A tool for automated inference in rule-based biological models
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/106447 |
| _version_ | 1811069490142642176 |
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| author | Voss, Chelsea (Chelsea S.) |
| author2 | Armando Solar-Lezama. |
| author_facet | Armando Solar-Lezama. Voss, Chelsea (Chelsea S.) |
| author_sort | Voss, Chelsea (Chelsea S.) |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. |
| first_indexed | 2024-09-23T08:11:16Z |
| format | Thesis |
| id | mit-1721.1/106447 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:11:16Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1064472019-04-09T17:13:15Z A tool for automated inference in rule-based biological models Voss, Chelsea (Chelsea S.) Armando Solar-Lezama. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Chelsea Voss. M. Eng. 2017-01-12T18:33:52Z 2017-01-12T18:33:52Z 2016 2016 Thesis http://hdl.handle.net/1721.1/106447 967663582 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 46 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Voss, Chelsea (Chelsea S.) A tool for automated inference in rule-based biological models |
| title | A tool for automated inference in rule-based biological models |
| title_full | A tool for automated inference in rule-based biological models |
| title_fullStr | A tool for automated inference in rule-based biological models |
| title_full_unstemmed | A tool for automated inference in rule-based biological models |
| title_short | A tool for automated inference in rule-based biological models |
| title_sort | tool for automated inference in rule based biological models |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/106447 |
| work_keys_str_mv | AT vosschelseachelseas atoolforautomatedinferenceinrulebasedbiologicalmodels AT vosschelseachelseas toolforautomatedinferenceinrulebasedbiologicalmodels |