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Bioinformatics glossary & taxonomy
Evolving terminologies for emerging technologies

Suggestions? Comments? Questions?
Mary Chitty MSLS
Last revised October 04, 2019

The definition of bioinformatics is not universally agreed upon. Generally speaking, we define it as the creation and development of advanced information and computational technologies for problems in biology, most commonly molecular biology (but increasingly in other areas of biology). As such, it deals with methods for storing, retrieving and analyzing biological data, such as nucleic acid (DNA/RNA) and protein sequences, structures, functions, pathways and genetic interactions.  Some people construe bioinformatics more narrowly, and include only those issues dealing with the management of genome project sequencing data. Others construe bioinformatics more broadly and include all areas of computational biology, including population modeling and numerical simulations.  Biomedical informatics is a slightly broader umbrella that includes not only bioinformatics, but other areas of informatics in biology, medicine and health-care. They are closely related.  Russ Altman "Guide to informatics at Stanford University,  2006 

Systems biology and cellular and physiological biology informatics are in this Bioinformatics glossary

Related glossaries: Informatics: Algorithms & data analysis, ChemoinformaticsClinical informatics   
Drug discovery informatics  Genomic informatics   IT infrastructure   Protein informatics   See Genomic Informatics for data analysis using these technologies, as well as gene specific informatics. Technologies  Microarrays  Sequencing    Biology: DNA, Expression, Proteins, Sequences, DNA & beyond   Related glossaries include  Drug Discovery & Development,  Functional genomics 

Bio-IT World April 21-23 2020, Boston MA   The Bio-IT space right now is rife with hype. Blockchain, AI, machine learning, data science, deep learning, edge, IoT, and more are being touted as panaceas, sure to at least facilitate a cure for what ails you. There are some legitimately cool technologies maturing in our space, but there is also plenty of smoke and mirrors designed to conceal the tech growing pains.

annotation:  The annotation process identifies sequence features on the contigs such as variation, sequence tagged sites, FISH-mapped clone regions, transcript alignments, known and predicted genes, and gene models. This stage provides contig, RNA, and protein records with added feature annotation. In addition, organism specific features, such as Gene Trap clones for mouse will also be annotated.. NCBI Annotation Information 2008 

The value of a genome is only as good as its annotation. At the Sanger Institute, we are providing high quality manual curation in addition to automated prediction provided by Ensembl. Finished genomic sequence is analysed on a clone by clone basis using a combination of similarity searches against DNA and protein databases as well as a series of ab initio gene predictions. Manual Curation of the Human Genome, Wellcome Trust, Sanger Institute, 2003  archived

Each fragment of DNA contains unique features. A DNA fragment may encode a portion of a gene or a gene control sequence, or the fragment may be a portion of a genome that has no apparent function. Bioinformaticists perform detailed analysis of DNA fragments, comparing new DNA sequence, previously annotated DNA sequences and identifying common characteristics, and assigning known or putative potential functions to the DNA sequence. Cross species DNA sequence comparison is quite common and can reveal common genes shared between organisms. A bioinformatic study may also require peptide to peptide comparisons allowing common structural features of proteins to define the function a DNA fragment encoding a specific protein or enzyme.  Explanatory notes, comments, analysis and commentaries added to a database. May refer to sequence data or protein structures and includes predictions, characterizations, summaries, and other detailed information, including gene function. Annotation can be manual (as in SWISS- PROT) or automated (as in TrEMBL).  Since annotation is highly skilled and labor intensive, efforts are being made to automate the process, at least for preliminary data. Related terms: annotated databases, curated databases, comparative genome annotation,  distributed annotation system, genome annotation; SNPs & genetic variations Genetic Annotation Initiative  Narrower terms: baseline annotation, computational annotation, distributed sequence annotation; Proteomics: annotation - proteins

big data:   Bioinformatics for Big Data  March 11-13, 2019 • San Francisco, CA   Program | Creating Clinically Actionable Data In the era of precision medicine, enormous amounts of data are being generated from disparate sources including omics, imaging, sensing and beyond. Today, computational scientists need to develop better tools to manage, integrate and share data to make it clinically actionable

