Add the book chapter with Ion.

bib/sivanov-extra.bib

@@ -1,3 +1,19 @@
+@inbook{NushiPSMICP2022,
+author = {Nushi, Elio and Popescu, Victor-Bogdan and Sanchez Martin, Jose-Angel and Ivanov, Sergiu and Czeizler, Eugen and Petre, Ion},
+publisher = {John Wiley & Sons, Ltd},
+isbn = {9781119716600},
+title = {Network Modelling Methods for Precision Medicine},
+booktitle = {Systems Biology Modelling and Analysis},
+chapter = {10},
+pages = {363-423},
+doi = {https://doi.org/10.1002/9781119716600.ch10},
+url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119716600.ch10},
+eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119716600.ch10},
+year = {2022},
+keywords = {network medicine, computational modelling, precision medicine, drug repurposing, centrality measures, systems controllability, graph theory.},
+abstract = {Summary We discuss in this chapter several network modelling methods and their applicability to precision medicine. We review several network centrality methods (degree centrality, closeness centrality, eccentricity centrality, betweenness centrality, and eigenvector-based prestige) and two systems controllability methods (minimum dominating sets and network structural controllability). We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein–protein interactions (PPI) built around a specific patient's mutated genes, around the targets of the drugs used in the standard of care in multiple myeloma, and around multiple myeloma-specific essential genes. For each network, we demonstrate how the network methods we discuss can be used to identify personalized, targeted drug combinations uniquely suited to that patient.}
+}
+
 @techreport{AlhazovFI2016,
 author={Artiom Alhazov and Rudolf Freund and Sergiu Ivanov},
 title={Polymorphic P Systems: A Survey},