Accelerating Synthetic Biology Research with GPU-Accelerated ML Algorithms

EasyChair Preprint no. 13941

Abstract

Synthetic biology, a field that combines biology and engineering to design and construct new biological parts and systems, stands at the forefront of modern scientific innovation. However, the complexity and scale of biological data pose significant challenges, necessitating advanced computational methods for effective analysis and synthesis. This paper explores the transformative potential of GPU-accelerated machine learning (ML) algorithms in accelerating synthetic biology research. By leveraging the parallel processing power of GPUs, we can significantly enhance the performance and scalability of ML models used in various synthetic biology applications, including gene editing, metabolic pathway optimization, and protein design. We discuss the integration of GPU-accelerated ML in the design-build-test-learn (DBTL) cycle, demonstrating how it can streamline experimental workflows, reduce computational bottlenecks, and enable real-time analysis and decision-making. Case studies highlighting the successful application of GPU-accelerated ML in synthetic biology projects underscore the practical benefits and future prospects of this approach. This paper aims to provide a comprehensive overview of the intersection between GPU acceleration and synthetic biology, offering insights into how these advanced computational techniques can drive innovation and expedite research in this rapidly evolving field.

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational genomics, Computational Proteomics, Deep learning in bioinformatics, Genomic data processing, GPU-accelerated machine learning, GPU-based bioinformatics, High Performance Computing, Machine learning in computational biology

@Booklet{EasyChair:13941,
  author = {Abi Cit},
  title = {Accelerating Synthetic Biology Research with GPU-Accelerated ML Algorithms},
  howpublished = {EasyChair Preprint no. 13941},

  year = {EasyChair, 2024}}