Bioinformatics for Big Data

the term "Big Data" is meant to capture the opportunities and address the challenges facing all biomedical researchers in releasing, accessing, managing, analyzing, and integrating datasets of diverse data types.  Such data types may include imaging, phenotypic, molecular (including –omics), clinical, behavioral, environmental, and many other types of biological and biomedical data.  They may also include data generated for other purposes (e.g., social media, search histories, and cell phone data).  The datasets are increasingly larger and more complex, and exceed the abilities of currently-used approaches to manage and analyze them.  Biomedical Big Data primarily emanate from three sources: 1) a few groups that produce very large amounts of data, usually as part of projects specifically funded to produce important resources for the research community; 2) individual investigators who produce large datasets for their own projects, which might be broadly useful to the research community; and 3) an even greater number of investigators who each produce small datasets whose value can be amplified by aggregating or integrating them with other data. Centers of Excellence for Big Data Computing in the Biomedical Sciences (U54), July 2013

BioConductor:  An open source and open development software project to provide tools for the analysis and comprehension of genomic data (bioinformatics). 

Bioinformatics  April 17-18, 2019 • Boston, MA | case studies using computational resources and tools that take data from multiple –omics sources and align it with clinical action. Turning big data into smart data can lead to real time assistance in disease prevention, prognosis, diagnostics, and therapeutics. With the ever-increasing volume of information generated for curing or treating diseases and cancers, bioinformatics technologies, tools and techniques play a critical role in turning data into actionable knowledge to meet unstated and unmet medical needs.
Track 4: Bioinformatics

The Bioinformatics and Computational Biology program, which supports the National Centers for Biomedical Computing, aims to develop novel, cutting-edge software and data management tools to effectively mine the vast wealth of biomedical data generated from sophisticated modern laboratory techniques and facilitate data sharing between researchers. NIH Common Fund 

Roughly, bioinformatics describes any use of computers to handle biological information. In practice the definition used by most people is narrower; bioinformatics to them is a synonym for "computational molecular biology" - the use of computers to characterise the molecular components of living things. Damian Counsell, FAQ]

See above FAQ for tight and loose definitions of bioinformatics, and information on how long the term has been used. 

We have coined the term Bioinformatics for the study of informatic processes in biotic systems. Our Bioinformatic approach typically involves spatial, multi- leveled models with many interacting entities whose behavior is determined by local information. Theoretical Biology Group, Univ. of Utrecht, Netherlands, Paulien Hogeweg Director  

Original definition was “the study of informatic processes in biotic systems” Paulien Hogeweg MIRROR beyond MIRROR, puddles of LIFE, in Artificial Life, ed. C.G. Langton, Addison Wesley, 297-316, 1988

The earliest Medline reference I've found to bioinformatics is William Bain's "Bioinformatics in Europe - the federation strikes back" in Trends in Biotechnology 11(6): 217- 218 June 1993. 
Narrower terms: bacterial bioinformatics, comparative bioinformatics, functional bioinformatics, glycobioinformatics, medical bioinformatics, molecular bioinformatics, pharmaceutical bioinformatics, protein bioinformatics; Protein informatics structural bioinformatics;  Related terms: European Bioinformatics Institute EBI, Open Bioinformatics Foundation; Algorithms  data mining
Carole Goble, Seven Deadly Sins of Bioinformatics, 2007 

Bioinformatics information resources poster Mary Chitty presented at BioIT World 2014 BioJava is open-source and entirely hosted on GitHub. You can check the code, submit issues and pull requests and download latest and past release binaries from GitHub. .

biological computing: Wikipedia  

biological databases: Biological databases have inherent complications stemming from the nature of the information they contain and the dependence of computational methods on these data. Most biological data are not digital, making machine- readability of the data (for automated data- mining) impossible. In addition, the lack of standardized nomenclature and ontology, the use of protein aliases (leading to ambiguity), the lack of interoperability across databases, and the presence of errors in database annotations have hindered and complicated the use of computational methods. Defining the Mandate of Proteomics in the Post- Genomics Era, Board on International Scientific Organizations, National Academy of Sciences, 2002

bioMOBY: An international group of biological data hosts, biological data service providers, and coders whose aim is to set standards for biological data representation, distribution, and discovery. Natural language processing of biology text. Bob Futrelle, Computer Science, Northeastern Univ., US

