Daniele De Martino

Tkacik Group

13 Publications

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[13]
2023 | Journal Article | IST-REx-ID: 12762 | OA
F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain,” Nature Computational Science, vol. 3. Springer Nature, pp. 254–263, 2023.
[Published Version] View | Files available | DOI | arXiv
 
[12]
2019 | Journal Article | IST-REx-ID: 6049 | OA
D. De Martino, “Feedback-induced self-oscillations in large interacting systems subjected to phase transitions,” Journal of Physics A: Mathematical and Theoretical, vol. 52, no. 4. IOP Publishing, 2019.
[Published Version] View | Files available | DOI | WoS
 
[11]
2018 | Journal Article | IST-REx-ID: 306 | OA
A. De Martino and D. De Martino, “An introduction to the maximum entropy approach and its application to inference problems in biology,” Heliyon, vol. 4, no. 4. Elsevier, 2018.
[Published Version] View | Files available | DOI
 
[10]
2018 | Journal Article | IST-REx-ID: 161 | OA
D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical mechanics for metabolic networks during steady state growth,” Nature Communications, vol. 9, no. 1. Springer Nature, 2018.
[Published Version] View | Files available | DOI | WoS
 
[9]
2018 | Research Data | IST-REx-ID: 5587 | OA
D. De Martino and G. Tkačik, “Supporting materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
[8]
2017 | Journal Article | IST-REx-ID: 959 | OA
D. De Martino, “Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics,” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p. 062419, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[7]
2017 | Journal Article | IST-REx-ID: 947 | OA
D. De Martino, F. Capuani, and A. De Martino, “Quantifying the entropic cost of cellular growth control,” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 96, no. 1. American Institute of Physics, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[6]
2017 | Journal Article | IST-REx-ID: 823 | OA
S. Colabrese, D. De Martino, L. Leuzzi, and E. Marinari, “Phase transitions in integer linear problems,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no. 9. IOPscience, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[5]
2017 | Journal Article | IST-REx-ID: 548 | OA
D. De Martino, “Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes,” Physical Review E, vol. 96, no. 6. American Physical Society, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
[4]
2016 | Journal Article | IST-REx-ID: 1188 | OA
D. De Martino and D. Masoero, “Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 12. IOPscience, 2016.
[Preprint] View | DOI | Download Preprint (ext.)
 
[3]
2016 | Journal Article | IST-REx-ID: 1260 | OA
D. De Martino, “The dual of the space of interactions in neural network models,” International Journal of Modern Physics C, vol. 27, no. 6. World Scientific Publishing, 2016.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2016 | Journal Article | IST-REx-ID: 1394 | OA
D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.
[Preprint] View | DOI | Download Preprint (ext.)
 
[1]
2016 | Journal Article | IST-REx-ID: 1485 | OA
D. De Martino, “Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis,” Physical Biology, vol. 13, no. 1. IOP Publishing Ltd., 2016.
[Preprint] View | DOI | Download Preprint (ext.)
 
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13 Publications

Mark all

[13]
2023 | Journal Article | IST-REx-ID: 12762 | OA
F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain,” Nature Computational Science, vol. 3. Springer Nature, pp. 254–263, 2023.
[Published Version] View | Files available | DOI | arXiv
 
[12]
2019 | Journal Article | IST-REx-ID: 6049 | OA
D. De Martino, “Feedback-induced self-oscillations in large interacting systems subjected to phase transitions,” Journal of Physics A: Mathematical and Theoretical, vol. 52, no. 4. IOP Publishing, 2019.
[Published Version] View | Files available | DOI | WoS
 
[11]
2018 | Journal Article | IST-REx-ID: 306 | OA
A. De Martino and D. De Martino, “An introduction to the maximum entropy approach and its application to inference problems in biology,” Heliyon, vol. 4, no. 4. Elsevier, 2018.
[Published Version] View | Files available | DOI
 
[10]
2018 | Journal Article | IST-REx-ID: 161 | OA
D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical mechanics for metabolic networks during steady state growth,” Nature Communications, vol. 9, no. 1. Springer Nature, 2018.
[Published Version] View | Files available | DOI | WoS
 
[9]
2018 | Research Data | IST-REx-ID: 5587 | OA
D. De Martino and G. Tkačik, “Supporting materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
[8]
2017 | Journal Article | IST-REx-ID: 959 | OA
D. De Martino, “Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics,” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p. 062419, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[7]
2017 | Journal Article | IST-REx-ID: 947 | OA
D. De Martino, F. Capuani, and A. De Martino, “Quantifying the entropic cost of cellular growth control,” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 96, no. 1. American Institute of Physics, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[6]
2017 | Journal Article | IST-REx-ID: 823 | OA
S. Colabrese, D. De Martino, L. Leuzzi, and E. Marinari, “Phase transitions in integer linear problems,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no. 9. IOPscience, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[5]
2017 | Journal Article | IST-REx-ID: 548 | OA
D. De Martino, “Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes,” Physical Review E, vol. 96, no. 6. American Physical Society, 2017.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
[4]
2016 | Journal Article | IST-REx-ID: 1188 | OA
D. De Martino and D. Masoero, “Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 12. IOPscience, 2016.
[Preprint] View | DOI | Download Preprint (ext.)
 
[3]
2016 | Journal Article | IST-REx-ID: 1260 | OA
D. De Martino, “The dual of the space of interactions in neural network models,” International Journal of Modern Physics C, vol. 27, no. 6. World Scientific Publishing, 2016.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2016 | Journal Article | IST-REx-ID: 1394 | OA
D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.
[Preprint] View | DOI | Download Preprint (ext.)
 
[1]
2016 | Journal Article | IST-REx-ID: 1485 | OA
D. De Martino, “Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis,” Physical Biology, vol. 13, no. 1. IOP Publishing Ltd., 2016.
[Preprint] View | DOI | Download Preprint (ext.)
 
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    Citation Style: IEEE

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