Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach
Background: Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective ex...
| Główni autorzy: | , , , , , , , , , , , , , , , , |
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| Kolejni autorzy: | |
| Format: | Artykuł |
| Język: | English |
| Wydane: |
BioMed Central Ltd
2014
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| Dostęp online: | http://hdl.handle.net/1721.1/86005 https://orcid.org/0000-0002-2724-7228 |
| _version_ | 1826204601680920576 |
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| author | Meyer, Pablo Cokelaer, Thomas Chandran, Deepak Kim, Kyung H. Loh, Po-Ru Lipson, Mark Berger, Bonnie Kreutz, Clemens Raue, Andreas Steiert, Bernhard Timmer, Jens Bilal, Erhan DREAM 6&7 Parameter Estimation consortium Sauro, Herbert M. Stolovitzky, Gustavo Saez-Rodriguez, Julio Tucker, George Jay |
| author2 | Massachusetts Institute of Technology. Department of Mathematics |
| author_facet | Massachusetts Institute of Technology. Department of Mathematics Meyer, Pablo Cokelaer, Thomas Chandran, Deepak Kim, Kyung H. Loh, Po-Ru Lipson, Mark Berger, Bonnie Kreutz, Clemens Raue, Andreas Steiert, Bernhard Timmer, Jens Bilal, Erhan DREAM 6&7 Parameter Estimation consortium Sauro, Herbert M. Stolovitzky, Gustavo Saez-Rodriguez, Julio Tucker, George Jay |
| author_sort | Meyer, Pablo |
| collection | MIT |
| description | Background:
Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants.
Results:
We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation.
Conclusions:
A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission. |
| first_indexed | 2024-09-23T12:58:03Z |
| format | Article |
| id | mit-1721.1/86005 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:58:03Z |
| publishDate | 2014 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | mit-1721.1/860052022-09-28T11:12:32Z Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach Meyer, Pablo Cokelaer, Thomas Chandran, Deepak Kim, Kyung H. Loh, Po-Ru Lipson, Mark Berger, Bonnie Kreutz, Clemens Raue, Andreas Steiert, Bernhard Timmer, Jens Bilal, Erhan DREAM 6&7 Parameter Estimation consortium Sauro, Herbert M. Stolovitzky, Gustavo Saez-Rodriguez, Julio Tucker, George Jay Massachusetts Institute of Technology. Department of Mathematics Loh, Po-Ru Tucker, George Jay Lipson, Mark Berger, Bonnie Background: Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results: We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions: A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission. American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship National Science Foundation (U.S.). Graduate Research Fellowship Program 2014-04-03T19:05:12Z 2014-04-03T19:05:12Z 2014-02 2013-09 2014-04-02T15:40:13Z Article http://purl.org/eprint/type/JournalArticle 1752-0509 http://hdl.handle.net/1721.1/86005 Meyer, Pablo et al. “Network Topology and Parameter Estimation: From Experimental Design Methods to Gene Regulatory Network Kinetics Using a Community Based Approach.” BMC Systems Biology 8.1 (2014): 13. https://orcid.org/0000-0002-2724-7228 en http://dx.doi.org/10.1186/1752-0509-8-13 BMC Systems Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 Pablo Meyer et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd BioMed Central Ltd |
| spellingShingle | Meyer, Pablo Cokelaer, Thomas Chandran, Deepak Kim, Kyung H. Loh, Po-Ru Lipson, Mark Berger, Bonnie Kreutz, Clemens Raue, Andreas Steiert, Bernhard Timmer, Jens Bilal, Erhan DREAM 6&7 Parameter Estimation consortium Sauro, Herbert M. Stolovitzky, Gustavo Saez-Rodriguez, Julio Tucker, George Jay Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach |
| title | Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach |
| title_full | Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach |
| title_fullStr | Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach |
| title_full_unstemmed | Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach |
| title_short | Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach |
| title_sort | network topology and parameter estimation from experimental design methods to gene regulatory network kinetics using a community based approach |
| url | http://hdl.handle.net/1721.1/86005 https://orcid.org/0000-0002-2724-7228 |
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