BioPax:  Biological Pathways Exchange.  A collaborative effort to create a data exchange format for biological pathway data.  Related terms: metabolic pathways An international association of developers of open source Perl tools for bioinformatics, genomics and life science research. We work closely with our friends and colleagues at, and The Bioperl server provides an online resource for modules, scripts, and web links for developers of  Perl- based software for life science research. An international association of developers of freely available Python tools for computational molecular biology. provides an online resource for modules, scripts, and web links for developers of Python- based software for life science research.

biosemiotics: according to the basic types of semiosis under study, biosemiotics can be divided into vegetative semiotics (also endosemiotics, or phytosemiotics), the study of semiosis at the cellular and molecular level (including the translation processes related to genome and the organic form or phenotype);[3][4] vegetative semiosis occurs in all organisms at their cellular and tissue level; vegetative semiotics includes prokaryote semiotics, sign-mediated interactions in bacteria communities such as quorum sensing and quorum quenching. zoosemiotics or animal semiotics,[5] or the study of animal forms of knowing;[6] animal semiosis occurs in the organisms with  neuromuscular  system, also includes anthroposemiotics, the study of semiotic behavior in humans. According to the dominant aspect of semiosis under study, the following labels have been used: biopragmatics, biosemantics, and biosyntactics.
Wikipedia accessed 2018 Oct 18  

BISTI Consortium: The Biomedical Information Science and Technology Initiative is a consortium of representatives from each of the NIH institutes and centers. ...  The mission of BISTI is to make optimal use of computer science and technology to address problems in biology and medicine by fostering new basic understandings, collaborations, and transdisciplinary initiatives between the computational and biomedical sciences.

comparative systems biology: My research projects in comparative systems biology have four main thrusts: whole-genome functional annotation, multi-clustering of molecular profiles, cross-condition analysis of functional genomics data, and computationally-driven design of biological experiments. The research I am conducting with my life science colleagues in comparative systems biology has the goal of providing precise functional annotations to hypothetical genes in model organisms and in newly-sequenced genomes; delineating similarities and differences in cellular networks activated in different diseases; identifying core cellular pathways common to response networks for multiple stresses in various model organisms; and refining our understanding of the molecular basis of disease resistance in plant-pathogen interactions. Research interests, TM Murali, Computer Sciences, Virginia Tech,    Broader term: systems biology  

computational annotation: The workshop began with a series of presentations on computational annotation and experimental approaches to biological confirmation of functional elements in the genomes of both model organisms and the human. Subsequent to those discussions, NHGRI outlined its proposal for a pilot project to exhaustively determine all functional elements in a small fraction (~1 percent) of the human genome, Initial Inventory of Functional Elements to Identify: The participants recommended that both protein- coding genes and non- protein- coding genes need to be identified. For each of these, the complete (full- length) coding sequence and all variants, as well as the transcriptional regulatory elements (e.g., promoters and enhancers) and post- transcriptional regulatory elements (e.g. cis- acting RNA elements) should be described. All pseudogenes should be identified. A number of global sequence features, such as sites of methylation, sequence variation, evolutionary history of sequence blocks and repetitive elements were suggested for inclusion, as were a number of chromosomal elements, such as origins of replication, nuclease hypersensitive sites, matrix attachment sites and histone modifications. Workshop on the Comprehensive Extraction of Biological Information from Genomic Sequence, Bethesda, Md. July 23-24, 2002,

computational annotation technologies: Several ‘wet bench’ technologies and resources were discussed. These included DNA array studies, RT-PCR/ cDNAs, in situ hybridization, chromatin immunoprecipitation, RNAi, knockout mice, and antibody analysis of protein function. A broad range of computational approaches were also considered to be critical for inclusion. These included both comparative sequence analysis of multiple genomic sequences to identify conserved elements and automated prediction of functional elements, including coding sequences, promoters, alternative splice variants and other highly conserved regions. The importance of ensuring close collaboration between experimental and computational approaches was stressed. Workshop on the Comprehensive Extraction of Biological Information from Genomic Sequence, Bethesda, Md. July 23-24, 2002,

computational biology: The development and application of data - analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of biological, behavioral, and social systems. Biomedical Information Science and Technology Initiative BISTI Bioinformatics at the NIH, 2000 

