QuantumDNA: A Python Package for Analyzing Quantum Charge Dynamics in DNA and Exploring Its Biological Relevance
- URL: http://arxiv.org/abs/2502.06883v1
- Date: Sat, 08 Feb 2025 16:48:16 GMT
- Title: QuantumDNA: A Python Package for Analyzing Quantum Charge Dynamics in DNA and Exploring Its Biological Relevance
- Authors: Dennis Herb, Marco Trenti, Marilena Mantela, Constantinos Simserides, Joachim Ankerhold, Mirko Rossini,
- Abstract summary: The study of DNA charge dynamics is a highly interdisciplinary field that bridges physics, chemistry, biology, and medicine.<n>We present QuantumDNA, an open-source Python package for simulating DNA charge transfer (CT) and excited states using quantum-physical methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The study of DNA charge dynamics is a highly interdisciplinary field that bridges physics, chemistry, biology, and medicine, and plays a critical role in processes such as DNA damage detection, protein-DNA interactions, and DNA-based nanotechnology. However, despite significant advances in each of these areas, knowledge often remains inaccessible to other scientific communities, limiting the broader impact of advances across disciplines. To bridge this gap, we present QuantumDNA, an open-source Python package for simulating DNA charge transfer (CT) and excited states using quantum-physical methods. QuantumDNA combines an efficient Linear Combination of Atomic Orbitals (LCAO) approach with tight-binding (TB) models, incorporating open quantum systems techniques to account for environmental effects. This approach allows rapid yet accurate analysis of large DNA ensembles, enabling statistical studies of genetic and epigenetic phenomena. To ensure accessibility, the package features a graphical user interface (GUI), making it suitable for researchers across disciplines.
Related papers
- HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model [70.69095062674944]
We propose HybriDNA, a decoder-only DNA language model that incorporates a hybrid Transformer-Mamba2 architecture.
This hybrid design enables HybriDNA to efficiently process DNA sequences up to 131kb in length with single-nucleotide resolution.
HybriDNA achieves state-of-the-art performance across 33 DNA understanding datasets curated from the BEND, GUE, and LRB benchmarks.
arXiv Detail & Related papers (2025-02-15T14:23:43Z) - Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification [53.488387420073536]
Life-Code is a comprehensive framework that spans different biological functions.<n>Life-Code achieves state-of-the-art performance on various tasks across three omics.
arXiv Detail & Related papers (2025-02-11T06:53:59Z) - GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
We present a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>The model adheres to the central dogma of molecular biology, accurately generating protein-coding sequences.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of promoter sequences.
arXiv Detail & Related papers (2025-02-11T05:39:49Z) - Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models [51.316001071698224]
We introduce Biology-Instructions, the first large-scale multi-omics biological sequences-related instruction-tuning dataset.<n>This dataset can bridge the gap between large language models (LLMs) and complex biological sequences-related tasks.<n>We also develop a strong baseline called ChatMultiOmics with a novel three-stage training pipeline.
arXiv Detail & Related papers (2024-12-26T12:12:23Z) - Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA [44.630039477717624]
MxDNA is a novel framework where the model autonomously learns an effective DNA tokenization strategy through gradient decent.<n>We show that MxDNA learns unique tokenization strategy distinct to those of previous methods and captures genomic functionalities at a token level during self-supervised pretraining.
arXiv Detail & Related papers (2024-12-18T10:55:43Z) - Effect of environmental noise on charge diffusion in DNA: Towards modeling its potential epigenetic impact in live processes [0.0]
We analyze quantum diffusion of single charges along DNA-inspired tight-binding lattices in presence of different sources of intrinsic and environmental fluctuations.
Our results may trigger further experimental activities aiming at investigating charge mobility in DNA both in the native in-vivo context as well as on artificial platforms.
arXiv Detail & Related papers (2024-07-19T12:32:17Z) - Ultrafast excitonic dynamics in DNA: Bridging correlated quantum
dynamics and sequence dependence [0.0]
We show that a tight-binding approach allows to correlate relaxation properties, average charge separation, and dipole moments to a large ensemble of DNA sequences.
By systematically screening the impact of electron-hole interaction (Coulomb forces), we verify that these correlations are relatively robust against finite-size variations of the interaction parameter.
arXiv Detail & Related papers (2024-02-23T18:24:58Z) - Quantum gate algorithm for reference-guided DNA sequence alignment [0.0]
We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing.
The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors.
arXiv Detail & Related papers (2023-08-08T18:41:24Z) - Efficient Automation of Neural Network Design: A Survey on
Differentiable Neural Architecture Search [70.31239620427526]
Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures.
This rise is mainly due to the popularity of DARTS, one of the first major DNAS methods.
In this comprehensive survey, we focus specifically on DNAS and review recent approaches in this field.
arXiv Detail & Related papers (2023-04-11T13:15:29Z) - Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and
Deep Learning [49.3231734733112]
We show a modular and holistic approach that combines Deep Neural Networks (DNN) trained on simulated data, Product (TP) based Error-Correcting Codes (ECC) and a safety margin into a single coherent pipeline.
Our work improves upon the current leading solutions by up to x3200 increase in speed, 40% improvement in accuracy, and offers a code rate of 1.6 bits per base in a high noise regime.
arXiv Detail & Related papers (2021-08-31T18:21:20Z) - Standard Model Physics and the Digital Quantum Revolution: Thoughts
about the Interface [68.8204255655161]
Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress.
From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
arXiv Detail & Related papers (2021-07-10T06:12:06Z) - Efficient approximation of DNA hybridisation using deep learning [0.0]
We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation.
We introduce a synthetic hybridisation dataset of over 2.5 million data points, enabling the use of a wide range of machine learning algorithms.
arXiv Detail & Related papers (2021-02-19T19:23:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.