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| Role of a Web-based Software Platform for Systems Biology |
| Nicolas Turenne |
| INRA, SenS UR1326, F-77420 Champs-sur-Marne |
| *Corresponding author: |
Dr. Nicolas Turenne
INRA, SenS UR1326, France
Champs-sur-Marne
Tel: +33 (0)1 34 65 28 82
+33 (0)1 34 65 29 01
E-mail: nicolas.turenne@jouy.inra.fr |
|
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| Received July 14, 2011; Accepted July 20, 2011; Published July 25, 2011 |
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| Citation: Turenne N (2011) Role of a Web-based Software Platform for Systems
Biology. J Comput Sci Syst Biol 4: 035-041. doi:10.4172/jcsb.1000101e |
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| Copyright: © 2011 Turenne N. This is an open-access article distributed under
the terms of the Creative Commons Attribution License,which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited. |
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| Introduction |
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| Open-source software has gained interest in scientific communities
in all domains and in all fields, although it is expensive to develop tools
in individual teams. The pooling of resources is a good argument in
promoting teams to develop new tools, in exploiting costly centralized
computer-resources and building an interesting repository of
databases. Providing users with a standard browsing interface is also
a key-point when curation, navigation, and selection of hypotheses are
required. In systems biology, these arguments are also justified since
network reconstruction and simulation are time-consuming, and very
large, hard to manage datasets become available. Such tasks may also
require simple, specific tools and innovating algorithms 'integrable' to
the frameworks. Users can also be involved in curating data, selecting
model hypotheses and in interpreting and sharing results. The modern
platform is a recent concept, as recent as global efforts in systems
biology. The meeting of such two key research fronts is fascinating
enough in itself to address the purpose of its future. |
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| What is a computational platform? |
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| Let us return to the original definition of a platform in general, but
more specifically in science and bioinformatics. In language, the word
'platform' is very old. In common language before the 1990 there were
four widespread meanings: (1) a flat surface; (2) a flat base for devices;
(3) a political basis (programme); and (4) plant for drilling. For our
purpose with respect to bioinformatics, two features are fundamental
to the definition, the basis for assembly, and integrative capability. In
this context, a software platform is some sort of hardware architecture
and software framework, including an application framework, which
allows software to run. A platform might be simply defined as a place
to launch software. It includes a computer's architecture, operating
system, programming languages and related user interface (runtime
system libraries or graphical user interface). Such a working
environment is becoming an instrument in managing data in many
fields, for example natural sciences, social sciences and management
systems in big companies [1]. Specifically, in computer science we can
say that it is the continuation of effort to develop intelligent systems
able to gather information from experts (now called users) with data
integration workflows. Modern artificial intelligence has to develop
and to adhere to stringent standards and frameworks to become
integrative and novel computing paradigms and environments, such
as collaborative networks and cluster architectures [2]. Hence, we can
argue that a software platform is a scriptable integration computing
environment and a web portal to make systems available in any
location. Such an environment is not free, and the mutualization of
costs and also perenity of its maintenance over time are guaranteed
by an institution or a consortium of institutions. We defined at length
what a computational platform could be. But what is a computational
platform not? It is not open-source downloadable software. In this case,
it may be just a framework. It is not a protocol to acquire wet-lab data
and propose analytical tools to implement data processing and display
results. It is not just an online database. |
| |
| Short historical overview |
| |
| The notion of an integrative library available to users is an old
concept. The idea of an integrated environment for computers is as
old as computer science itself since, by its architecture, a computer is a
group of sub-systems. With regard to the technical toolbox we should
mention two well-known environments. The first, the Numerical
Algorithms Group (NAG), a non-profit software company (Oxford,
UK) which proposed its first oldest Fortran libraries in October
1971. The second, the R-project, initiated in 1993 at the University
of Auckland, proposed a core distribution in mid-1997, and now has
more than 3,000 packages. Both toolboxes provide methods for the
solution of mathematical, statistical and data mining problems, within
visualization capabilities, in scientific software development. |
| |
| The 'omics' measurement platforms developed in the 1990s
preceded the emergence of software platforms. The primary platforms
in use are mass spectrometry and microarrays which associate tools
to process data flows. If we look at the bibliography, history started
with database building and microarray processing; both terms 'systems
biology' and 'platform' in bioinformatics occur with the emergence
of large databases of high-throughput data and pathways availability.
