BioNumbers—the database of key numbersin molecular and cell biologyRon Milo1,*, Paul Jorgensen2,3, Uri Moran1, Griffin Weber4 and Michael Springer2

1Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel, 2Department ofSystems Biology, Harvard Medical School, Boston, MA 02445, USA, 3Donnelly Centre for Cellular andBiomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1 and 4Department of Medicine,Beth Israel Deaconess Medical Center, Boston, MA 02215, USA

Received July 16, 2009; Accepted October 2, 2009

ABSTRACT

BioNumbers (http://www.bionumbers.hms.harvard.edu) is a database of key numbers in molecularand cell biology—the quantitative properties of bio-logical systems of interest to computational,systems and molecular cell biologists. Contents ofthe database range from cell sizes to metaboliteconcentrations, from reaction rates to generationtimes, from genome sizes to the number of mito-chondria in a cell. While always of importance tobiologists, having numbers in hand is becomingincreasingly critical for experimenting, modeling,and analyzing biological systems. BioNumbers wasmotivated by an appreciation of how long it can taketo find even the simplest number in the vast biolog-ical literature. All numbers are taken directly froma literature source and that reference is providedwith the number. BioNumbers is designed to behighly searchable and queries can be performedby keywords or browsed by menus. BioNumbers isa collaborative community platform where regis-tered users can add content and make commentson existing data. All new entries and commentaryare curated to maintain high quality. Here wedescribe the database characteristics and imple-mentation, demonstrate its use, and discuss futuredirections for its development.

INTRODUCTION

Molecular biologists use numbers from the literatureconstantly: to plan experiments in the laboratory, toconduct thought experiments, and to construct andevaluate models. The increasing use of computationalmethods to capture the complexity of biological systemsonly enhances the importance of having access to suchvalues. Availability of numbers is crucial in transforming

our understanding of biological systems from qualitative,schematic arrows models to quantitative predictive models(1). Unfortunately for biologists of all stripes, findingnumbers in the vast literature can be an incredibly timeconsuming and frustrating experience. Even for propertiesthat have been measured numerous times, it can besurprisingly difficult to find the values, or even the orderof magnitude. In many cases, it does not necessarily sufficeto find a single measurement of this value, as one typicallywants to use the most relevant and most accurate mea-surement of this value that is available in the literature.Different measurements of the same property can beinconsistent, often reflecting subtle differences in thesystem being studied or the experimental details.

Biology does not have the kind of handbooks that areso common in engineering, physics, and chemistry con-taining material properties, universal constants, andother numbers of interest and use (2). Efforts to build arepository of quantitative data in molecular biology weremade in the 1970s, culminating in the three-volumeBiology Data Book (3). Even though it was planned tobe updated and expanded regularly, this book has notbeen continued and is by now effectively obsolete. Likethis inspiring predecessor, the BioNumbers databaseaddresses head-on the urgent need to quickly connectresearchers with numbers in molecular biology. The liter-ature has expanded enormously since the 1970s. Ratherthan relying solely on the limited expertise of our groupof curators, the BioNumbers database aims to utilizecurrent information technology to capture the knowledgeof the research community as a whole. Unlike ahandbook, BioNumbers is dynamic, being continuouslyupdated by curators and by engaged members of thescientific community.

THE DATABASE

The inconvenience to a single biologist of having to spendseveral hours searching textbooks and the Internet fora number may seem of trivial importance. But when

*To whom correspondence should be addressed. Tel: þ97289344466; Fax: þ97289344181; Email: ron.milo@weizmann.ac.il

D750–D753 Nucleic Acids Research, 2010, Vol. 38, Database issue Published online 23 October 2009doi:10.1093/nar/gkp889

� The Author(s) 2009. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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integrated over the whole of the biological researchcommunity, we would argue that such difficulties havenot only slowed progress but have also made molecularbiology less quantitative than it perhaps should be.Inspired by such considerations, we have createdBioNumbers (http://bionumbers.hms.harvard.edu/), thedatabase of key numbers in molecular and cell biology.The goal of BioNumbers is to quickly connect researchersto numbers available in the literature. Visitors to the sitecan search the database with text strings or by browsingthrough menus. Registered users can contribute entriesby providing information on values of interest that havebeen published in a peer reviewed journal. Users canalso provide commentary on numbers already in thedatabase. All numbers and commentary are curated toensure that entries are of a scholarly nature and tone.As all numbers are derived from the literature and arereferenced, entries are not a question of personal viewbut based on evidence, as judged according to the stan-dards of the scientific community.

