Computational Biology
Computational biology is an interdisciplinary field that applies mathematical and computational methods to analyze biological data. Computational biology
aims to understand the structure, function, evolution and interaction of biological systems at different levels of organization, from molecules to ecosystems.
Computational biology can also help to design and test new hypotheses, experiments and therapies in biotechnology and medicine.
Some of the main topics of computational biology include bioinformatics, genomics, proteomics, metabolomics, systems biology, phylogenetics,
structural biology and synthetic biology.
Bioinformatics is the application of computational methods to analyze biological data. It involves the use of algorithms, databases, software tools and statistical techniques to process, store and interpret information from various sources such as DNA sequences, protein structures, gene expression and metabolic pathways. Bioinformatics can help answer questions such as how genes function, how diseases develop and how organisms evolve.
Genomics is the study of the structure, function, and evolution of genomes, which are the complete sets of genetic information in an organism. Genomics can help us understand the molecular basis of life, disease, and diversity. Genomics can also enable the development of new technologies and applications in fields such as medicine, agriculture, biotechnology, and environmental science.
Proteomics is a field of science that studies the structure, function, and interactions of proteins in living organisms. Proteins are essential for many biological processes, such as metabolism, signaling, immunity, and gene expression. Proteomics uses various techniques, such as mass spectrometry, protein purification, and bioinformatics, to identify and quantify proteins and their modifications. Proteomics can provide insights into the molecular mechanisms of diseases, drug targets, biomarkers, and cellular pathways.
Metabolomics is the study of the chemical processes that occur in living organisms, such as the production and degradation of metabolites. Metabolomics can provide insights into the metabolic state, function, and regulation of cells, tissues, and organs. Metabolomics can also be used to identify biomarkers, discover new pathways, and understand disease mechanisms.
Phylogenetics is the study of the evolutionary history and relationships among different groups of organisms. It uses various methods, such as molecular sequences, morphological traits, and fossils, to infer the patterns of descent and divergence among taxa. Phylogenetic trees are graphical representations of these relationships, showing the branching order and relative time of speciation events. Phylogenetics has many applications in biology, such as understanding the origin and diversity of life, reconstructing the ancestral features of organisms, and testing hypotheses about adaptation and coevolution.
Transcriptomics
Transcriptomics is the study of the transcriptome, which is the complete set of RNA transcripts produced by the genome of an organism or a cell. Transcriptomics can reveal the expression levels of genes and their regulation under different conditions, such as development, stress, disease, or treatment. Transcriptomics can also identify novel transcripts, such as non-coding RNAs, alternative splicing variants, and fusion genes. Transcriptomics relies on high-throughput sequencing technologies, such as RNA-seq, to capture and analyze the transcriptome data.
Spatial Transcriptomics
Park HE, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. Adv Sci (Weinh). 2023 Apr 7:e2206939. doi: 10.1002/advs.202206939. Epub ahead of print. PMID: 37026425.
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to “in situ” biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.