A tool for automated inference in rule-based biological models
Name
967663582-MIT.pdf
Description
Full printable version
Size
3.16 MB
Format
Adobe PDF
Checksum (MD5)
20d092297a2e5bb64775591161d02ec3
Author(s)
Voss, Chelsea (Chelsea S.)
Advisor(s)
Armando Solar-Lezama.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Rule-based biological models help researchers investigate systems such as cellular signalling pathways. Although these models are generally programmed by hand, some research efforts aim to program them automatically using biological facts extracted from papers via natural language processing. However, NLP facts cannot always be directly converted into mechanistic reaction rules for a rule-based model. Thus, there is a need for tools that can convert biological facts into mechanistic rules in a logically sound way. We construct such a tool specifically for Kappa, a model programming language, by implementing Iota, a logic language for Kappa models. Our tool can translate biological facts into Iota predicates, check predicates for satisfiability, and find models that satisfy predicates. We test our system against realistic use cases, and show that it can construct rule-based mechanistic models that are sound with respect to the semantics of the biological facts from which they were constructed.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 45-46).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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