Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

Sci Rep. 2016 Feb 24;6:22023. doi: 10.1038/srep22023.


Biomarkers have gained immense scientific interest and clinical value in the practice of medicine. With unprecedented advances in high-throughput technologies, research interest in identifying novel and customized disease biomarkers for early detection, diagnosis, or drug responses is rapidly growing. Biomarkers can be identified in different levels of molecular biomarkers, networks biomarkers and dynamical network biomarkers (DNBs). The latter is a recently developed concept which relies on the idea that a cell is a complex system whose behavior is emerged from interplay of various molecules, and this network of molecules dynamically changes over time. A DNB can serve as an early-warning signal of disease progression, or as a leading network that drives the system into the disease state, and thus unravels mechanisms of disease initiation and progression. It is therefore of great importance to identify DNBs efficiently and reliably. In this work, the problem of DNB identification is defined as a multi-objective optimization problem, and a framework to identify DNBs out of time-course high-throughput data is proposed. Temporal gene expression data of a lung injury with carbonyl chloride inhalation exposure has been used as a case study, and the functional role of the discovered biomarker in the pathogenesis of lung injury has been thoroughly analyzed.

MeSH terms

  • Acute Lung Injury / chemically induced
  • Acute Lung Injury / diagnosis*
  • Acute Lung Injury / genetics
  • Acute Lung Injury / metabolism
  • Administration, Inhalation
  • Algorithms
  • Animals
  • Apelin Receptors
  • Biomarkers / metabolism
  • CDC2 Protein Kinase
  • Cyclin-Dependent Kinases / genetics
  • Cyclin-Dependent Kinases / metabolism
  • Early Diagnosis
  • Gene Expression Profiling
  • Gene Expression Regulation*
  • Lectins, C-Type / genetics
  • Lectins, C-Type / metabolism
  • Mice
  • Microarray Analysis
  • Models, Statistical*
  • Phosgene / toxicity
  • Pulmonary Edema / chemically induced
  • Pulmonary Edema / diagnosis*
  • Pulmonary Edema / genetics
  • Pulmonary Edema / metabolism
  • Receptors, G-Protein-Coupled / genetics
  • Receptors, G-Protein-Coupled / metabolism
  • Sulfotransferases / genetics
  • Sulfotransferases / metabolism
  • Tenascin / genetics
  • Tenascin / metabolism
  • Xanthine Dehydrogenase / genetics
  • Xanthine Dehydrogenase / metabolism


  • APLNR protein, human
  • Apelin Receptors
  • Biomarkers
  • Lectins, C-Type
  • Receptors, G-Protein-Coupled
  • Tenascin
  • Phosgene
  • Xanthine Dehydrogenase
  • CDC2 Protein Kinase
  • CDK1 protein, human
  • Cyclin-Dependent Kinases
  • Sulfotransferases
  • carbohydrate sulfotransferases