Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics
A hybrid modeling framework integrating a highly specific mechanistic model with highly abstract empirical model is presented. With the growing interest in the scientific and medical community for identification of therapeutic targets in treatment of disease, it is necessary to develop predictive mo...
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| Format: | Article |
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ASME International
2018
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| Online Access: | http://hdl.handle.net/1721.1/118971 https://orcid.org/0000-0002-9678-0157 https://orcid.org/0000-0003-3155-6223 |
| _version_ | 1826208958161879040 |
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| author | Mayalu, Michaelle N Asada, Haruhiko |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Mayalu, Michaelle N Asada, Haruhiko |
| author_sort | Mayalu, Michaelle N |
| collection | MIT |
| description | A hybrid modeling framework integrating a highly specific mechanistic model with highly abstract empirical model is presented. With the growing interest in the scientific and medical community for identification of therapeutic targets in treatment of disease, it is necessary to develop predictive models that can describe cellular behavior in response to environmental cues. Intracellular signaling pathways form complex networks that regulate cellular response in both health and disease. Mechanistic (or white-box) models of biochemical networks are often unable to explain comprehensive cellular response due to lack of knowledge and/or intractable complexity (especially in events distal from the cell membrane). Empirical (or black-box) models may provide a less than accurate representation of cellular response due to data deficiency and/or loss of mechanistic detail. In the proposed framework, we use a mechanistic model to capture early signaling events and apply the resulting generated internal signals (along with external inputs) to a downstream empirical sub-model. The key construct in the approach is the treatment of a cell's biochemical network as an encoder that creates a functional internal representation of external environmental cues. The signals derived from this representation are then used to inform downstream behaviors. Using this idea, we are able to create a comprehensive framework that describes important mechanisms with sufficient detail, while representing complex or unknown mechanisms in a more abstract form. The model is verified using published biological data describing T-Cells in immune response. |
| first_indexed | 2024-09-23T14:15:08Z |
| format | Article |
| id | mit-1721.1/118971 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:15:08Z |
| publishDate | 2018 |
| publisher | ASME International |
| record_format | dspace |
| spelling | mit-1721.1/1189712022-10-01T20:08:29Z Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics Mayalu, Michaelle N Asada, Haruhiko Massachusetts Institute of Technology. Department of Mechanical Engineering Mayalu, Michaelle N Asada, Haruhiko A hybrid modeling framework integrating a highly specific mechanistic model with highly abstract empirical model is presented. With the growing interest in the scientific and medical community for identification of therapeutic targets in treatment of disease, it is necessary to develop predictive models that can describe cellular behavior in response to environmental cues. Intracellular signaling pathways form complex networks that regulate cellular response in both health and disease. Mechanistic (or white-box) models of biochemical networks are often unable to explain comprehensive cellular response due to lack of knowledge and/or intractable complexity (especially in events distal from the cell membrane). Empirical (or black-box) models may provide a less than accurate representation of cellular response due to data deficiency and/or loss of mechanistic detail. In the proposed framework, we use a mechanistic model to capture early signaling events and apply the resulting generated internal signals (along with external inputs) to a downstream empirical sub-model. The key construct in the approach is the treatment of a cell's biochemical network as an encoder that creates a functional internal representation of external environmental cues. The signals derived from this representation are then used to inform downstream behaviors. Using this idea, we are able to create a comprehensive framework that describes important mechanisms with sufficient detail, while representing complex or unknown mechanisms in a more abstract form. The model is verified using published biological data describing T-Cells in immune response. 2018-11-08T20:59:30Z 2018-11-08T20:59:30Z 2013-10 2018-10-23T18:23:04Z Article http://purl.org/eprint/type/ConferencePaper 978-0-7918-5614-7 http://hdl.handle.net/1721.1/118971 Mayalu, Michaëlle N., and H. Harry Asada. “Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics.” ASME 2013 Dynamic Systems and Control Conference, 21-23 October, 2013, Palo Alto, California, ASME, 2013, p. V003T43A002. © 2013 by ASME. https://orcid.org/0000-0002-9678-0157 https://orcid.org/0000-0003-3155-6223 http://dx.doi.org/10.1115/DSCC2013-3806 Proceedings of the ASME 2013 Dynamic Systems and Control Conference Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf ASME International ASME |
| spellingShingle | Mayalu, Michaelle N Asada, Haruhiko Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics |
| title | Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics |
| title_full | Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics |
| title_fullStr | Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics |
| title_full_unstemmed | Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics |
| title_short | Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics |
| title_sort | integrated mechanistic empirical modeling of cellular response based on intracellular signaling dynamics |
| url | http://hdl.handle.net/1721.1/118971 https://orcid.org/0000-0002-9678-0157 https://orcid.org/0000-0003-3155-6223 |
| work_keys_str_mv | AT mayalumichaellen integratedmechanisticempiricalmodelingofcellularresponsebasedonintracellularsignalingdynamics AT asadaharuhiko integratedmechanisticempiricalmodelingofcellularresponsebasedonintracellularsignalingdynamics |