They naturally met as modelling and pathways exploitation were
required to understand groups of genes. The birth of the joining of
these two concepts was probably at the beginning of 2000 [3]. The
implementation of a platform comes in the wake of traditional expert
systems and tools for problem solving and knowledge management.
A platform is useful in building molecular network maps, simulation
tools, data resources and web services for sharing information. |
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| Location of platforms |
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| As bioinformatics is community-oriented, rather than countrypiloted,
many platforms, which would benefit from a centralized
information portal, occurred in several universities. It is the case in
France where 13 platforms are indexed by the French national network
in bioinformatics [113]. Another institution called IBISA (Device
in Agronomy and Life Sciences) propose a directory of platforms in life
sciences but also proposed quality control labels dedicated to the usage,
service and development of a platform (technological and software)
[108]. |
| |
| Data for systems biology |
| |
| As mentioned above, a platform is not a database but is strongly
linked to a database since data are necessary to infer interactions and obtain parameters for models (using pathways for instance).
Some key experimental methods for systems biology are as follows.
(1) Oligonucleotide microarrays; the most widely used methods
to monitor the expression levels of RNA transcripts in a biological
sample are based on microarrays. They measure the hybridization of
fluorescently labelled cDNA, synthesized from extracted mRNA, to
known nucleotide sequences spotted on solid surfaces. For all genes on
the microarray, an expression value is derived from the fluorescence
intensity of the hybridized RNAs (2) RNA deep sequencing: the most
recent transcriptomics approaches are based on the deep sequencing of
transcripts extracted from biological samples. (3) Mass spectrometry
experiments (MS): the compounds present in a sample are identified
through the accurate measurements of their mass-to-charge ratios.
(4) Nuclear magnetic resonance (NMR) is a common method in
metabolomics and, in contrast to MS-based approaches, in most cases
does not require analyte separation. NMR spectroscopy can provide
detailed information on the molecular structure of compounds found
in complex mixtures, and a wide range of small molecule metabolites
in a sample can be detected simultaneously. (5) Literature, and already
built pathways, can help to select a model which mines existing
interactions in a given space and time development of a biological state. |
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| Objectives of a platform in systems biology |
| |
| Modelling leads to multiple and immediate fallout for such
biological issues as: (1) metabolic engineering: (2) predictive toxicology
and drug repurposing; drug-target networks can be used to identify
multiple targets and to determine suitable combinations of drug targets
or drugs: (3) solving major nutrition-associated problems in humans
and animals including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine growth retardation: (4) inflammatory
(immune) response below the baseline: (5) development of novel and
more efficient microbes for the production of biofuels: (6) revealing
new biomarkers in medical pathologies. |
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| Examples and core of a software platform |
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| If platforms 10 years ago were largely focused on database
repositories and microarray or sequence processing, now some
platforms are typically dedicated to systems biology. Of course
they tried to offer access through the internet as a web portal, were
developed on a linux operating system and stored data in a database
management system such as Mysql, with a Java or Html-based user
interface; integration of frameworks and data analysis tools written
in script languages (python, R, Matlab, Perl), or in C++ to speed up
computation as in BioArray Software Environment (BASE) [4]. |
| |
| A system-centric global knowledge management approach to
discovering (organizing and sharing) scientific knowledge from
large-scale data (SKM). Knowledge management is the collection
of processes that govern the creation, dissemination, and utilization
of knowledge. In this context, the creation step refers to the data
integration 'pipeline' which proceeds from experimental data
(either from biological experiments or simulations), to the sharing
and utilization of the underlying inputs/outputs of each of the data
integration steps (from 'raw' data to fully fledged systems dynamics
models). Currently, solutions of large-scale system-centred problems
suffer from a serious lack of integration of the underlying human, data,
