DNA Strand Displacement (DSD) as a hardware substrate for digital, analog and machine learning systems

• Autor(a) principal: Renan Albuquerque Marks
• Data de Publicação: 2023
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: http://hdl.handle.net/1843/62057; https://orcid.org/0000-0001-8239-8320
• topic: Nanocomputação; Computação Molecular; Circuitos em DNA; Computação – Teses; Nanocomputação – Teses; Computação em DNA – Teses

There was a time when the evolution of computer systems technology was fast and steady. The development rate of new miniaturization techniques was high, meaning that smaller and more efficient fabrication methods became available and employed to develop new integrated circuits after a few months. Although this was true in the past decades, this is no longer true due to physics limits. Each new generation of devices faces transistor miniaturization challenges mainly because of power consumption and heat dissipation due to the nature of quantum physics at this scale. As artificial intelligence becomes more prevalent due to the need to process an enormous amount of data in parallel and fast, the hardware will be a bottleneck. Several solutions to this hardware problem are being proposed and tested. Some of them involve trying new paradigms to build computer systems beyond silicon, such as using light or organic molecules as a substrate to carry out the computation. One of the alternatives is using DNA as ``wet'' hardware to complement the traditional silicon computers, accelerating some specific computations in vitro or in vivo. DNA's massively parallel processing capabilities and low power consumption make it the perfect alternative to implementing new medical applications, ranging from diagnostic of diseases to new drugs to treat various illnesses. This thesis proposes that DNA Strand Displacement (DSD) technology can be a hardware substrate for developing digital, analog, and machine-learning modular molecular circuits. Thus, to achieve this goal, this thesis will present the contribution of new Computer Aided Design (CAD) tools developed that support the modularization, composition, design, simulation, and validation of molecular circuits based on DSD.