I find that people use "computational biology" when discussing that subset of bioinformatics (in the broadest sense) closest to the field of classical general biology.  Computational biologists interest themselves more with evolutionary, population and theoretical biology rather than cell and molecular biomedicine. It is inevitable that molecular biology is profoundly important in computational biology, but it is certainly not what computational biology is all about (see next paragraph). In these areas of computational biology it seems that computational biologist's have tended to prefer statistical models for biological phenomena over physico- chemical ones. This is often wise...   One computational biologist (Paul J Schulte) did object to the above and makes the entirely valid point that this definition derives from a popular use of the term, rather than a correct one. Paul works on water flow in plant cells and points out that biological fluid dynamics is a field of computational biology in itself - and this, like any application of computing to biology, can be described as computational biology... Where we disagree, perhaps, is in his conclusion from this - which I reproduce in full: "Computational biology is not a "field", but an "approach" involving the use of computers to study biological processes and hence it is an area as diverse as biology itself."  Richard Durbin, Head of Informatics at the Wellcome Trust Sanger Institute, expressed an interesting opinion on this distinction in an interview on this distinction:  "I do not think all biological computing is bioinformatics, e.g. mathematical modelling is not bioinformatics, even when connected with biology- related problems. In my opinion, bioinformatics has to do with management and the subsequent use of biological information, particular genetic information."  [Damian Counsell, FAQ, 2001]

A field of biology concerned with the development of techniques for the collection and manipulation of  biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories applicable to molecular biology and areas of computer- based techniques for solving biological problems including manipulation of models and datasets.  MeSH, 1997  Related terms: protein informatics  

conceptual biology: 
As we see it, is not a distinct type of science, but rather it has a different source: the information in databases... By logical, critical analysis of existing facts and models, one can generate a hypothesis in which predictions are formulated in testable terms, and then search for relevant information among published reports of experiments that may have had a different purpose altogether. MG Blagosklonny and AB Pardee, Unearthing the gems: Conceptual Biology, Nature 416 (6879): 373, 28 March 2002

The iterative process of analysing existing facts and models available in published literature to generate new hypotheses. Julie C. Barnes, Conceptual biology: a semantic issue and more, Nature 417(6889): 587-588, 6 June 2002  Related terms: Research  meta-analyses, meta- analysis

curated databases: Often less complete than primary databases, but they have less redundancy and the added value of scientific annotation; therefore, a biologically significant sequence should be easier to find in such a database and of greater value. Naturally, the degree of redundancy and annotation in such a database depends on the experience, skills, aims, and devotion of its curators.  ...  The only proper way to curate databases is the way groups like those that developed OMIM [Online Mendelian Inheritance in Man], SWISS- PROT and most commercial databases have done it — that is, through making scientific judgments as data are cleaned up and merged.   Under the supervision of a curator. Other curated databases include LocusLink, RefSeq, & SGD (Saccharomyces cerevisae Genome Database) 

databases: Collections of data in machine- readable form, which can be manipulated by software to appear in varying arrangements and subsets. 