information, and knowledge resources. Researchers, who discover new
insights, disperse their results over a variety of journal and conference publications, biomedical databases, information bases, and knowledge
bases. These are typically devoted to a general subject area (e.g., gene
sequences, protein structures, etc.) as opposed to being exclusively
dedicated to the system under study. Researchers wanting to obtain
relevant data, information, and knowledge invest considerable effort in
locating and reintegrating the information in the context of the system
under investigation. In other words, instead of publishing, sharing,
and using the data, information and knowledge in the context of the
relevant structures of the system in question, the data, information
and knowledge is being heavily fragmented, decontextualized, and
physically distributed, only to be relocated, recontextualized, and
reintegrated by those who need the information. From a knowledge
management perspective, this is an extremely poor solution. |
| |
| SynBioWave [5], Cell Illustrator [6], PINA [7], Moksiskaan [8],
MEMOSys [9], Babelomics [10], MetNet [11], Virtual Plant [12] are
examples of actual systems biology platforms. |
| |
| In the following sections (VIII-XVIII) we detail different core parts
of the bioinformatics community. |
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| Commercial toolkits |
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| Toolkits proposing access to large pathway repositories are also
widespread; high level display, easy searching and quality of service
are guaranteed. These are some popular products: Ingenuity Pathway
Analysis (IPA) from Ingenuity Systems [109], PhysioLab from
Entelos [13], Pathway Studio from Ariadne Genomics [112],
SimPheny from Geomatica [114], BioCarta from BioCarta LLC
[106], MetaCore and MetaMiner from GeneGo [111]. |
| |
| Web-based databases |
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| Pathway-oriented approaches are appealing because they are
hypothesis-driven. However, their limitation is the lack of knowledge
of biological gene networks. Hence, even if sharing databases was
fundamental to the emergence of platforms, sharing curated pathways
and models is still useful beyond experimental data. We can say that
KEGG (Kyoto Encyclopedia of Genes and Genomes) [14] or the
visualization of pathways [15] played a pioneering role. Some recent
platforms, for example WikiPathways, take into account modern
technology such as web 2.0 [16]. There is also a tendency to sharing
models of simulation such as BioModels.net [17]. Some databases
are species-oriented like AtPID [18] or RIMAS [19] for plants,
CoryneRegNet for bacteria [20] or HPRD for humans [21]. Uetz et al.
[15] identified a network about 957 interactions and 6,000 proteins for
yeast, now HPRD proposes 39,194 interactions and 30,047 proteins for
humans. Some other databases are related to diseases like NetAge [22].
Other databases are more generalist like Gaggle Tool Creator (GTC)
[23]. |
| |
| Standards |
| |
| Standards help to integrate and exchange information; they are
currently more and more used in platforms. BioPAX is a protocol
for the specification and representation of cell signalling pathways,
gene-regulatory networks, protein-protein interactions and other
types of biomolecular interaction data [24]. SMBL (Systems Biology
Markup Language) provides community-driven software support
[25]. MIRIAM, or minim information requested in the annotation of
biochemical networks, is a scheme to provide extensive documentation
in the model file in a structured manner [26]. SBGN (Systems Biology
Graphical Notation) is a recent attempt to standardize the visual
representation of biological networks [27]. Automatic equation generation for SBML from SBGN diagrams was made possible.
Biocoder is a C++library that enables biologists to express the exact
steps needed to execute a protocol [28]. |
| |
| Simulation frameworks |
| |
| A simulation framework is used to confirm the interest of
interactions and validate the correct structure and dynamics of a
pathway. Distinction occurs based on the type of modelling used,
i.e. deterministic (DM) versus stochastic (SM). Deterministic models
implicitly assume that the underlying quantities, i.e., concentrations or
molecule numbers, vary in a deterministic and continuous fashion. On
the other hand, the stochastic framework takes into account the random
interactions of the biochemical species' more coarse-grained models
using Ordinary Differential Equations (ODEs), Partial Differential
Equations (PDEs), Stochastic Differential Equations (SDEs), or
Markov Jump Processes (MJPs), typically used to model simple
synthetic biology circuits. Two main types of stochastic models (MJPs
and SDEs) are typically used in the literature to represent stochastic
systems (Gillespie, Gibson-Bruck, tau-leaping, Bayesian methods).
