Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation
Abstract Background Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cellular function, in which cells must perceive environmental cues and rela...
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2970-7 |
| _version_ | 1819028969382477824 |
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| author | Aditya Sai Nan Kong |
| author_facet | Aditya Sai Nan Kong |
| author_sort | Aditya Sai |
| collection | DOAJ |
| description | Abstract Background Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cellular function, in which cells must perceive environmental cues and relay signals accurately to make timely and informed decisions. Using multivariate response data can greatly improve estimates of the latent information content underlying important cell fates, like differentiation. Results We undertake an information theoretic analysis of two stochastic models concerning glioma differentiation therapy, an alternative cancer treatment modality whose underlying intracellular mechanisms remain poorly understood. Discernible changes in response dynamics, as captured by summary measures, were observed at low noise levels. Mitigating certain feedback mechanisms present in the signaling network improved information transmission overall, as did targeted subsampling and clustering of response dynamics. Conclusion Computing the channel capacity of noisy signaling pathways present great probative value in uncovering the prevalent trends in noise-induced dynamics. Areas of high dynamical variation can provide concise snapshots of informative system behavior that may otherwise be overlooked. Through this approach, we can examine the delicate interplay between noise and information, from signal to response, through the observed behavior of relevant system components. |
| first_indexed | 2024-12-21T06:06:48Z |
| format | Article |
| id | doaj.art-4e3e51c83373427fb3bc9d54fbbe04e0 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T06:06:48Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-4e3e51c83373427fb3bc9d54fbbe04e02022-12-21T19:13:38ZengBMCBMC Bioinformatics1471-21052019-07-0120111110.1186/s12859-019-2970-7Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiationAditya Sai0Nan Kong1Weldon School of Biomedical Engineering, Purdue UniversityWeldon School of Biomedical Engineering, Purdue UniversityAbstract Background Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cellular function, in which cells must perceive environmental cues and relay signals accurately to make timely and informed decisions. Using multivariate response data can greatly improve estimates of the latent information content underlying important cell fates, like differentiation. Results We undertake an information theoretic analysis of two stochastic models concerning glioma differentiation therapy, an alternative cancer treatment modality whose underlying intracellular mechanisms remain poorly understood. Discernible changes in response dynamics, as captured by summary measures, were observed at low noise levels. Mitigating certain feedback mechanisms present in the signaling network improved information transmission overall, as did targeted subsampling and clustering of response dynamics. Conclusion Computing the channel capacity of noisy signaling pathways present great probative value in uncovering the prevalent trends in noise-induced dynamics. Areas of high dynamical variation can provide concise snapshots of informative system behavior that may otherwise be overlooked. Through this approach, we can examine the delicate interplay between noise and information, from signal to response, through the observed behavior of relevant system components.http://link.springer.com/article/10.1186/s12859-019-2970-7Information theoryMutual informationChannel capacityStochastic modelingChemical langevin equationGlioma differentiation |
| spellingShingle | Aditya Sai Nan Kong Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation BMC Bioinformatics Information theory Mutual information Channel capacity Stochastic modeling Chemical langevin equation Glioma differentiation |
| title | Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation |
| title_full | Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation |
| title_fullStr | Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation |
| title_full_unstemmed | Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation |
| title_short | Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation |
| title_sort | exploring the information transmission properties of noise induced dynamics application to glioma differentiation |
| topic | Information theory Mutual information Channel capacity Stochastic modeling Chemical langevin equation Glioma differentiation |
| url | http://link.springer.com/article/10.1186/s12859-019-2970-7 |
| work_keys_str_mv | AT adityasai exploringtheinformationtransmissionpropertiesofnoiseinduceddynamicsapplicationtogliomadifferentiation AT nankong exploringtheinformationtransmissionpropertiesofnoiseinduceddynamicsapplicationtogliomadifferentiation |