About us

Quick Intro

Computational Systems Biology aims to develop and apply efficient algorithms to address critical scientific questions through computer simulations and theoretical modeling. The system-wide modeling is particularly relevant in modern biological sciences, where the key challenge has shifted from the study of single molecules to the exhaustive exploration of molecular interactions and biological processes at the level of complete proteomes. Understanding how complex living systems work can help find treatments for disorders of poorly understood etiology, such as cancer and neurodegenerative disorders.

Our Vision

The major focus of our group is the design and development of novel tools for the modeling and analysis of biological networks using Computational Systems Biology. Briefly, Computational Systems Biology can be considered as a complex platform that integrates many algorithms from different research areas such as Structural Bioinformatics, Functional Genomics, Cheminformatics and Pharmacogenomics. We are interested in applying various Computational Systems Biology tools to study the evolution and organization of pathways into biological networks with the primary application in modern drug discovery and design. Biological pathways, which are the common units of biological networks, can be broadly defined as the series of interactions between molecular entities such as proteins, nucleic acids and small organic molecules that trigger a variety of cellular responses. Their malfunction can be often directly linked to many disease states. Our ambitious goal is to reveal the underlying principles of biological network evolution, organization and dynamics. By doing so, we hope to be able to predict the phenotypic outcome of biological network perturbations.

Requisite Components

csb01 Structural Bioinformatics. Construction of molecular structures of all gene products in a given proteome using highly accurate template-based modeling techniques.
csb02 Functional Genomics. Proteome-scale function inference using structure-based approaches that cover most of the functional aspects of proteins, including protein-DNA/RNA binding, protein-ligand, protein-metal and protein-protein interactions.
csb06 Cheminformatics. Large-scale ligand docking and virtual screening using structure/evolution-based modeling techniques.
csb04 Pharmacogenomics. Intersection of Pharmacology and Genomics that focuses on the interactions between genes, diseases and drugs.

Practical Applications

  • Network Biology. In a crowded cellular environment, numerous interactions occur between molecular species in a cell. They interact with each other in specific ways to perform their biological functions. The classical view of molecular interactions arranges them into biological pathways, which are often treated as independent functional entities; however, recent functional genomic experiments reveal very extensive cross-talks between pathways, suggesting a much more convoluted picture of molecular interactions in vivo. These inter-pathway connections are currently the subject of intense research since they are responsible for the extreme complexity of biological systems, yet are still poorly defined at the proteome level. It is hoped that the emergence of systems-level disciplines, such as Network Biology, will help uncover the organizing principles of the intercellular webs of interactions, which collectively control the behavior of a cell. We focus on various types of biological networks, which cover a broad spectrum of functionally relevant interactions:
Protein-ligand Protein-DNA Protein-metal Protein-protein  
csb03 csb07 csb05 csb08  
  • Network Pharmacology. Protein-drug interactome is of paramount importance for drug discovery; the field that is facing significant difficulties related to the binding promiscuity of drugs and the intricate networks of drug-target interactions, far more complicated than originally anticipated. Polypharmacology, also referred to as Network Pharmacology, rapidly emerges as a new trend in pharmaceutical research to challenge the problem of drug resistance and side-effects caused by compounds with unanticipated promiscuous binding properties. By tuning up molecular selectivity profiles, polypharmacology concentrates on multi-target drugs, which are selectively non-selective, i.e. target disease-related pathways or sub-networks rather than individual proteins.

Selected References

© Michal Brylinski
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