These coarse-grained models can be used as simplifications as long as
their corresponding assumptions are satisfied. Flux balance analysis
exploits the properties of a stoichiometric matrix and homeostasis with
matrix algebra (MA); p-systems can be associated to this family as a
kind of finite automata. In this family we also find Petri Net modelling,
and Boolean networks. Logical models (LM) offer goods properties
to analyze slopes of dynamics but also in this family we find symbolic
differential systems. |
| |
| SM: FERN, CaliBayes [29], SPARK, STEPS [30], CellMC [31]. |
| |
| DM: Simulation System [32], PK-Sim [33], COPASI [34],
SBTOOLBOX [35]. |
|
| |
| MA: MOMA [36], ROOM [37], Genomic Object Net [38], Snoopy
[39], OptFlux [40], COBRA-Matlab [41], Biomet [42], BioRica [43]. |
| |
| LM: ChemChains [44], GNA [45], SQUAD [46], Odefy [47],
BooleanNet [48], GinSim [49], CellNetAnalyzer [50], CellNetOptimizer
[51], MetaReg [52], JAMES [53], FBA-SimVis [54], Fasimu [55], BlenX
[56], Psim [57], BioXyce [58], MetaPlab [59]. |
| |
| Laboratory information management system (LIMS)
frameworks |
| |
| A LIMS is the central part of a typical proteomics workflow, including
journal articles and data stored in repositories for community-wide
use. Some of the LIMS systems were more convincing than others due
to a highly sophisticated graphical workflow editor, mature advanced
functionality, easy out-of-box behaviour, or the possibility to combine
results from multiple search engines [60]. |
| |
| Collaboration frameworks |
| |
| Sharing data, analysis tools and infrastructure can accelerate
the efforts of large research consortia by enabling new insights and
enhancing efficiency. In this way, a collaboration framework enlightens
curation, annotation and database enrichment with regard to models
and pathways. These are examples of existing frameworks: Bionumbers
[61], SBMM [62], GARNET [63], Patika [64], Payao [65], Quail
Genomics [66], PMI [67], Rapyd [68], DC pathway [69], InnateDB
[70]. |
| |
| Integration frameworks |
| |
| A data warehouse is one of the famous architectures of materialized integration. In bioinformatics the data warehouse is usually used for
data integration. These are examples of existing frameworks: Atlas [71],
BioMart [72], BioWarehouse [73], Columba [74], SYSTOMONAS
[75], BioDWH [76], VINEdb [77], Booly [78], GNCPro [79]. |
| |
| Visualization frameworks |
| |
| Visualization is a means of exploratory data analysis and has been
a key method for network analysis like GraphViz [107] since
the birth of platforms and web-based portals. Cytoscpape is a general
framework for visualization with customization [80]. The Genome
Network Platform provides an integrated user interface with a proteinprotein-
interaction network viewer [81]. CellDesigner is a structure
diagram editor for drawing gene-regulatory and biochemical networks
[82]. ProMoT is a 'drag and drop' design platform for synthetic gene
circuits [83]. MetPa [84] and Reactome [85] offer tools for visualization
and navigation over thousands of pathways. |
| |
| Multivariate data analysis frameworks |
| |
| Biological space is highly dimensional in term of objects and
features. Multivariate data analysis can be fruitful for data processing.
Classical approaches can be used and the R platform is very widespread,
as in STRUCTURELAB [86]. Some implementations are ad-hoc with
multidimensional scaling as in BASE [87]. Less standard in multivariate
data analysis but also popular for graphical analysis are the BIOCHAM
[88] or Bayesian approaches such as the Visual Integration for Bayesian
Evaluation (VIBE) software [89]. Even more ad-hoc techniques are
integrated in toolkits such as ORBIT [90]. PerturbationAnalyzer [91],
model checking [92,93]. |
| |
| Workflow frameworks |
| |
| Workflow technologies for data processing design, application, and
execution link these tools into high-throughput processing pipelines.
Their ongoing development can be expected to greatly enrich the
ability of researchers to not only process newly obtained bio/text data
but also to compare, contrast, and combine results from previous
research studies via meta-analytic and data mining approaches and
to visualize unique patterns present in bio/text results that could only
be identified through large-scale computing approaches. Taverna is a
good representative of such a framework [91]. |
| |
| Ontology and terminology management |
| |
| A 'knowledge space' where information is heavily fragmented,
decontextualized, and physically distributed, and requires
recontextualization for those who need information, is a poor solution.