Genetic information is stored in different ways in different databases, which makes it hard to compare their holdings. So while computational biologists are trying to improve the quality of the databases, they are also working to build bridges between them.  So far, they have had only limited success … each database has its own Web site with unique navigation tools and data storage formats that make such searching difficult … programs can’t easily recognize data that are not stored in a uniform way. Elizabeth Pennisi “Seeking Common language in a Tower of Babel” Science: 449 Oct. 15 1999

distance functions or similarity scores: The key issue in comparing expression profiles is deciding what it means for two profiles to be "similar." Mathematically, we need a function that takes two expression profiles and calculates a similarity score. It is sometimes easier to work with the opposite concept of distance, and people often speak of distance functions instead of similarity scores. Many similarity or distance functions are used in microarray work, and there is no consensus as to which one is best. Narrower terms: Euclidean distance, Pearson correlation

distributed annotation system:  A client- server system in which a single client integrates information from multiple servers. It allows a single machine to gather up genome annotation information from multiple distant web sites, collate the information, and display it to the user in a single view. Little coordination is needed among the various information providers.

dynamic modeling: Mathematical approaches to studying biological variation have changed little in several decades. There is a need to develop new dynamic models to illuminate how systems interact and evolve. Just as important, it is critical to study the nature of biological and mathematical assumptions of models and statistics. Tools for analyzing and interpreting data on the architecture of complex phenotypes should be developed in the context of real biological information. Genetic Architecture, Biological Variation and Complex Phenotypes, PA-02-110, May 29, 2002- June 5, 2005

Euclidean distance: Commonly used distance function, which works by treating each expression profile as defining a point in a multidimensional space. 

European Bioinformatics Institute EBI, Hinxton, Cambridge, UK. An EMBL outstation.

functional bioinformatics:  An emerging subfield of bioinformatics that is concerned with ontologies and algorithms for computing with biological function. Functional bioinformatics is the computational counterpart of functional genomics ...  is concerned with managing and analyzing functional genomics data, such as gene expression experiments and large- scale knock- out experiments. .. emphasizes large- scale computational problems, such as problems involving complete metabolic networks and genetic networks.  Peter D. Karp "An ontology for biological function based on molecular interactions" Bioinformatics Ontology 16 (3): 269- 285, 2000
Related terms: Functional genomicsMetabolic Engineering  Ontologies & taxonomies  

glycobioinformatics, glycoinformatics: Glycosciences

LSID Life Sciences Identifiers:  Cover pages  

life sciences informatics: Informatics are essential at every step of genomics- based drug discovery and development. The commercial landscape of life sciences information technology has changed dramatically in recent years. Bioinformatics, in particular, has gone through a dramatic boom/bust. While IT companies are looking to the drug discovery and development arena as a new market opportunity, pharmaceutical companies  are faced with rising pressure to reduce (or at least control) costs, and have a growing need for new informatics tools to help manage the influx of data from genomics, and turn that data into tomorrow's drugs. Key IT tools, such as high- performance computing, Web services, and grids, are being used to improve the speed and efficiency of drug discovery and development. True breakthroughs are still lacking, particularly in key areas such as gene prediction, data mining, protein structure modeling and prediction, and modeling of complex biological systems. However, most experts agree that IT and bioinformatics are essential to reaching the improved productivity the pharmaceutical industry craves.  

molecular bioinformatics: Conceptualizing biology in terms of molecules (in the sense of physical- chemistry) and then applying "informatics" techniques (derived from disciplines such as applied math, CS [computer science] and statistics to understand and organize the information associated with these molecules on a large- scale. Mark Gerstein "What is Bioinformatics?" MB&B 474b3, 2001

molecular information theory: Schneider TD. A brief review of molecular information theory. Nano communication networks. 2010;1(3):173-180. doi:10.1016/j.nancom.2010.09.002.

molecular systems biology: An integrative discipline that seeks to explain the properties and behaviour of complex biological systems in terms of their molecular components and their interactions.  Nature Publishing, Molecular Systems Biology aims & scope   Broader term: systems biology

NCBI  National Center for Biotechnology Information: Established in 1988 as a national  resource for molecular biology information, NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information - all for the better understanding of molecular processes affecting human health and disease. Part of  NIH.

non-redundant databases: Researchers at the National Center for Biotechnology Information (NCBI) coined the term "nr" database (nonredundant database) to refer to a database in which the obviously redundant entries have been merged. These entries are typically those that are 100%, character- by- character identical, and algorithms exist that can remove such redundancy. Although such a database has less redundancy than a primary database, a substantial amount of redundancy remains, and it can be removed only by a curator using scientific judgment.

