Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.
A generegulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).
Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.
A biochemical cascade, also known as a signaling cascade or signaling pathway, is a series of chemical reactions that occur within a biological cell when initiated by a stimulus. This stimulus, known as a first messenger, acts on a receptor that is transduced to the cell interior through second messengers which amplify the signal and transfer it to effector molecules, causing the cell to respond to the initial stimulus. Most biochemical cascades are series of events, in which one event triggers the next, in a linear fashion. At each step of the signaling cascade, various controlling factors are involved to regulate cellular actions, in order to respond effectively to cues about their changing internal and external environments.
The Rat Genome Database (RGD) is a database of rat genomics, genetics, physiology and functional data, as well as data for comparative genomics between rat, human and mouse. RGD is responsible for attaching biological information to the rat genome via structured vocabulary, or ontology, annotations assigned to genes and quantitative trait loci (QTL), and for consolidating rat strain data and making it available to the research community. They are also developing a suite of tools for mining and analyzing genomic, physiologic and functional data for the rat, and comparative data for rat, mouse, human, and five other species.
Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology.
Reactome is a free online database of biological pathways. There are several Reactomes that concentrate on specific organisms, the largest of these is focused on human biology, the following description concentrates on the human Reactome. It is authored by biologists, in collaboration with Reactome editorial staff. The content is cross-referenced to many bioinformatics databases. The rationale behind Reactome is to visually represent biological pathways in full mechanistic detail, while making the source data available in a computationally accessible format.
The Biomolecular Object Network Databank is a bioinformatics databank containing information on small molecule structures and interactions. The databank integrates a number of existing databases to provide a comprehensive overview of the information currently available for a given molecule.
Biological network inference is the process of making inferences and predictions about biological networks. By using these networks to analyze patterns in biological systems, such as food-webs, we can visualize the nature and strength of these interactions between species, DNA, proteins, and more.
The Comparative Toxicogenomics Database (CTD) is a public website and research tool launched in November 2004 that curates scientific data describing relationships between chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, GO annotations, pathways, and interaction modules. The database is maintained by the Department of Biological Sciences at North Carolina State University.
BioPAX is a RDF/OWL-based standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data. Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.
In molecular biology, STRING is a biological database and web resource of known and predicted protein–protein interactions.
The ConsensusPathDB is a molecular functional interaction database, integrating information on protein interactions, genetic interactions signaling, metabolism, gene regulation, and drug-target interactions in humans. ConsensusPathDB currently includes such interactions from 32 databases. ConsensusPathDB is freely available for academic use under http://ConsensusPathDB.org.
In molecular biology short linear motifs (SLiMs), linear motifs or minimotifs are short stretches of protein sequence that mediate protein–protein interaction.
A biological pathway is a series of interactions among molecules in a cell that leads to a certain product or a change in a cell. Such a pathway can trigger the assembly of new molecules, such as a fat or protein. Pathways can also turn genes on and off, or spur a cell to move. Some of the most common biological pathways are involved in metabolism, the regulation of gene expression and the transmission of signals. Pathways play a key role in advanced studies of genomics.
TopFIND is the Termini oriented protein Function Inferred Database (TopFIND) is an integrated knowledgebase focused on protein termini, their formation by proteases and functional implications. It contains information about the processing and the processing state of proteins and functional implications thereof derived from research literature, contributions by the scientific community and biological databases.
Cancer systems biology encompasses the application of systems biology approaches to cancer research, in order to study the disease as a complex adaptive system with emerging properties at multiple biological scales. Cancer systems biology represents the application of systems biology approaches to the analysis of how the intracellular networks of normal cells are perturbed during carcinogenesis to develop effective predictive models that can assist scientists and clinicians in the validations of new therapies and drugs. Tumours are characterized by genomic and epigenetic instability that alters the functions of many different molecules and networks in a single cell as well as altering the interactions with the local environment. Cancer systems biology approaches, therefore, are based on the use of computational and mathematical methods to decipher the complexity in tumorigenesis as well as cancer heterogeneity.
In bioinformatics, a Gene Disease Database is a systematized collection of data, typically structured to model aspects of reality, in a way to comprehend the underlying mechanisms of complex diseases, by understanding multiple composite interactions between phenotype-genotype relationships and gene-disease mechanisms. Gene Disease Databases integrate human gene-disease associations from various expert curated databases and text mining derived associations including Mendelian, complex and environmental diseases.
The human interactome is the set of protein–protein interactions that occur in human cells. The sequencing of reference genomes, in particular the Human Genome Project, has revolutionized human genetics, molecular biology, and clinical medicine. Genome-wide association study results have led to the association of genes with most Mendelian disorders, and over 140 000 germline mutations have been associated with at least one genetic disease. However, it became apparent that inherent to these studies is an emphasis on clinical outcome rather than a comprehensive understanding of human disease; indeed to date the most significant contributions of GWAS have been restricted to the “low-hanging fruit” of direct single mutation disorders, prompting a systems biology approach to genomic analysis. The connection between genotype and phenotype remain elusive, especially in the context of multigenic complex traits and cancer. To assign functional context to genotypic changes, much of recent research efforts have been devoted to the mapping of the networks formed by interactions of cellular and genetic components in humans, as well as how these networks are altered by genetic and somatic disease.