Ontology and terminology offer a more efficient proposal. Open
Biomedical Ontologies (OBO) [94] and Gene Ontology (GO) [95] are
popular packages for integration. Other specific ontologies appeared
for plants or microbes. iHop [96] is a full platform with the extraction
of relations and named entities from literature (2,800 organisms,
110,000 genes, 23.4 million sentences). SZGR [97] integrates literature
and GO exploration for a specific disease platform. The ONTO-Toolkit
is a framework for managing known ontologies such as OBO [98],
in the same way Ontology Lookup Service [99] is based on GO and
vocabulary exploration. Onto-Tools [100] is a framework for help
annotation with ontologies based on GO. Ondex is a help tool with
semantic integration [101]. EXCERBT is a tool integrated to MIPS
web-database and gives access to literature on relationships between
proteins [102]. caBIG [103] integrates a framework in a cancer database
platform, with high level vocabulary and concept exploration, cleaning
and exploration. Finally an ontology tool can also be integrated into a simulation framework such as Ph-SIM which links physiological
knowledge to simulation parameters [104]. |
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| What should be the next step? |
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| The next generation platforms will perhaps be as novel as current
next generation sequencing devices, or the previous microarrays,
and will transform our working habits. Some points are perhaps
almost achievable now. The following is a short list but it is not final
and complete. Advances should be made in the following: (1) In silico
support of synthetic biology, from the specific data exchange formats,
to the most popular software platforms and algorithms; (2) design and
construction of an artificial bacterial cell; (3) workflow experiment
design; (4) drug-target discovery - pharmacogenomics; (5) coupling
distinct computational models of science and engineering systems, still
a recurring challenge when developing multi-applications - a kind of
meta-analysis. |
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Figure 1: General schema of a computational platform dedicated to systems biology. |
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| References |
| |
- Gawer A (2009) Platforms, Markets and Innovation, Edward Elgar Publisher.
- Berrar D, Sato N, Schuster A. Quo Vadis (2010) Artificial Intelligence? Advances in Artificial Intelligence Article ID 629869.
- Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, et al.
(2003) Development of human protein reference database as an initial platform
for approaching systems biology in humans. Genome Res 13: 2363-2371.
- Vallon-Christersson J, Nordborg N, Svensson M, Häkkinen J (2009) BASE -
2nd generation software for microarray data management and analysis. BMC
Bioinformatics 10: 330.
- Staab PR, Walossek J, Nellessen D, Grünberg R, Arndt KM, et al. (2010)
SynBioWave--a real-time communication platform for molecular and synthetic
biology. Bioinformatics 26: 2782-2783.
- Nagasaki M, Saito A, Jeong E, Li C, Kojima K, et al. (2010) Cell Illustrator 4.0:
A computational platform for systems biology. In Silico Biology 10.
- Wu J, Vallenius T, Ovaska K, Westermarck J, Mäkelä TP, et al. (2009)
Integrated network analysis platform for protein-protein interactions. Nat
Methods 6: 75-77.
- Laakso M, Hautaniemi S (2010) Integrative platform to translate gene sets to
networks. Bioinformatics. 26: 1802-1803.
- Pabinger S, Rader R, Agren R, Nielsen J, Trajanoski Z (2011) MEMOSys:
Bioinformatics platform for genome-scale metabolic models BMC Syst Biol 5:
20.
- Al-Shahrour F, Minguez P, Tárraga J, Montaner D, Alloza E et al. (2006)
BABELOMICS: a systems biology perspective in the functional annotation of
genome-scale experiments. Nucleic Acids Res 1: 472-476.
- Wurtele ES, Ling Li, Dan Berleant, Dianne Cook, Julie A, et al. (2007) MetNet:
Systems Biology Software for Arabidopsis. In Concepts in Plant Metabolomics,
Springer Verlag.
- Katari MS, Nowicki SD, Aceituno FF, Nero D, Kelfer J, et al. (2010) VirtualPlant:
a software platform to support systems biology research. Plant Physiol 152:
500-515.
- Rullmann JAC, Struemper H, Defranoux NA, Ramanujan S, Meeuwisse CM,
et al. (2005) Systems biology for battling rheumatoid arthritis: application of
the Entelos PhysioLab platform. Syst Biol IEE Proc Syst Biol 152: 2560-262.
