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WholeCellKB: model organism databases for
comprehensive whole-cell models
Jonathan R. Karr
1
, Jayodita C. Sanghvi
2
, Derek N. Macklin
2
, Abhishek Arora
3
and
Markus W. Covert
2,
*
1
Graduate Program in Biophysics,
2
Department of Bioengineering and
3
Department of Electrical Engineering,
Stanford University, 318 Campus Drive West, Stanford, CA 94305, USA
Received August 15, 2012; Revised October 1, 2012; Accepted October 19, 2012
ABSTRACT
Whole-cell models promise to greatly facilitate the
analysis of complex biological behaviors. Whole-
cell model development requires comprehensive
model organism databases. WholeCellKB (http://
wholecellkb.stanford.edu) is an open-source web-
based software program for constructing model
organism databases. WholeCellKB provides an ex-
tensive and fully customizable data model that fully
describes individual species including the structure
and function of each gene, protein, reaction and path-
way. We used WholeCellKB to create WholeCellKB-
MG, a comprehensive database of the Gram-positive
bacterium Mycoplasma genitalium using over 900
sources. WholeCellKB-MG is extensively cross-
referenced to existing resources including BioCyc,
KEGG and UniProt. WholeCellKB-MG is freely
accessible through a web-based user interface as
well as through a RESTful web service.
INTRODUCTION
A primary challenge in computational biology is to predict
how complex phenotypes such as growth and replication
arise from networks of individual molecules. Whole-cell
models promise to tackle this challenge by integrating het-
erogeneous molecular data into predictive computational
models. This integration requires model organism data-
bases which comprehensively provide readily computable
molecular data.
WholeCellKB is an open-source, web-based software
program for developing comprehensive model organism
databases for whole-cell models. As illustrated in
Figure 1, WholeCellKB enables whole-cell modeling by
organizing diverse molecular data from primary research
articles, reviews, books and databases into a single
database. The WholeCellKB data model supports
detailed descriptions of individual species including their
genes, operons, proteins, macromolecular complexes,
molecular interactions, chemical reactions and
pathways. Importantly, WholeCellKB also facilitates
extensive source documentation. We used WholeCellKB
to develop WholeCellKB-MG, an extensive database of
the pathogenic Gram-positive bacterium Mycoplasma
genitalium.
Here, we describe WholeCellKB-MG’s content, cur-
ation, user interface and implementation. We also
compare WholeCellKB-MG to existing resources, high-
lighting WholeCellKB-MG’s greater scope and granular-
ity. Finally, we discuss our future plans for WholeCellKB.
CONTENT
Our goal was to create a database comprehensive enough
to enable a whole-cell model (1). As illustrated in Figure 2,
WholeCellKB-MG broadly represents M. genitalium mo-
lecular biology including (i) its subcellular organization;
(ii) its chromosome sequence; (iii) the location, length, dir-
ection and essentiality of each gene; (iv) the organization
and promoter of each transcription unit; (v) the expression
and degradation rate of each RNA transcript; (vi) the spe-
cific folding and maturation pathway of each RNA and
protein species including the localization, N-terminal
cleavage, signal sequence, prosthetic groups, disulfide
bonds and chaperone interactions of each protein
species; (vii) the subunit composition of each macromol-
ecular complex; (viii) its genetic code; (ix) the binding sites
and footprint of every DNA-binding protein; (x) the struc-
ture, charge and hydrophobicity of every metabolite; (xi)
the stoichiometry, catalysis, coenzymes, energetics and
kinetics of every chemical reaction; (xii) the regulatory
role of each transcription factor; (xiii) its chemical com-
position and (xiv) the composition of its laboratory
growth medium. Table 1 summarizes WholeCellKB-
MG’s size and content.
