A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Abstract

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

Keywords: Biological networks; Genotype; Phenotype; Systems biology; Systems engineering.

Fig. 1

Schematic diagram showing the typical coordination of genetic, signaling and metabolic networks for generating a phenotypic response. The figure shows the schematic of the typical information processing in a cellular network. Any defect at either of these levels of information process can lead to disruption in the adequate response leading to disease states

Fig. 2

A systemic map of the complex nature of insulin resistance and defective metabolic homeostasis. The factors affecting insulin secretion, signaling and action in liver, muscle, adipose and brain are shown in the figure. The bold lines represent the modeled interactions. The green and red lines show positive and negative effects of corresponding factors on insulin, respectively, whereas the blue lines show the general flow of the processes. (Color figure online)

Fig. 3

a The integrated network of metabolic signaling regulation and the interplay of insulin, mTOR, glucagon and calcium signaling pathways. The interplay depicts the regulation of these signaling pathways on glucose, amino acids and fat metabolism. b Dose response curve for a sub module of integrated network, i.e. insulin stimulated fractional GLUT4 on plasma membrane. Curve ‘a’ is sigmoidal dose–response curve obtained in absence of positive feedback loop. Curve ‘b’ represents a bistable response in insulin-stimulated fractional GLUT4 on plasma membrane in presence of feedback loop. Simulated type 2 diabetic conditions represented by dose–response curve of insulin-stimulated fractional GLUT4 on membrane at threefold higher PTP concentration. Note that the input–output relationship is perturbed for high PTP concentration leading to higher requirement of insulin above the physiological levels

Fig. 4

a Integrated network of metabolic and signaling pathways involved in warberg effect in cancerous cells. The network consists of the interplay of growth factor (insulin) signalling, p53-Mdm2-PTEN signaling and the HIF1 signalling that regulates central metabolism. The apoptosis, crab-tree effect and the degradation of HIF1 by metabolites is shown by dotted lines. b The dose response curve for a submodule of the integrate network for HIF1 with respect to varying α-ketoglutarate concentration. The system elicit bistability with two distinct states i.e. glycolytic phenotype and normal TCA functioning state. c The dose response curve for another submodule of the integrate network for p53 activation with respect to total Akt concentration. Due to mutual inhibitory loop the AKT-p53 signaling system also elicit bistability with respect to varying AKT concentrations leading to prosurvival/cancerous phenotype and pro apoptotic states. In the normal range of Akt, p53 shows oscillatory behaviour (curve ‘a’) in the dynamic scale which enables the cell to frequently decide between survival and apoptotic cellular fate

Fig. 5

Shematic framework for application of network modeling in disease systems and drug development. The framework illustrates the flow and interconnections of the five modules viz. data mining, networking, modeling and analysis, experimental and drug development, validation and drug implementation processes