Overview of Systems Biology / Biotechnology
Special attention is recently being paid to Systems Biology and Biotechnology as promising research and development paradigms for elucidating the complex biological processes and for achieving efficient biotechnological processes.
The central task of this area is to comprehensively collect the global cellular information such as omics-data, and to combine such data through complex biological processes, i.e., metabolic, signaling and regulatory networks, in order to generate predictive computational models of the biological system.
Thus, better understanding the cellular physiology, regulation and metabolism at the systems level and designing strategies for metabolic and cellular engineering of organisms are a prime target of this research.
Synthetic microbial alternatives for producing biofuels
This project aims to identify, study, innovate, and design novel microbial strains for efficiently producing biofuels such as bioethanol and biobutanol by integrating in silico systems approaches (modelling, simulation, and optimization) with in vivo synthetic biological techniques in the context of systems biotechnology. In addition, it will develop an integrated and optimized framework for effectively using these novel strains in industrial production of biofuele. The current approach expedite our progress by enabling us to design fewer, faster, more direct, and rational metabolic engineering experiments to overcome the well-known limitations of the conventional, try-and-see, costly, time-consuming experimental techniques.
Identification and prioritization of antimicrobial drug targets
The large-scale organization principles and functional properties of the biological systems (like how these molecules are interacting with each other to maintain the robustness of cellular functions) can be elucidated by analyzing the topological and dynamic properties of biological networks. A graph-theoretic approach and constraints-based flux analysis for the identification of multiple biochemical pathways can be promising for the analysis of the genome-wide networks. This would facilitate the discovery of antimicrobial drug targets that are essential for the growth or survival of a pathogen satisfying the principle of selective toxicity.
Investigation of pharmacodynamic and mechanistic effects of drug candidates
To date, numerous drug targets for various diseases have been identified at different levels in the signaling pathways. A major challenge is to explore such identified drug effects on the pharmacological modulation within the reconstructed pathways in order to evaluate the efficacy, safety and cost-effectiveness of drug targets available. The detailed molecular mechanisms of the reconstructed cell-signaling network could provide useful information for developing a rational approach to the quantification of the mechanistic effects of drugs and prioritizing and verifying drug candidates at the system-level.
Reconstruction and in silico analysis of metabolic and signaling network models
Genome-scale models of metabolic networks for microbial (e.g., E. coli) and mammalian (e.g., CHO) cells are reconstructed and then will be combined with transcriptional regulatory information. The simulation of systems behavior with the help of those mathematical models reconstructed allows us to characterize and predict the cellular behavior under genetic modifications and environmental changes. Similar procedure can be applied to the reconstruction and analysis of apoptotic and FGF-induced signaling pathways in CHO cells.
Systems glycomics and glycoengineering
Methodology and approaches are developed for identifying a target glycoprotein, thereby achieving a desired glycoform distribution of a recombinant protein in CHO cells. To this end, three steps are mainly involved.
- Mathematical modeling and simulation of glycosylation in CHO cells
- Management of various glycome data, e.g., gene expression, proteomic and glycoform profiles
- Combining the model with omics data at systems level
Development of bioinformatic and systems biological platforms
An integrated platforms are established for managing experimental data and exploring the static and dynamic behavior of intracellular metabolism in CHO cells. It can be achieved by systematically combining omics data analysis with in silico modeling. To achieve this goal through an integrated pipeline, four steps are mainly involved. They include:
- Developing data standards
- Integrated platform for managing and analyzing omics data
- Designing software architecture and Implementation of web applications
- Bioinformatic analysis of omics data