Many databases try to be “non-redundant”.  Unfortunately, biological data is too complex to fit a simple definition of redundancy … Each “non- redundant” database has its own definition of redundancy. George Church Lab, Harvard Medical School, US   Examples of non- redundant databases include UniGene and SWISS- PROT, while DDBJ/ EMBL/ GenBank are redundant databases.

Open Bioinformatics Foundation OPEN-BIO: The purpose of the foundation is to act as an umbrella organization for the various bio*.org projects that grew out of the original BioPerl project. The goal of the foundation is to provide financial, administrative and technical assistance for our various open source life science projects.  Narrower terms:,,, Related term:

prediction: Narrower terms: exon prediction, gene prediction, ORF prediction, protein sequence prediction;  Protein informatics protein structure prediction; Related terms: recognition

proprietary databases:  Fee- based, copyrighted databases (in contrast to public databases such as those at DDBJ/ EMBL/ GenBank). Some databases charge subscription fees to commercial organizations, with other arrangements available to non- profits.. Also referred to as private databases.  Compare: public databases

public databases: Freely accessible databases such as GenBank/ EMBL/ DDBJ, ArrayExpress or BLOCKS. There has been much debate about public vs. proprietary databases. 

recognition: Narrower terms: computational gene recognition, gene recognition, molecular recognition. recognition site: Pharmaceutical biology

spatio temporal dynamics: Local interactions in space can give rise to large scale spatio temporal patterns (e.g. (spiral) waves, spatio- temporal chaos (turbulence), stationary (Turing- type) patterns and transitions between these modes). Their occurrence and properties are largely independent of the precise interaction structure. They are indeed seen to occur at many organizational levels of biotic systems. Space can be either 'real' space or a state space, e.g. 'phenotype space' in models of speciation or 'shape space' in immunological models of shape- based receptor interactions. We show that such spatio- temporal patterns have important consequences for fundamental bioinformatic processes. Paulien Hogeweg, Overview of Research 1993- 1998, Utrecht University, Netherlands, 1999

standards: Related terms: Bio-ontology Standards Group, CORBA
, Data Model Standards Group, object protocol model OPM . EBI [European Bioinformatics Institute] is also working on standards. Microarrays MAML, MGED, MIAMI   

systems biology: NIGMS views "systems biology" as a conceptual framework for the analysis of complex biological systems. Such systems derive from interactions among many distinct components in varying contexts. These systems exhibit properties, such as nonlinear dynamics and emergent behavior, that cannot easily be inferred from studies of components in isolation. Systems biology relies on mathematical methods and computational models to generate hypotheses and to design new experiments. Iteration between theory and experiment is crucial. The quantity and quality of data required for these approaches often challenge current technologies, and the development of new technologies and cross-disciplinary collaborations may be required. When applied to human health, systems biology can be a powerful tool to test hypotheses relevant to health and disease, particularly the results of therapeutic interventions.  National Centers for Systems Biology

The label “systems biology” is pretty awful, except, of course, for the many even worse labels that have been tried. More important is what SB seeks to do: transform biology and health care into a rigorous, predictive science offering a richer understanding of biology and a vastly improved approach to drug development and medicine. SB would build on the molecular biology revolution and elucidate the wiring diagrams (and their rules) buried in the data.   John Russell, BioIT World, Sept  2007 

Systems biology is frequently defined as the study of all of the elements in a biological system and their relationship to one another in response to perturbation. Advances in science and technology are enabling the development of this emerging and cross-disciplinary field by allowing researchers to explore how biological components function as a network in cells, tissues and organisms. Recently, pharmaceutical companies have begun to embrace systems approaches in an effort to better understand physiology, pathogenic processes and pharmacological responses. This review focuses on recent advances within three core areas of systems biology: data collection, data analysis, and the integration and sharing of data.  Susie Stevens and J. Rung, Advances in systems biology: measurement, modeling and representation, Current Opinion in Drug Discovery and Development, 2006 Mar; 9(2): 240- 250.

