Machine learning inspired synthetic biology: neuromorphic computing in mammalian cells

Synthetic biologists seek to collect, refine, and repackage nature so that it's easier to design new and reliable biological systems, typically at the cellular or multicellular level. These redesigned systems are often referred to as "biological circuits," for their ability to perform operations on biomolecular signals, rather than electrical signals, and for their aim to behave as predictably and modularly as would integrated circuits in a computer. In natural and synthetic biological systems, the abstraction of these circuits' behaviors to digital computation is often appropriate, especially in decision-making settings wherein the output is selected to coordinate a discrete set of outcomes, e.g. developmental networks or disease-state classication circuits. However, there are challenges in engineering entire genetic systems that mimic digital logic.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Architecture

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Moorman, Andrew(Andrew Robert)

Advisor dc:contributor.advisor

• Ron Weiss and Skylar Tibbits.

• Architecture.
• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129864

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129864