"Systems biology is the computational and mathematical analysis and modeling of complex biological systems."
The study of biological networks at different levels of organization, including molecular, cellular, and organismal, and the exploration of the properties of these networks and their implications for biological function.
Types of networks: Understanding different types of networks such as protein-protein interaction, gene regulatory, metabolic, and signaling networks is essential in network analysis.
Network topology: The study of network topology helps in understanding the structure of the network, the relationships between nodes and the overall organization of the system.
Network metrics: Metrics such as degree distribution, centrality, and modularity help in quantifying the properties of the network and making comparisons between networks.
Graph theory and algorithms: Understanding graph theory and different algorithms such as clustering, community detection, and network motif analysis helps in analyzing large-scale networks.
Data mining and machine learning approaches: Data mining and machine learning approaches help in analyzing large-scale omics data that are generated from high-throughput experiments.
Systems biology modeling: Building models helps in simulating biological networks and understanding the underlying biological processes that take place in the system.
Network evolution: Analysis of network evolution helps in understanding the dynamics of network growth, the emergence of new functionalities, and adaptation to changing environments.
Network inference: Inferring networks from high-dimensional omics data is essential in constructing network models and understanding the biological processes underlying the system.
Network visualization: Visualization of biological networks helps in comprehending complex interactions and identifying functional modules within the network.
Integrative data analysis: Integrating different types of data such as genetic, proteomic, and metabolic data helps in understanding the systems-level properties of a biological network.
Protein-protein interaction (PPI) network analysis: This type of network analysis involves mapping and analyzing protein interactions within a cell to identify functional modules and signaling pathways that play critical roles in cellular processes.
Gene regulatory network (GRN) analysis: This type of network analysis involves the identification of regulatory interactions between genes and transcription factors in a biological system. It helps to understand how genes are controlled and expressed, leading to insights into developmental and disease processes.
Metabolic pathway analysis: This type of network analysis involves the study of biochemical reactions and pathways that occur within a cell or organism. It helps to understand how metabolic processes are regulated and how they contribute to cellular function.
Signaling network analysis: This type of network analysis involves the study of cellular signaling pathways that regulate various cellular functions such as proliferation, differentiation, and apoptosis. It helps to identify key protein interactions and signaling mechanisms that contribute to the development and progression of diseases.
Structural network analysis: This type of network analysis involves the analysis of structural relationships between molecules, such as protein-protein, protein-DNA and protein-ligand interactions. It helps to understand the molecular basis of biological processes and can be used to design new drugs and therapies.
Multi-omics network analysis: This type of network analysis involves integrating multiple types of data, such as genomics, transcriptomics, proteomics, and metabolomics data. By combining data from multiple layers, it can provide a more comprehensive understanding of complex biological processes.
Boolean network analysis: This type of network analysis involves the construction of logical networks where each node represents a gene or protein and each edge represents a regulatory interaction. It helps to model complex systems and predict the outcomes of perturbations.
Co-expression network analysis: This type of network analysis involves analyzing gene expression data across multiple samples to identify groups of genes that are co-regulated. It can help to uncover novel gene functions and identify biomarkers for various diseases.
Evolutionary network analysis: This type of network analysis involves the study of the evolution of protein interactions and regulatory networks across species. It can help to understand the origins and evolution of biological complexity.
"It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research."
"Particularly from the year 2000 onwards, the concept has been used widely in biology in a variety of contexts."
"The Human Genome Project is an example of applied systems thinking in biology..."
"...which has led to new, collaborative ways of working on problems in the biological field of genetics."
"One of the aims of systems biology is to model and discover emergent properties..."
"...properties of cells, tissues, and organisms functioning as a system..."
"...whose theoretical description is only possible using techniques of systems biology."
"These typically involve metabolic networks or cell signaling networks."
"...complex biological systems."
"The computational and mathematical analysis..."
"...using a holistic approach..."
"...to model and discover emergent properties..."
"...leads to new, collaborative ways of working on problems in the biological field of genetics."
"...applied systems thinking in biology..."
"...more traditional reductionism..."
"It is a biology-based interdisciplinary field of study..."
"...complex interactions within biological systems..."
"...using a holistic approach (holism instead of the more traditional reductionism)..."
"...to model and discover emergent properties..."