Systems biology has been responsible for some of the most important developments in the science of human health and environmental sustainability. It is a holistic approach to deciphering the complexity of biological systems that starts from the understanding that the networks that form the whole of living organisms are more than the sum of their parts. It is collaborative, integrating many scientific disciplines – biology, computer science, engineering, bioinformatics, physics and others – to predict how these systems change over time and under varying conditions, and to develop solutions to the world’s most pressing health and environmental issues.  This ability to design predictive, multiscale models enables our scientists to discover new biomarkers for disease, stratify patients based on unique genetic profiles, and target drugs and other treatments. Systems biology, ultimately, creates the potential for entirely new kinds of exploration, and drives constant innovation in biology-based technology and computation.   Institute for Systems Biology

There are basically two approaches to 
systems biology. One has its roots in biology, the other in systems theory. The former sees it as a way to integrate data from a variety of sources. For the latter, the main idea is that the methods developed in those fields might also have a useful application in biology, since engineering
sciences have a tradition of borrowing from natures design principles. Only recently, the prospect of 'designing' biological systems has become feasible. Currently this is mostly done by 'improving' plants or animals by adding genes from other organisms, but first simple from-scratch designs of biological functional modules are starting to appear. Examples are designed cells as thermometers and oscillators which are independent of the cell cycle. Even before all this became possible, though, the possibility of using engineering methods to assist in 'reverse engineering nature' had a certain appeal. Glossary for Systems Biology, Univ of Stuttgart

The very nature of systems biology requires integrating data from a variety of sources generated and interpreted by people skilled in different areas --  engineering, computer science, biology, physics, mathematics, and statistics. Key considerations in this process include the generation of quantitative data, barriers in communication across departments, and organizational challenges.

Glossary for systems biology
systems biology has been around - under different names - for quite some time… This is not the first attempt at systems-level analysis of biological systems; there have been several efforts in the past, the most notable of which is cybernetics, or biological cybernetics, proposed by NORBERT WIENER. As shown in the historical review (see Chapter History), those earlier attempts did provide solutions for special problems, but were bound to fail as a 'real' systems biology because of the lack of understanding of molecular biology at the time and insufficient data due to deficiencies in measurement techniques [31]. Today's advances in measurement, data acquisition and handling technologies provide a wealth of new data which can be used to improve existing models. That data can be divided into four categories or key properties: system structures, system dynamics, control methods, and design methods [34]. Progress in these areas requires ``breakthroughs in our understanding of computational sciences, genomics, and measurement technologies, and integration of such discoveries with existing knowledge'' [34]. (see Fig. 2.1)

Institutes for System Dynamics and Control and for Systems Theory in Engineering of the University of Stuttgart 2011
Narrower terms: comparative systems biology, molecular systems biology; hepatocyte systems biology, semantic systems biology ;  In silico & molecular modeling applied systems biology, in silico biology ; Metabolic engineering signal transduction Pharmaceutical biology integrative biology-  

The researcher defines minimum and maximum values that are considered reliable; measurements that are too low or too high are dropped from the dataset or marked as unreliable. It also makes sense to subtract the minimum value from all other measurements, because this reflects baseline noise. This approach implicitly assumes that microarrays normally operate in the linear part of the dynamic range, and that the transitions between the linear and flat regimes occur abruptly. Broader term: normalization   translational bioinformatics: it is clear that many exciting and emerging health topics are squarely within the scope of translational bioinformatics: cancer, pharmacogenomics, medical genetics, small molecule drugs, and diseases of protein malfunction. There is an unmistakable flavor of personalized medicine here as well (genome association studies, mining genetic markers, personal genomic data analysis, data mining of electronic records): our molecular and clinical data resources are now allowing us to consider individual variations, and not simply population averages.   Altman RB (2012) Introduction to Translational Bioinformatics Collection. PLoS Comput Biol 8(12): e1002796., 2012

Bioinformatics resources 
EMBL-EBI Bioinformatics Services, European Molecular BIology Lab, European Bioinformatics Institute 

How to look for other unfamiliar  terms

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