Daniele De Martino
Tkacik Group
13 Publications
2023 | Published | Journal Article | IST-REx-ID: 12762 |
Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. Nature Computational Science. 2023;3:254-263. doi:10.1038/s43588-023-00410-9
[Published Version]
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| DOI
| arXiv
2019 | Published | Journal Article | IST-REx-ID: 6049 |
De Martino D. Feedback-induced self-oscillations in large interacting systems subjected to phase transitions. Journal of Physics A: Mathematical and Theoretical. 2019;52(4). doi:10.1088/1751-8121/aaf2dd
[Published Version]
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| Files available
| DOI
| WoS
2018 | Published | Journal Article | IST-REx-ID: 306 |
De Martino A, De Martino D. An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. 2018;4(4). doi:10.1016/j.heliyon.2018.e00596
[Published Version]
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| DOI
2018 | Research Data | IST-REx-ID: 5587 |
De Martino D, Tkačik G. Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” 2018. doi:10.15479/AT:ISTA:62
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2018 | Published | Journal Article | IST-REx-ID: 161 |
De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics for metabolic networks during steady state growth. Nature Communications. 2018;9(1). doi:10.1038/s41467-018-05417-9
[Published Version]
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| WoS
2017 | Published | Journal Article | IST-REx-ID: 548 |
De Martino D. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. 2017;96(6). doi:10.1103/PhysRevE.96.060401
[Submitted Version]
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2017 | Published | Journal Article | IST-REx-ID: 823 |
Colabrese S, De Martino D, Leuzzi L, Marinari E. Phase transitions in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment. 2017;2017(9). doi:10.1088/1742-5468/aa85c3
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2017 | Published | Journal Article | IST-REx-ID: 947 |
De Martino D, Capuani F, De Martino A. Quantifying the entropic cost of cellular growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics . 2017;96(1). doi:10.1103/PhysRevE.96.010401
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| WoS
2017 | Published | Journal Article | IST-REx-ID: 959 |
De Martino D. Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . 2017;95(6):062419. doi:10.1103/PhysRevE.95.062419
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| WoS
2016 | Published | Journal Article | IST-REx-ID: 1260 |
De Martino D. The dual of the space of interactions in neural network models. International Journal of Modern Physics C. 2016;27(6). doi:10.1142/S0129183116500674
[Preprint]
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| arXiv
2016 | Published | Journal Article | IST-REx-ID: 1394 |
De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005
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2016 | Published | Journal Article | IST-REx-ID: 1188 |
De Martino D, Masoero D. Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. 2016;2016(12). doi:10.1088/1742-5468/aa4e8f
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2016 | Published | Journal Article | IST-REx-ID: 1485 |
De Martino D. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. 2016;13(1). doi:10.1088/1478-3975/13/1/016003
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Grants
13 Publications
2023 | Published | Journal Article | IST-REx-ID: 12762 |
Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. Nature Computational Science. 2023;3:254-263. doi:10.1038/s43588-023-00410-9
[Published Version]
View
| Files available
| DOI
| arXiv
2019 | Published | Journal Article | IST-REx-ID: 6049 |
De Martino D. Feedback-induced self-oscillations in large interacting systems subjected to phase transitions. Journal of Physics A: Mathematical and Theoretical. 2019;52(4). doi:10.1088/1751-8121/aaf2dd
[Published Version]
View
| Files available
| DOI
| WoS
2018 | Published | Journal Article | IST-REx-ID: 306 |
De Martino A, De Martino D. An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. 2018;4(4). doi:10.1016/j.heliyon.2018.e00596
[Published Version]
View
| Files available
| DOI
2018 | Research Data | IST-REx-ID: 5587 |
De Martino D, Tkačik G. Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” 2018. doi:10.15479/AT:ISTA:62
[Published Version]
View
| Files available
| DOI
2018 | Published | Journal Article | IST-REx-ID: 161 |
De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics for metabolic networks during steady state growth. Nature Communications. 2018;9(1). doi:10.1038/s41467-018-05417-9
[Published Version]
View
| Files available
| DOI
| WoS
2017 | Published | Journal Article | IST-REx-ID: 548 |
De Martino D. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. 2017;96(6). doi:10.1103/PhysRevE.96.060401
[Submitted Version]
View
| DOI
| Download Submitted Version (ext.)
2017 | Published | Journal Article | IST-REx-ID: 823 |
Colabrese S, De Martino D, Leuzzi L, Marinari E. Phase transitions in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment. 2017;2017(9). doi:10.1088/1742-5468/aa85c3
[Submitted Version]
View
| DOI
| Download Submitted Version (ext.)
| WoS
2017 | Published | Journal Article | IST-REx-ID: 947 |
De Martino D, Capuani F, De Martino A. Quantifying the entropic cost of cellular growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics . 2017;96(1). doi:10.1103/PhysRevE.96.010401
[Submitted Version]
View
| DOI
| Download Submitted Version (ext.)
| WoS
2017 | Published | Journal Article | IST-REx-ID: 959 |
De Martino D. Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . 2017;95(6):062419. doi:10.1103/PhysRevE.95.062419
[Submitted Version]
View
| DOI
| Download Submitted Version (ext.)
| WoS
2016 | Published | Journal Article | IST-REx-ID: 1260 |
De Martino D. The dual of the space of interactions in neural network models. International Journal of Modern Physics C. 2016;27(6). doi:10.1142/S0129183116500674
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2016 | Published | Journal Article | IST-REx-ID: 1394 |
De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005
[Preprint]
View
| DOI
| Download Preprint (ext.)
2016 | Published | Journal Article | IST-REx-ID: 1188 |
De Martino D, Masoero D. Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. 2016;2016(12). doi:10.1088/1742-5468/aa4e8f
[Preprint]
View
| DOI
| Download Preprint (ext.)
2016 | Published | Journal Article | IST-REx-ID: 1485 |
De Martino D. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. 2016;13(1). doi:10.1088/1478-3975/13/1/016003
[Preprint]
View
| DOI
| Download Preprint (ext.)