- Kanehisa M (1997) "A database for post-genome analysis". Trends Genet 13:
375-376.
- Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, et al. (2000) A
comprehensive analysis of protein-protein interactions in Saccharomyces
cerevisiae. Nature 403: 623-627.
- Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, et al. (2008)
WikiPathways: pathway editing for the people. PLoS Biol 6: 184.
- Li P, Dada JO, Jameson D, Spasic I, Swainston N, et at. (2010) Systematic
integration of experimental data and models in systems biology. BMC
Bioinformatics 11: 582.
- de Oliveira Dal'Molin CG, Quek LE, Palfreyman RW, Brumbley SM, Nielsen
LK (2010) AraGEM, a genome-scale reconstruction of the primary metabolic
network in Arabidopsis. Plant Physiol 152: 579-89.
- Junker A, Hartmann A, Schreiber F, Bäumlein H (2010) An engineer's view on
regulation of seed development. Tren Pl Sci 15: 303-307.
- Baumbach J (2007) CoryneRegNet 4.0 - A reference database for
corynebacterial gene regulatory networks. BMC Bioinformatics 8: 429.
- Prasad TSK, Goel R, Kandasamy K, Keerthikumar S, Kumar S, et al. (2009) Human Protein Reference Database - 2009 Update. Nucl Acid Res 37: 767-
772.
- Tacutu R, Budovsky A, Fraifeld VE (2010) The NetAge database: a
compendium of networks for longevity, age-related diseases and associated
processes. Biogerontol 11: 513-522.
- Tenenbaum D, Bare JC, Baliga NS (2010) GTC: A web server for integrating
systems biology data with web tools and desktop applications. Source Code
Biol Med 13: 5-7.
- Demir E, Cary MP, Paley S, Fukuda K, Lemer C, et al. (2010) The BioPAX
community standard for pathway data sharing. Nat Biotechnol 28: 935-942.
- Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, et al. (2003). "The systems
biology markup language (SBML): a medium for representation and exchange
of biochemical network models" Bioinformatics 19: 524-531.
- Le Novère N, Finney A, Hucka M, Bhalla U, Campagne F, et al. (2005)
Minimum Information Required In the Annotation of Models (MIRIAM). Nature
Biotechnology 23: 1509-1515.
- Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, et al. (2009) The Systems
Biology Graphical Notation. Nat Biotechnol 27: 735-741.
- Ananthanarayanan V , Thies W (2010) Biocoder: A programming language for
standardizing and automating biology protocols. J Biol Eng 8: 4-13.
- Chen Y, Lawless C, Gillespie CS, Wu J, Boys RJ, et al. (2010) CaliBayes and
BASIS: integrated tools for the calibration, simulation and storage of biological
simulation models. Brief Bioinform 11: 278-289.
- Wils S, De Schutter E (2009) STEPS: Modeling and Simulating Complex
Reaction-Diffusion Systems with Python. Front Neuroinformatics 3: 15.
- Caulfield E, Hellander A (2010) CellMC--a multiplatform model compiler for the
Cell Broadband Engine and x86. Bioinformatics 26: 426-428.
- Wang KQ, Yuan YF, Li J (2010) A Simulation System for Computational Cell
Models Based on Object-Oriented Design Patterns. Computer and Information
Science 3: 2.
- Eissing T, Kuepfer L, Becker C, Block M, Coboeken K, et al. (2011) A
computational systems biology software platform for multiscale modeling and
simulation: integrating whole-body physiology, disease biology, and molecular
reaction networks. Front Physiol 24: 2-4.
- Hoops S, Sahle S, Gauges R, Lee C, Pahle J, et al. (2006) COPASI - a
complex pathway simulator. Bioinformatics 22: 3067-3074.
- Schmidt H, Jirstrand M (2005) Systems Biology Toolbox for MATLAB: A
computational platform for research in Systems Biology. Bioinformatics 22:
514-515.
- Segrè D, Vitkup D, Church G (2002) Analysis of optimality in natural and
perturbed metabolic networks. Proc Natl Acad Sci U S A 99: 15112-15117.