BioNumbers currently contains >4500 distinct pro-perties from >200 organisms. The data were extractedmanually from >1000 separate references. Table 1depicts some of the main statistics of the database. Theorganisms with the most properties are, in decreasingorder: Escherichia coli, Homo sapiens, Saccharomycescerevisiae, Rattus norvegicus, Xenopus laevis, Musmusculus, Spinacia oleracea (spinach), Drosophilamelanogaster, and Caenorhabditis elegans. Someproperties are derived from less-specific groups, suchas biosphere, mammalian cells and plants. The datain BioNumbers are accessible through two differentsearch options: Free Text and Browse. The completedatabase can also be downloaded as a flat file at:http://bionumbers.hms.harvard.edu/resources.aspx. Eachof the >4500 properties in BioNumbers is described bythe fields shown in Table 2. Screenshots of theBioNumbers database are presented in Figure 1.

Two problems bedevil nearly any attempt to quantifya biological property: variability and experimentalconditions (e.g. Mg concentration, growth temperature,etc.). Variation is one of the pervasive aspects of biology.Indeed, even within a clonal population of genetically iden-tical cells there can be wide cell-to-cell variability for manyparameters. Variability makes the task of ascribing anumber to the properties of biological systems fraughtwith the danger of misinterpretation. In an attempt toaccount for such variability, BioNumbers records theranges and/or standard deviations reported for thenumber in question, whenever available. It is evident toany working scientist that the experimental method usedto measure a parameter can greatly influence the measured

value. To account for experimental influences on themeasured value, BioNumbers records can include experi-mental details, such as the environmental conditions underwhich cells were grown or which technique was used tomake the measurement. BioNumbers also allows formultiple entries of the same property to accommodate mea-surement of the same property by different groups ormethods. As each record in BioNumbers is directly refer-enced to a literature source, a researcher can quickly findout how the number was obtained.We feel that any caveatsarising from variability and experimental influences areusually far outweighed by the benefit of having an actualnumber. Indeed, even if a number is only accurate within anorder of magnitude, it can provide significant insight.What qualifies as a good BioNumber, one worth

entering into the database? This is hard to define preciselyand there is certainly no a priori limit on the size of thedatabase. We anticipate that as researchers engage withthe database, by requesting that certain numbers be foundby our curators or by adding these numbers themselves,that the database will fill with numbers important to adiversity of specialties in molecular and cell biology. Inseeding the database, we have initially focused on modelorganisms and on extreme cases. For instance, inproperties where the value is known for many organisms(e.g. number of chromosomes), we have chosen to createentries for common laboratory organisms and for theextreme cases that describe the limits of that property(e.g. the animals with the most and least chromosomes).We do not envision BioNumbers as a one-stop destina-

tion, but rather as the first stop in the search for biological

Table 2. Fields for BioNumbers entries

Field Description

ID A unique, automatically generated ID for everyBioNumber.

Property The property quantitated by the BioNumber.Organism Can be chosen from a drop down menu

consisting >50 organisms. Previouslyundefined organisms can be entered manually.Special groups are ‘generic’,‘unspecified’, ‘various’ and ‘biosphere’.

Value The value for the property.Units Use whatever unit is appropriate and/or

standard in the relevant field of study.If no units, then ‘Unitless’.

Range In cases where a range of values is availableor an associated standard error or deviation.