CURATION
We curated WholeCellKB-MG in five steps based on
>900 primary research articles, reviews, books and
*To whom correspondence should be addressed. Tel: +1 650 7256615; Fax: +1 650 7211409; Email: mcovert@stanford.edu
Nucleic Acids Research, 2012, 1–6
doi:10.1093/nar/gks1108
ß The Author(s) 2012. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which
permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
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Nucleic Acids Research Advance Access published November 21, 2012
at Stanford University on December 5, 2012http://nar.oxfordjournals.org/Downloaded from
databases. First, we curated the overall structure of
M. genitalium including its size, shape, subcellular organ-
ization and chemical composition based on several experi-
mental studies including Morowitz et al. (2). We also
assembled the chemical composition of Mycoplasma la-
boratory growth medium based on analyses reported by
Solabia (3).
Second, we curated the structure of the M. genitalium
chromosome including its sequence, the location, length
and direction of each gene and its transcription unit or-
ganization based on the Comprehensive Microbial
Resource (CMR) annotation (4) and a recent study by
Gu
¨
ell et al. (5). We reconstructed the location of each
promoter and the expression, degradation rate and essen-
tiality of each gene product from four recent studies (6–9).
We catalogued DNA-binding sites and transcriptional
regulatory interactions from several sources including
DBTBS (10).
Third, we assembled the structure of each RNA and
protein gene product. We compiled the post-tran-
scriptional processing and modification of each RNA
transcript from several sources including Peil (11). We
reconstructed the signal sequence, localization,
chaperone-mediated folding, post-translational modifica-
tion, disulfide bonds, subunit composition and DNA
footprint of each protein and macromolecular complex
from a large number of primary research articles,
computational models and databases. We assembled the
chemical regulation of each gene product from several
sources including DrugBank (12). We used ExPASy
ProtParam (13) to calculate the pI, extinction coefficient,
half-life, instability index, aliphatic index and grand
average of hydropathy of every protein species.
Fourth, we curated the specific chemical reactions
catalyzed by each gene product starting from the CMR
(4), GenBank (14), KEGG (15) and UniProt (16) genome
annotations and the reconstructed RNA and protein mat-
uration pathways. To maximize the scope of the database
and to fill gaps in the genome annotation, we expanded
each gene product’s annotation based on primary research
articles we identified by searching PubMed (17) and
Google Scholar (http://scholar.google.com). We consulted
BioCyc (18), KEGG (15), two flux-balance analysis (FBA)
models of bacterial metabolism (19,20) and hundreds of
additional primary research articles to curate the stoichi-
ometry of each chemical reaction. We assembled the
thermodynamics and kinetics of each chemical reaction
from several databases including BRENDA (21),
SABIO-RK (22) and UniProt (16 ) and a FBA model (20).
Finally, we compiled the M. genitalium metabolome.
We included all metabolites involved in the reconstructed
reactions, biomass or growth medium. We curated the
empirical formula, structure, charge and intracellular con-
centration of each metabolite from several databases
including BioCyc (18), CyberCell (23) and PubChem
(24) and a comprehensive mass-spectrometry study (25).
We used ChemAxon Marvin (http://www.chemaxon.com/
products/marvin) to calculate the molecular weight, van
der Waals volume, pI, log
d
and log
p
of each metabolite.
In order to create a comprehensive description of
M. genitalium physiology, we based WholeCellKB-MG
on studies of closely related organisms where studies of
M. genitalium were unavailable. In cases where multiple
observations were available, we based the reconstruction
on the most closely related organism. We used
bi-directional best BLAST (26) to identify homologous
genes. To provide model transparency, we tracked the
species, experimental conditions and citation of each
piece of evidence.
COMPARISON TO EXISTING RESOURCES
WholeCellKB represents the specific molecular inter-
actions of individual species similar to previous databases
such as BioCyc (18,27) and BiGG (28). In particular,
WholeCellKB’s data model, user interface and species-
specific content were heavily inspired by BioCyc.
Importantly, WholeCellKB-MG also has several
major differences from existing resources. First,
WholeCellKB-MG more broadly represents cell physi-
ology. WholeCellKB-MG represents the molecular
details of 28 cellular processes including well-studied
processes such as metabolism as well as less well-
understood processes such as DNA damage and repair
and RNA and protein degradation. The online documen-
tation at http://wholecellkb.stanford.edu/about provides
further information about the WholeCellKB-MG data
(a)
(b)
(c)
Figure 1. WholeCellKB-MG enables whole-cell modeling by
integrating diverse data sources into a single database. (a) Currently,
WholeCellKB-MG integrates >900 primary research articles, reviews,
books and databases. (b) WholeCellKB-MG comprehensively repre-
sents all aspects of molecular physiology including metabolomics,
genomics, transcriptomics and proteomics. (c) WholeCellKB-MG
provides molecular data for whole-cell models.
