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

Abstract

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.

Toward Dynamical Systems Medicine: Personalized and Preventive Strategies

“…For example, the 'dynamical network biomarkers' can illustrate the cell as a CASs with the varying molecular networks to represent the early warning signals of disease initiation and progression [15]. This strategy can be expanded to include across-scale spatiotemporal factors to replace the static portraits in the conventional diagnosis.…”

Two Novel Methods for Extracting Synchronously Fluctuated Genes

“…The development of methods for transcriptome data analysis is one of the important research topics in the field of bioinformatics. In this study, I focus on the extraction of synchronously fluctuated genes (SFGs), which were also called dynamical network biomarkers in previous studies [1], [2], [3], [4], [5], [6], [7], [8], [9]. SFGs are similar to but different from differentially expressed genes (DEGs).…”

“…SFGs have been investigated by several research groups [1], [2], [3], [4], [5], [6], [7], [8], [9] based on an expectation that they are useful for characterizing the predisease state [1], that is, a state before the disease onset with lowered stability and abnormal homeostasis. The characterization of the predisease state may lead to rational and efficient disease prevention.…”

“…In addition, SFGs are also expected to be useful for the diagnosis of the predisease state [1], although the cost of DNA microarray or RNA-seq measurements seems to be too high at present for the purpose of health monitoring. Putative predisease states before several diseases have been claimed to be identified and characterized using SFGs: acute respiratory distress syndrome [1], [5], [6], hepatocellular carcinoma [8], [9], diabetes mellitus [2], [3], [5], and influenza [5]. In addition, preceding oku@inm.u-toyama.ac.jp states before cell differentiation in in-vitro experiments have also been characterized using SFGs [4], [7].…”

Identification of thyroxine-binding globulin as a candidate plasma marker of chronic obstructive pulmonary disease

“…Biomarkers, such as proteins, microRNAs, and metabolites, play an important role in the diagnosis and management of various complex diseases 2–5. However, biomarkers for the diagnosis and management of COPD are very limited 6,7.…”