Lombardi, F., Pepic, S., Shriki, O., Tkadik, G., &; De Martino, D. (2023). Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. Nature Computational Science. Springer Nature.https://doi.org/10.1038/s43588-023-00410-9 

De Martino, D. (2019). Feedback-induced self-oscillations in large interacting systems subjected to phase transitions. Journal of Physics A: Mathematical and Theoretical. IOP Publishing.https://doi.org/10.1088/1751-8121/aaf2dd 

De Martino, A., &; De Martino, D. (2018). An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. Elsevier.https://doi.org/10.1016/j.heliyon.2018.e00596 

De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., &; Tkadik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. Nature Communications. Springer Nature.https://doi.org/10.1038/s41467-018-05417-9 

De Martino, D., &; Tkadik, G. (2018). Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.”https://doi.org/10.15479/AT:ISTA:62__Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:62 

De Martino, D. (2017). Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. https://doi.org/10.1103/PhysRevE.95.062419__Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.https://doi.org/10.1103/PhysRevE.95.062419 

De Martino, D., Capuani, F., &; De Martino, A. (2017). Quantifying the entropic cost of cellular growth control. https://doi.org/10.1103/PhysRevE.96.010401__Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.https://doi.org/10.1103/PhysRevE.96.010401 

Colabrese, S., De Martino, D., Leuzzi, L., &; Marinari, E. (2017). Phase transitions in integer linear problems. https://doi.org/10.1088/1742-5468/aa85c3__Journal of Statistical Mechanics: Theory and Experiment. IOPscience.https://doi.org/10.1088/1742-5468/aa85c3 

De Martino, D. (2017). Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. American Physical Society.https://doi.org/10.1103/PhysRevE.96.060401 

De Martino, D., &; Masoero, D. (2016). Asymptotic analysis of noisy fitness maximization, applied to metabolism &;amp; growth. https://doi.org/10.1088/1742-5468/aa4e8f__Journal of Statistical Mechanics: Theory and Experiment. IOPscience.https://doi.org/10.1088/1742-5468/aa4e8f 

De Martino, D. (2016). The dual of the space of interactions in neural network models. International Journal of Modern Physics C. World Scientific Publishing.https://doi.org/10.1142/S0129183116500674 

De Martino, D., Capuani, F., &; De Martino, A. (2016). Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. IOP Publishing Ltd.https://doi.org/10.1088/1478-3975/13/3/036005 

De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. IOP Publishing Ltd.https://doi.org/10.1088/1478-3975/13/1/016003