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Compilation and Inference with Chemical Reaction Networks


Poole, William (2022) Compilation and Inference with Chemical Reaction Networks. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/x3qc-je74.


The successful advancement and deployment of technologies in the field of synthetic biology will require sophisticated computational infrastructure coupled with new theoretical ideas in order to more effectively engineer and reverse engineer biochemical networks. This thesis argues that the field of machine learning can inform the development of these underlying principles and techniques. First, software for compiling diverse chemical reaction network models of biological circuits from simple specifications is described. Second, three chemical reaction network implementations of a powerful machine learning model called a Boltzmann machine are analyzed and compared. Third, the class of detailed balanced chemical reaction networks are proven to be capable of probabilistic inference and, when coupled to a driven chemical system, autonomous learning. Finally, the use of machine learning to interpret and understand biological systems is explored in an experimental case study modeling E. coli cell extract metabolism.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Chemical Reaction Networks, Machine Learning, Boltzmann Machines, Synthetic Biology, Systems Biology, Cell Extract
Degree Grantor:California Institute of Technology
Division:Biology and Biological Engineering
Major Option:Computation and Neural Systems
Awards:NSF Graduate Research Fellowship (GRFP), NSF Graduate Research Opportunities Worldwide (GROW)
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Winfree, Erik (advisor)
  • Murray, Richard M. (co-advisor)
Thesis Committee:
  • Thomson, Matthew (chair)
  • Winfree, Erik
  • Murray, Richard M.
  • Phillips, Robert B.
Defense Date:24 August 2021
Funding AgencyGrant Number
NSF Graduate Research Fellowship2017246618
Record Number:CaltechTHESIS:11102021-210013472
Persistent URL:
Related URLs:
URLURL TypeDescription VideoThesis Defense 2: BioCRNpyler adapted from this BioRxiv manuscript 3: Chemical Boltzmann Machines adapted from these proceedings
Poole, William0000-0002-2958-6776
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14424
Deposited By: William Poole
Deposited On:22 Nov 2021 18:27
Last Modified:09 Dec 2021 20:50

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