In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high ‘control centrality’ of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.

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Adiponectin and leptin link metabolic disorders and coronary artery disease (CAD). We analysed their relationship with CAD, classical risk factors and biomarkers in 287 CAD patients (cases) and 477 unaffected family members (controls) selected from the Indian Atherosclerosis Research Study (IARS). Classical risk factors included diabetes, hypertension, dyslipidaemia and obesity markers. Novel biomarkers were measured according to manufacturer recommendations. Adverse clinical events were recorded through telephonic follow-up. Cases showed lower adiponectin levels (4684.62 ± 190.73 ng/ml) than controls (5768.86 ± 152.87 ng/ml) (p=1.58X10(-5)); Leptin levels were higher in affected males (12.47 ± 1.32 ng/ml) than in male controls (9.53 ± 1.19 ng/ml, p=0.017). Adiponectin 1st quartile showed significant protection against CAD in females when compared to 3rd (odds ratio [OR] 0.39, 0.16-0.92, p=0.032) or 4th (OR 0.32, 0.14-0.72; p=0.006) quartile group. Leptin 3rd quartile showed higher CAD risk in males as compared to 1st quartile group (OR 2.09, 1.09-4.01, p=0.028). Subjects with metabolic syndrome showed low adiponectin and high leptin levels. Adipokines showed opposing association trend with lipids, inflammatory and coagulation markers and strong correlation (r=-0.14 to 0.52) with obesity markers. Cases with recurrent event and controls who developed new cardiac event during follow up showed high adiponectin levels (p<0.05). A model that combined adiponectin, leptin and conventional risk factors yielded the best 'C' index (0.890, 0.067-0.912). CAD patients in the top adiponectin tertile showed relatively poor survival curve as compared to the bottom Adiponectin tertile group. In conclusion, our findings strengthen the reported association between low adiponectin, high leptin, obesity-related metabolic disturbances and incident CAD in Asian Indians.

Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.