2 Nucleic Acids Research, 2012
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model and how WholeCellKB-MG represents each
cellular process. Figure 3 compares WholeCellKB-MG’s
content to that of several existing databases.
Second, whole-cell modeling requires model organism
databases which explicitly define the participants of
each molecular interaction and chemical reaction.
WholeCellKB-MG addresses this need by representing
the specific molecules involved in every molecular inter-
action and by requiring structures for each molecule. For
example, WholeCellKB-MG represents the specific RNA
bases involved in every RNA methylation reaction,
whereas existing resources lump RNA methylation inter-
actions into a single generic reaction. WholeCellKB-MG
represents every major cellular process including RNA
processing and protein processing, modification and trans-
location with similarly fine molecular resolution.
Third, where available WholeCellKB-MG contains not
only structural but also quantitative functional
descriptions of each molecule and molecular interaction.
For example, WholeCellKB-MG contains chemical
reaction rate laws and kinetic parameters, RNA transcript
expressions and half-lives, and cellular and growth medium
chemical compositions. In total, WholeCellKB-MG repre-
sents 1836 heterogeneous model parameters. Table 2 sum-
marizes how WholeCellKB represents these heterogeneous
parameters using several types of database entries.
DATA INPUT
WholeCellKB provides administrators with two editing
interfaces: (i) a web form to edit single entries and (ii)
an Excel-based interface to simultaneously edit multiple
entries. We believe that these two interfaces enable collab-
orative model organism database development.
In the beginning of our M. genitalium curation efforts,
we primarily used the batch interface to quickly import
large amounts of data from other genome annotations.
We continued to use the batch interface throughout the
project to import high-throughput molecular data. Later
in our M. genitalium curation efforts, we primarily used
the form interface to refine our annotation based on
specific biochemical studies. Overall, we found that
WholeCellKB improved the quality of our annotation
and in particular encouraged us to thoroughly annotate
the original source of each datum.
Data submitted to WholeCellKB was extensively
validated to ensure consistency and correctness. For
example, WholeCellKB checked that each chemical
formula was valid, that each reaction was mass-balanced
and that every molecule and kinetic parameter was defined
in each reaction rate law. WholeCellKB provided hints on
Figure 2. WholeCellKB aims to comprehensively describe cell physiology including the structure and dynamics of every metabolite, gene, RNA
transcript and protein. Boxes illustrate several molecular properties represented by WholeCellKB.
Table 1. WholeCellKB-MG size
Entry type Number
Cellular state 16
Chromosome feature 2305
Compartment 6
Gene 525
Metabolite 722
Pathway 17
Process 28
Protein complex 201
Protein monomer 482
Reaction 1857
Transcription unit 335
Transcriptional regulatory interaction 30
Nucleic Acids Research, 2012 3
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how to correct invalid data such as the atom imbalance of
invalid reactions.
DATA ACCESS
WholeCellKB-MG is freely accessible through a simple
and intuitive web-based interface at http://wholecellkb.
stanford.edu. This web-based interface allows users to
quickly browse, search and export the database. It
also allows administrators to add, edit and delete
entries. Importantly, the interface is extensively com-
mented and hyperlinked, allowing users to easily find the
primary source of each datum.
WholeCellKB-MG is also accessible through a RESTful
interface. This interface provides the content of every
HTML page in JSON and XML formats. We are cur-
rently using this interface to develop software for
visualizing whole-cell simulations.
DEVELOPER API
WholeCellKB was designed to enable modelers to develop
model organism databases for whole-cell models,
including designing custom data models and user
interfaces. WholeCellKB provides a framework for
viewing, searching, exporting and editing database
entries which developers can combine with custom data
models and HTML templates. This allows developers to
build custom model organism databases with minimal
effort and without any knowledge of database design.
