3.0). The researchers stated that this algorithm "constitutes a unique starting point for developing a more sophisticated model for the manipulation of data". They also predicted that "the integration of quantum computing with artificial intelligence represents an attractive avenue... By optimizing genetic algorithms and facilitating real-time data analyses, quantum computing can revolutionize trait discovery processes in agriculture and biotechnology". This achievement is evaluated as an important example demonstrating how quantum computing can contribute to next-generation synthetic biology technologies that analyze large-scale genetic big data and design genomes from the bottom up.
Computational methods are the most effective tools we have besides scientific experiments to explore the properties of complex biological systems. Progress is slowing because digital silicon computers have reached their limits in terms of speed. Other types of computation using radically different architectures, including neuromorphic and quantum, promise breakthroughs in both speed and efficiency. Quantum computing exploits the coherence and superposition properties of quantum systems to explore many possible computational paths in parallel. This provides a fundamentally more efficient route to solving some types of computational problems, including several of relevance to biological simulations. In particular, optimization problems, both convex and non-convex, feature in many biological models, including protein folding and molecular dynamics. Early quantum computers will be small, reminiscent of the early days of digital silicon computing. Understanding how to exploit the first generation of quantum hardware is crucial for making progress in both biological simulation and the development of the next generations of quantum computers. This review outlines the current state-of-the-art and future prospects for quantum computing, and provides some indications of how and where to apply it to speed up bottlenecks in biological simulation.
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.
• Developed quantum codes based on Grover's algorithm using an IBM 127-qubit quantum simulator for large-scale genetic sequence analysis and optimization. • Successfully searched a 3,022-nucleotide database for a 50-nucleotide target sequence at exponential speeds with high flexibility (allowing up to a 30% mismatch rate). • Successfully constructed a synthetic structure named "Quantum Biological Block (BioBloQu)," which combines components such as a promoter and an enzyme, within a minimal JCVI-syn3.0 cell. [Quantum Biology Society] To process the exponentially growing vast amounts of biological data, research results applying quantum algorithms to genomics in earnest have been released for the first time. Recently, a paper titled "GENETIC ENGINEERING THROUGH QUANTUM CIRCUITS: CONSTRUCTION OF CODES AND ANALYSIS OF GENETIC ELEMENTS BIOBLOQU" was published on bioRxiv, a preprint repository for biology. ■ Breaking Through the Limits of Genetic Analysis with a 127-Qubit Quantum Simulator To analyze and optimize large-scale genetic sequences, the research team newly developed two quantum codes applying Grover's algorithm based on IBM's 127-qubit quantum simulator. As a result of testing this quantum algorithm, they succeeded in finding a specific target sequence consisting of 50 nucleotides within a DNA database composed of up to 3,022 nucleotides. In particular, considering natural genetic variations, it demonstrated overwhelmingly fast and flexible search capabilities compared to existing classical computing exploration methods, even allowing a mismatch rate of up to 30%. ■ 'Quantum Biological Block (BioBloQu)' Planted Inside an Artificial Cell The team's achievements did not stop at simple quantum code development and sequence exploration. Based on these quantum computing-based search results, the researchers focused on the interior of 'JCVI-syn3.0', an artificial cell model that maintains life phenomena with only minimal genes. Here, they successfully constructed a synthetic structure named the 'Quantum Biological Block (BioBloQu)', composed of a promoter, a ribosome binding site (RBS), an enzyme, and a terminator. This achievement is highly significant in that it proved quantum computing technology is not merely at a theoretical level, but is a powerful tool capable of innovatively accelerating the speed of actual genetic exploration and manipulation.
Quantum biology is an emergent field that investigates quantum-mechanical phenomena, such as superposition, tunneling, and entanglement, in the context of data manipulation from living systems. The exploration and engineering of nucleotide sequences rely on quantum mechanical principles, particularly the use of qubit states for the development of quantum codes. Biological sequencing data is produced at about 1 Gb/h, but analysis lags due to complexity and the limitations of classical computing. Despite these challenges, quantum computing offers a potential tool for analyzing and assembling biological data. Here, we developed quantum codes for genetic engineering. The developed quantum computational framework identifies sequences of interest within a genomic database. It locates the left and right boundaries of the scar region in the JCVI-Syn3B genome and detects 20 nucleotides flanking each boundary. After confirming the left and right ends of the scar, a secondary computational routine performs the targeted insertion of the BioBloQu structure, composed of genetic elements, into the previously characterized scar region. Our algorithms constitute a unique starting point for advanced genetic data manipulation. This tool could accelerate the exploration of large volumes of genetic data and enable the developmental design and assembly of synthetic genomes enhanced by quantum computing. Further improvements in algorithms and codes, along with the expanded availability of devices, will accelerate the search for data for applied genetic research.