An introduction to dynamical systems

Antiosteoarthritis activities of 70% ethanol extract of <em>eleutherine bulbosa</em> (mill.) urb. bulb on rats monosodium iodoacetate-induced osteoarthritis

Background: Osteoarthritis (OA) is a common degenerative joint situation that induces pain and disability in the elderly. Traditionally, Eleutherine bulbosa bulb from Pasuruan, East Java, is used to treat many diseases, also as an anti-inflammatory. Objective: In this research, we employed an in vivo model to examine the effects of 70% ethanol extracts of E. bulbosa (EBE) on the progression and development of OA. Methods: A singular intraarticular injection of Monosodium Iodoacetate (MIA) was used to create the OA model in rats. The progression of OA was observed for three weeks. Furthermore, treatment of EBE at a dose of 6, 12, and 24 mg/200g BW orally for four weeks was conducted to assess the effects on decreasing IL- 1ß level, joint swelling, and hyperalgesia. Results: Induction was successful, indicated by a significant difference (P<0.05) in decreasing latency time, increasing joint swelling, and IL-1ß level. EBE 24 mg/200 g BW treatment has significantly (P<0.05) reduced IL-1ß levels, joint swelling, and response to hyperalgesia. Conclusion: The 70% ethanol extract of E. bulbosa bulb has therapeutic effects on inflammation through reducing IL-1ß in experimental MIA-induced osteoarthritis in a rat model. According to this study, EBE may have an effective potential new agent for OA therapy.

Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments

In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals.

Systems Medicine: The Application of Systems Biology Approaches for Modern Medical Research and Drug Development

The exponential development of highly advanced scientific and medical research technologies throughout the past 30 years has arrived to the point where the high number of characterized molecular agents related to pathogenesis cannot be readily integrated or processed by conventional analytical approaches. Indeed, the realization that several moieties are signatures of disease has partly led to the increment of complex diseases being characterized. Scientists and clinicians can now investigate and analyse any individual dysregulations occurring within the genomic, transcriptomic, miRnomic, proteomic, and metabolomic levels thanks to currently available advanced technologies. However, there are drawbacks within this scientific brave new age in that only isolated molecular levels are individually investigated for their influence in affecting any particular health condition. Since their conception in 1992, systems biology/medicine focuses mainly on the perturbations of overall pathway kinetics for the consequent onset and/or deterioration of the investigated condition/s. Systems medicine approaches can therefore be employed for shedding light in multiple research scenarios, ultimately leading to the practical result of uncovering novel dynamic interaction networks that are critical for influencing the course of medical conditions. Consequently, systems medicine also serves to identify clinically important molecular targets for diagnostic and therapeutic measures against such a condition.

Advanced systems biology methods in drug discovery and translational biomedicine

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.