- Shlomi T, Berkman O, Ruppin E (2005) Regulatory on/off minimization of
metabolic flux changes after genetic perturbations. Proc Natl Acad Sci U S A
102: 7695-7700.
- Nagasaki M, Doi A, Matsuno H, Miyano S (2004) Genomic Object Net:I. a
platform for modeling and simulating biopathways. Appl Bioinformatics 2: 181-
184.
- Rohr C, Marwan W, Heiner M (2010) Snoopy--a unifying Petri net framework to
investigate biomolecular networks. Bioinformatics 26: 974-975.
- Rocha I, Maia P, Evangelista P, Vilaça P, Soares S, et al. (2010) OptFlux: an
open-source software platform for in silico metabolic engineering. BMC Syst
Biol 19: 4-45.
- Becker SA, Feist AM, Mo ML, Hannum G, Palsson B, et al. (2007) Quantitative
prediction of cellular metabolism with constraint-based models: the COBRA
Toolbox Nature Protocols 2: 727-738.
- Cvijovic M, Olivares-Hernández R, Agren R, Dahr N, Vongsangnak W, et
al. (2010) Nielsen J. BioMet Toolbox: genome-wide analysis of metabolism. Nucleic Acids Res 38: 144-149.
- Garcia A, Sherman D (2010) Mixed-formalism hierarchical modeling and
simulation with BioRica. INRIA 2010 technical report.
- Helikar T, Rogers JA (2009) ChemChains: a platform for simulation and
analysis of biochemical networks aimed to laboratory scientists. BMC Syst Biol
Jun 6: 3-58.
- de Jong H, Geiselmann J, Hernandez C, Page M (2003) Genetic Network
Analyzer: Qualitative simulation of genetic regulatory networks. Bioinformatics
19: 336-344.
- Mendoza L, Xenarios I (2006) A method for the generation of standardized
qualitative dynamical systems of regulatory networks. TheorBiol Med Modell
3:13.
- Wittmann D, Krumsiek J, Saez-Rodriguez J, Lauffenburger DA, Klamt S, et al.
(2009) From Qualitative to Quantitative Modeling. BMC Syst Biol 3: 98.
- Albert I, Thakar J, Li S, Zhang R, Albert R (2008) Boolean network simulations
for life scientists. Boolean Net: Source Code Biol Med 14: 3-16.
- Chaouiya C, Remy E, Mosse B, Thieffry D (2003) Qualitative Analysis of
Regulatory Graphs: A Computations Tool Based On a Discrete Formal
Framework. In Lecture Notes in Control and Information Sciences, Positive
Systems (Benvenuit L, De Santis A, Farina L, Eds.) 119-126, Springer-Verlag,
Berlin.
- Klamt S, Saez-Rodriguez J, Lindquist J, Simeoni L, Gilles ED (2006) A
Methodology for the Structural and Functional Analysis of Signaling and
Regulatory Networks. BMC Bioinformatics 7: 56.
- Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger
DA, et al. (2009) Discreet logic modeling as a means to link protein signaling
networks and functional analysis of mammalian signal transduction. Mol Syst
Biol 5: 331.
- Ulitsky I, Gat-Viks R, Shamir (2008) MetaReg: A platform for modeling, analysis
and visualization of biological systems using large-scale experimental data. Genome Biology 9: R1.
- Ewald R, Himmelspach J, Jeschke M, Leye S, Uhrmacher AM (2010) Flexible
experimentation in the modeling and simulation framework JAMES II--
implications for computational systems biology. Brief Bioinform 11: 290-300.
- Grafahrend-Belau E, Klukas C, Junker BH, Schreiber F (2009) FBA-SimVis:
interactive visualization of constraint-based metabolic models. Bioinformatics
25: 2755-2757.
- Hoppe A, Hoffmann S, Gerasch A, Gille C, Holzhütter HG (2011) FASIMU:
flexible software for flux-balance computation series in large metabolic
networks. BMC Bioinformatics 22: 12-28.
- Valentinia R, Jordán F (2010) CoSBiLab Graph: The network analysis module
of CoSBiLab. Environmental Modelling & Software. 25: 886-888.
- Buiua C, Arsenea O, Corina Cipuc C, Patrascua M (2011) A software tool for
modeling and simulation of numerical P systems. Biosystems 103: 442-447.