Furthermore, because WholeCellKB is open source and
implemented with Python, modelers can easily display sci-
entific calculations alongside curated data in the user
interface. The online documentation provides further
instructions on how to customize WholeCellKB.
IMPLEMENTATION
WholeCellKB was implemented in Python using the
Django (http://www.djangoproject.com) web framework
and stored using the relational database MySQL (http://
www.mysql.com). Full-text search was implemented using
Haystack (http://haystacksearch.org) and Xapian (http://
xapian.org). Excel, JSON and XML export were
implemented using OpenPyXL (http://bitbucket.org/eric
Figure 3. Detailed comparison of the content of WholeCellKB-MG and several existing biological databases. In addition to containing detailed
descriptions of genetics, metabolism and transcriptional regulation comparable to existing resources such as BiGG (28), BioCyc (18) and CMR (4),
WholeCellKB-MG has detailed representations of RNA degradation, RNA and protein maturation and protein translocation. Black boxes indicate
physiology represented with fine granularity including the specific molecules involved in each specific interaction (e.g. specific metabolites involved in
each metabolic reaction). Gray boxes indicate coarsely represented physiology, for example lumping families of similar reactions such as RNA
methylation into a single database entry rather than representing the specific RNA bases involved in each individual reaction. White boxes indicate
unrepresented physiology.
Table 2. WholeCellKB-MG parameters
Type Number
Cell composition 73
Media composition 83
Reaction K
eq
225
Reaction K
m
483
Reaction V
max
434
RNA expression 525
RNA half-life 525
Stimulus values\ 10
Transcriptional regulation 32
Activity 30
Affinity 2
Other 154
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gazoni/openpyxl), simplejson (http://pypi.python.org/
pypi/simplejson) and xml.dom (http://docs.python.org/
library/xml.dom.html). WholeCellKB runs on the
Apache (http://www.apache.org) web server using the
mod_wsgi (http://code.google.com/p/modwsgi) module.
All of the software used to implement WholeCellKB is
available open source.
SUMMARY AND FUTURE DIRECTIONS
WholeCellKB-MG is an extensive database of
M. genitalium designed to facilitate whole-cell modeling.
Currently, we are continuing to curate the database as well
as starting to create equally comprehensive databases of
other model microorganisms. Beyond facilitating realistic
whole-cell models, we believe that these databases are
useful platforms for experimental and computational
biologists.
We created WholeCellKB-MG using WholeCellKB, an
open-source, web-based software program which enables
modelers to quickly develop model organism databases
for whole-cell modeling.
Beyond continuing to curate model organisms, we also
plan to continue to strengthen the WholeCellKB software.
We plan to add additional tools for importing databases
curated with other tools such as PathwayTools (27),
storing the detailed history of each database entry and
comparing model organism databases as well as expand-
ing the search functionality of the RESTful API. As the
whole-cell modeling community grows, in the future we
also plan to enable open-editing similar to Wikipedia.
Finally, we are currently using WholeCellKB’s RESTful
API to develop tools for visualizing whole-cell
simulations.
We hope that other researchers will use WholeCellKB
to develop model organism databases and whole-cell
models. We believe that WholeCellKB will not only
speed up database curation and whole-cell model develop-
ment but also encourage best annotation practices.
Ultimately, we hope that WholeCellKB in combination
with whole-cell models will accelerate biological discovery
and bioengineering.
ACKNOWLEDGEMENTS
We thank Elsa Birch, Nick Ruggero and Ruby Lee for
enlightening discussions on database design, curation,
modeling and visualization.
FUNDING
NIH Director’s Pioneer Award [5DP1LM01150-05] and a
Hellman Faculty Scholarship (to M.W.C.); NDSEG, NSF
and Stanford Graduate Fellowships (to J.R.K.); NSF and
Bio-X Graduate Student Fellowships (to J.C.S.) and a
Stanford Graduate Fellowship (to D.N.M.). Funding for
open access charge: NIH Director’s Pioneer Award
[5DP1LM01150-05].
Conflict of interest statement. None declared.
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