Reference The literature reference for the BioNumber.PubMed ID The ID number of the reference in PubMed.Entered By The user entering the BioNumber.Primary Source If the BioNumber was found in a book or review

that referenced a primary source,the primary literature source is entered.

Primary SourcePubMed ID

The ID number of the original primary referencein PubMed.

Measurementmethod

A short description of the method used to obtainthe number.

Keywords Words or brief phrases that help describe theBioNumber to aid searches.

Comments Any other pertinent information.Date added Date BioNumber was first entered.Date edited Date BioNumber was last updated.

Table 1. Database vital statistics

Number of entries �4500Number of distinct references �1000Number of organisms �200Unique visitors per day �150Searches performed in BioNumbers per month �4000

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numbers. In some cases, numbers for specific systems havealready been compiled on-line, and BioNumbers does notaim to replace such laudable projects. To the contrary, weaim to connect more researchers to such sites through theuse of ‘meta-BioNumbers’ that provide links to alreadyavailable on-line tables of useful numbers (e.g. theE. coli CyberCell Project, BNID 100599 or the spectraof pigments, BNID 101373). In addition, BioNumbers isnot intended to be a storehouse for high-throughput(HTP) data. Not only is HTP data well-serviced byother on-line repositories, such data are also typically ofa relative nature, while BioNumbers generally aimsto catalog absolute values, usually with measurementunits. BioNumbers contains links to HTP repositoriesvia meta-BioNumbers (e.g. BNID 100597, 100598).Thus, the BioNumbers database serves as a portal,directing researchers to numbers in the literature and toInternet resources rich in such numbers.

METHODS

The software is implemented as a Microsoft ASP.NET2.0 web application with a Microsoft SQL Server 2005database. The search is performed with built-in SQLcommands filtering each field separately and integratingthe results (hits) using flexible pre-defined weights to give afinal hit list. The data were collected via an extensive andongoing literature mining effort, as performed by the

BioNumbers team and the current community ofBioNumbers database users. The database is curated bythe BioNumbers team, as well as through the ability ofregistered users to comment on entries and thus facilitatecorrections and provide further details. Every databaseentry has a unique BioNumber identification number(BNID) that can also be searched. Entries can beupdated by the original contributor or by theadministrators. Every change is saved as a new versionwith the database showing the most current version bydefault but enabling views of older versions. Throughoutthe article, each time we invoke some particularBioNumber of interest, we reference its BioNumbers ID(e.g. BNID 102345).

Example of use: thought experiments and generatinghypotheses using BioNumbers

To show the power and usefulness of BioNumbers weaddress a specific thought experiment: What limits themaximal rate at which a bacterium can divide? That is,why does E. coli under ideal conditions of LB medium and37�C divide every �20min (BNID 100260) and not every�2min? Clearly the ability to divide at faster rates wouldprovide an overwhelming selective advantage, at least inlaboratory conditions. There are many cellular processesthat could potentially limit E. coli to a �20min doublingtime. But for most such processes, it seems possible for thebacterium to overcome the limitation by increasing the

Figure 1. Screenshot of the BioNumbers database website showing results for the search term ‘ribosome’.

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amount of the limiting factor, for instance by increasingthe number of nutrient transporters, the number of DNAreplication circles, or the number of RNA polymerasecomplexes. But ribosomes are an interesting partialexception to this rule. Ribosomes translate all theproteins in the cell including those that are assembledinto new ribosomes. Doubling ribosome content wouldnecessitate translating twice the number of ribosomalproteins. Here then is a potentially limiting rate: thetime that it takes a ribosome to translate enough aminoacids to copy itself (4). We demonstrate the use of theBioNumbers database with a brief analysis of theseconsiderations. An E. coli ribosome contains in total�7500 amino acids (7459, Search term: ‘ribosome’,BNID101175) and the translation rate is as high as�21 aa/sec (Search term: ‘translation ribosome’,BNID100059). Translating a single copy of all of theribosomal proteins thus minimally requires �7500/21� 400 sec� 7min. In order to make a new cell of thesame size, each ribosome must make a copy of itself.Taking into account essential translational cofactors likethe elongation factors EF-Tu and EF-G would increasethe required time to �9min. It therefore seems impossibleto obtain a cellular doubling time faster than �9min.Perhaps when further requirements for ribosome duplica-tion are taken into account, it will be evident why E. colidouble in �20min. We thus see that with simplecalculations and with several useful biological numberson hand, we can generate an intriguing hypothesisfor what sets a lower bound on the proliferation rateof E. coli.

