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This article is part of the supplement: Selected articles from the Second Annual Translational Bioinformatics Conference (TBC 2012)

Open Access Research

Altering physiological networks using drugs: steps towards personalized physiology

Adam D Grossman1, Mitchell J Cohen2, Geoffrey T Manley3 and Atul J Butte4*

Author Affiliations

1 Department of Bioengineering, Stanford University, Stanford, CA, USA. Now at Praedicat, Inc., Culver City, CA, USA

2 Department of Surgery, University of California San Francisco, San Francisco, CA, USA

3 Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA

4 Department of Pediatrics and the Department of Medicine, Stanford University School of Medicine, Stanford, CA, and Lucile Packard Children's Hospital, Palo Alto, CA, USA

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BMC Medical Genomics 2013, 6(Suppl 2):S7  doi:10.1186/1755-8794-6-S2-S7

Published: 7 May 2013



The rise of personalized medicine has reminded us that each patient must be treated as an individual. One factor in making treatment decisions is the physiological state of each patient, but definitions of relevant states and methods to visualize state-related physiologic changes are scarce. We constructed correlation networks from physiologic data to demonstrate changes associated with pressor use in the intensive care unit.


We collected 29 physiological variables at one-minute intervals from nineteen trauma patients in the intensive care unit of an academic hospital and grouped each minute of data as receiving or not receiving pressors. For each group we constructed Spearman correlation networks of pairs of physiologic variables. To visualize drug-associated changes we split the networks into three components: an unchanging network, a network of connections with changing correlation sign, and a network of connections only present in one group.


Out of a possible 406 connections between the 29 physiological measures, 64, 39, and 48 were present in each of the three component networks. The static network confirms expected physiological relationships while the network of associations with changed correlation sign suggests putative changes due to the drugs. The network of associations present only with pressors suggests new relationships that could be worthy of study.


We demonstrated that visualizing physiological relationships using correlation networks provides insight into underlying physiologic states while also showing that many of these relationships change when the state is defined by the presence of drugs. This method applied to targeted experiments could change the way critical care patients are monitored and treated.