Giovanni Paternostro, M.D., Ph.D.

Giovanni Paternostro's Research Focus

Leukemia/Lymphoma, Ovarian Cancer

Our lab uses a systems biology approach to the study of complex diseases. Combined drug interventions are an increasingly common therapeutic approach to complex diseases, for example in cancer. Drugs are, however, usually developed individually and only later combined empirically in the clinic based on their known effects as single-therapy agents. We are interested in the problem of inducing selective cancer cell death. We have developed and validated search algorithms to discover optimal combinations of three or more drugs that would be infeasible to identify by fully combinatorial searches. In our procedure the optimization is not carried out in silico, but directly in an in vivo high-throughput system, where the response to therapeutic combinations is used as information to guide the system toward improved combinations using an iterative algorithm. System-wide molecular measurements (for example metabolomics and transcriptomics) and models can also be incorporated in these algorithms. It is useful to view the information processing by our experimental cellular systems as biological computations, since the algorithms we use are indeed often derived from algorithms that are implemented in silico in other scientific fields.

We also use the fruit fly (Drosophila) to study cardiac and metabolic alterations caused by aging and hypoxia, using high-throughput physiological measurements, NMR metabolomics and models of metabolism.

Our multi-disciplinary team is composed of biomedical and computational scientists, and we have close collaborations with physicists, engineers and bioengineers.

Giovanni Paternostro's Bio

Giovanni Paternostro earned his Ph.D. in Biochemistry from the University of Oxford, England, in 1997. He has obtained his M.D. and Board Certification in Cardiology from the University of Rome, Italy. After postdoctoral training at the Imperial College School of Medicine, Hammersmith Hospital, London and at Sanford Burnham Prebys he was promoted to Research Investigator in 2001 and to Assistant Professor in 2003. In 2001 he was nominated member of the Whitaker Institute for Biomedical Engineering, UC San Diego. His research has been recognized by the 2002 Society for Geriatric Cardiology Basic Science Award and by the Ellison Medical Foundation New Scholar in Aging Award. Dr. Paternostro now holds adjunct faculty positions at Sanford Burnham Prebys Medical Discovery Institute and at the Department of Bioengineering, UC San Diego. His lab is located at Sanford Burnham Prebys.

CMSN Accessory


Integrating metabolomics and phenomics with systems models of cardiac hypoxia.

Feala JD, Coquin L, Paternostro G, McCulloch AD

Prog Biophys Mol Biol 2008 Jan-Apr ;96(1-3):209-25

Selective control of the apoptosis signaling network in heterogeneous cell populations.

Calzolari D, Paternostro G, Harrington PL Jr, Piermarocchi C, Duxbury PM

PLoS One 2007 Jun 20 ;2(6):e547

Show All Select Publications

Gain of function of a metalloproteinase associated with multiple myeloma, bicuspid aortic valve, and Von Hippel-Lindau syndrome.

Snipas SJ, Jappelli R, Torkamani A, Paternostro G, Salvesen GS

Biochem J 2022 Jul 29 ;479(14):1533-1542

Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq.

Domanskyi S, Hakansson A, Paternostro G, Piermarocchi C

Proceedings (IEEE Int Conf Bioinformatics Biomed) 2019 Nov ;2019:2129-2136

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells.

Domanskyi S, Hakansson A, Meng M, Pham BK, Graff Zivin JS, Piermarocchi C, Paternostro G, Ferrara N

Sci Rep 2022 Apr 6 ;12(1):5807

LIF, a mitogen for choroidal endothelial cells, protects the choriocapillaris: implications for prevention of geographic atrophy.

Li P, Li Q, Biswas N, Xin H, Diemer T, Liu L, Perez Gutierrez L, Paternostro G, Piermarocchi C, Domanskyi S, Wang RK, Ferrara N

EMBO Mol Med 2022 Jan 11 ;14(1):e14511

Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters.

Domanskyi S, Szedlak A, Hawkins NT, Wang J, Paternostro G, Piermarocchi C

BMC Bioinformatics 2019 Jul 1 ;20(1):369

Show All Publications