FUTURE PLANS AND CONCLUSIONS

BioNumbers is a dynamic database that is beingcontinuously expanded and advanced. The support ofthe scientific community, in particular by providing newnumbers for addition to the database, will greatly increaseits usefulness. To expand on the database functionality ofBioNumbers, we are currently developing a ‘BioNumberof the month’ feature which will showcase BioNumbers ofspecial interest by describing how the number wasmeasured and its relevance to different fields of study.A future direction currently being considered is a com-parative table builder, in which tables would automati-cally be generated for users that specify a property andthe organisms of interest. These tables could facilitatecomparative studies and could lead to new insights intothe quantitative design principles of biological systems.Another future direction under consideration is agraphical interface that will visualize the relative scalesof entries that share the same units (e.g. velocities,volumes, concentrations). Such a tool would be reminis-cent of the well-known displays that visualize the progres-sion in length scale from molecules to galaxies.

Numbers are often peripheral components in contem-porary publications, relegated to the supplementary infor-mation because they are not central to the ‘narrative’.

Therefore, although great efforts are often required toaccurately and precisely measure properties in biology,there is generally little reward for such efforts. We there-fore envision the creation of a ‘Journal of BioNumbers’where authors could send short reports on importantnumbers that they have measured, with details on themethod and how it relates to previous measurements.This journal would be peer reviewed and edited toensure that only numbers, or sets of numbers, trulyworthy of addition to the scientific literature wereconsidered.Numbers are absolute and immutable entities. Biology

is built on adaptation, flexibility and variation. It is thusno surprise that concrete values for many biologicalproperties are hard to find. Most quantitative propertiesin biology depend on the context or the method of mea-surement, the organism and the cell type. Yet it is clearthat characteristic numbers and ranges are very usefultools to have available. Doing biology without knowingthe numbers can feel like learning history withoutknowing geography. BioNumbers is an integrated andpractical portal that will provide researchers with agreater ability to do biology by the numbers.

ACKNOWLEDGEMENTS

The authors thank Marc Kirschner, Rebecca Ward andthe Systems Biology department at Harvard MedicalSchool for providing a nurturing environment and finan-cial support. They also thank the Information Technologydepartment at Harvard Medical School, Afaq Husain andthe Zaztech development team, Yiftach Ravid and theEquivio team, Ricardo Vidal and the OpenWetWareteam at MIT, Jaime Prilusky and the bioinformatics unitat the Weizmann Institute of Science, Ruchi Chauhan,Ben Marks, Phil Mongiovi, David Osterbur, RobPhillips, Chris Sander, and Sudhakaran Prabakaran.

FUNDING

Systems Biology Department at Harvard MedicalSchool, and Weizmann Institute of Science, Israel, toBioNumbers. Funding for open access charge: Personalresearch funds of corresponding author.

Conflict of interest statement. None declared.

REFERENCES

1. Alon,U. (2006) An Introduction to Systems Biology: DesignPrinciples of Biological Circuits. Chapman & Hall/CRC, London.

2. Lide,D.R. (2008) CRC Handbook of Chemistry and Physics,89th edn. CRC press, Boca Raton FL.

3. Altman,P.L. and Katz,D.S.D. (1978) Biology Data Book, 2nd edn.John Wiley & Sons Inc, Bethesda, Maryland.

4. Cooper,S. (1991) Bacterial Growth and Division: Biochemistryand Regulation of Prokaryotic and Eukaryotic Division Cycles.Academic Press, San Diego, California.

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