Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli
Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2011
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| Online Access: | http://hdl.handle.net/1721.1/66218 |
| _version_ | 1826188073588752384 |
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| author | Morris, Melody Kay Saez-Rodriguez, Julio Clarke, David C. Sorger, Peter K. Lauffenburger, Douglas A. |
| author2 | Massachusetts Institute of Technology. Cell Decision Process Center |
| author_facet | Massachusetts Institute of Technology. Cell Decision Process Center Morris, Melody Kay Saez-Rodriguez, Julio Clarke, David C. Sorger, Peter K. Lauffenburger, Douglas A. |
| author_sort | Morris, Melody Kay |
| collection | MIT |
| description | Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. |
| first_indexed | 2024-09-23T07:54:11Z |
| format | Article |
| id | mit-1721.1/66218 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:54:11Z |
| publishDate | 2011 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/662182022-09-30T00:53:37Z Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli Morris, Melody Kay Saez-Rodriguez, Julio Clarke, David C. Sorger, Peter K. Lauffenburger, Douglas A. Massachusetts Institute of Technology. Cell Decision Process Center Massachusetts Institute of Technology. Department of Biological Engineering Lauffenburger, Douglas A Morris, Melody Kay Saez-Rodriguez, Julio Clarke, David C. Sorger, Peter K. Lauffenburger, Douglas A. Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. National Institutes of Health (U.S.) (NIH grant P50-GM68762) National Institutes of Health (U.S.) (Grant U54-CA112967) United States. Dept. of Defense (Institute for Collaborative Biotechnologies) 2011-10-12T14:59:56Z 2011-10-12T14:59:56Z 2011-03 2010-09 Article http://purl.org/eprint/type/JournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/66218 Morris, Melody K. et al. “Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli.” Ed. Daniel A. Beard. PLoS Computational Biology 7 (2011): e1001099. en_US http://dx.doi.org/10.1371/journal.pcbi.1001099 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Morris, Melody Kay Saez-Rodriguez, Julio Clarke, David C. Sorger, Peter K. Lauffenburger, Douglas A. Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli |
| title | Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli |
| title_full | Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli |
| title_fullStr | Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli |
| title_full_unstemmed | Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli |
| title_short | Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli |
| title_sort | training signaling pathway maps to biochemical data with constrained fuzzy logic quantitative analysis of liver cell responses to inflammatory stimuli |
| url | http://hdl.handle.net/1721.1/66218 |
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