- May EE (2010) Circuit-based Models of Biomolecular System Dynamics, In
Simulation and Verification of Electronic and Biological Systems. [Hardcover]
Peng Li (Editor), Luís Miguel Silveira (Editor), Peter Feldmann (Editor) 2010.
- Bianco L, Manca V, Marchetti L, Petterlini M (2007) Psim: a simulator for
biomolecular dynamics based on P systems. In: Congress on Evolutionary
Computation 883-887.
- Stephan C, Kohl M, Turewicz M, et al. (2010) Using Laboratory Information
Management Systems as central part of a proteomics data workflow. Proteomics 10: 1230-1249.
- Milo R, Jorgensen P, Moran U, Weber G, Springer M (2010) BioNumbers--the
database of key numbers in molecular and cell biology. Nucleic Acids Res 38:
750-753.
- Navas-Delgado I, Real-Chicharro A, Medina MA, Sánchez-Jiménez F, Aldana-
Montes JF (2010) Social pathway annotation: extensions of the systems
biology metabolic modelling assistant. Brief Bioinform.
- Rho K, Kim B, Jang Y, Lee S, Bae T, et al. (2011) GARNET--gene set analysis
with exploration of annotation relations. BMC Bioinformatics 12: 25.
- Demir E, Babur O, Dogrusoz U, Gursoy A, Nisanci G, et al. (2002) PATIKA:
An integrated visual environment for collaborative construction and analysis of
cellular pathways. Bioinformatics 18: 996-1003.
- Matsuoka Y, Ghosh S, Kikuchi N, Kitano H (2010) Payao: a community platform
for SBML pathway model curation. Bioinformatics 26: 1381-1383.
- Rawat A, Gust KA, Elasri MO, Perkins EJ (2010) Quail Genomics: a
knowledgebase for Northern bobwhite. BMC Bioinformatics 11: 6-13.
- Guruprasad Kora, Edward C Uberbacher, Mitchel J. Doktycz (2011) Plant-
Microbe Interfaces: Collaboration Platform for Scientific Communication,
Management, Information Storage and Sharing, DOE Joint Meeting Genomic
Science Awardee meeting IX April 10-13 2011 Crystal City Virginia, USA.
- Schneider J, Blom J, Jaenicke S, Linke B, Brinkrolf K, et al. (2010) RAPYD -
Rapid Annotation Platform for Yeast Data. J Biotechnol.
- Patil S, Pincas H, Seto J, Nudelman G, Nudelman I, et al. (2010) Signaling
network of dendritic cells in response to pathogens: a community-input
supported knowledgebase. BMC Syst Biol 4:137.
- Lynn DJ, Chan C, Naseer M, Yau M, Lo R, et al. (2010) Curating the innate
immunity interactome. BMC Syst Biol 4:117.
- Shah SP, Huang Y, Xu T, Yuen MMS, Ling J, et al. (2005) Atlas - a data
warehouse for integrative bioinformatics. BMC Bioinformatics 6: 34.
- Durinck S, Moreau Y, Kasprzyk A, Davis S, Moor BD, et al. (2005) BioMart
and Bioconductor: a powerful link between biological databases and microarray
data analysis. Bioinformatics 21: 3439-3440.
- Lee TJ, Pouliot Y, Wagner V, Gupta P, Stringer-Calvert DWJ, et al. (2006)
BioWarehouse: a bioinformatics database warehouse toolkit. BMC
Bioinformatics 7: 170.
- Tril S, Rother K, Muller H, Steinke T, Koch I, et al. (2005) Columba: an integrated
database of proteins, structures, and annotations. BMC Bioinformatics 6: 81.
- Choi CC, Munch R, Leupold S, Klein J, Siegel I, et al. (2007) SYSTOMONAS -
an integrated database for systems biology analysis of Pseudomonas. Nucleic
Acids Res 35: 533-537.
- Topel T, Kormeier B, Klassen A, Hofestadt R (2008) BioDWH: A Data
Warehouse Kit for Life Science Data Integration. J Integr Bioinform 5: 932.
- Hariharaputran S, Topel T, Brockschmidt Band R. Hofest¨adt. VINEdb: a data
warehouse for integration and interactive exploration of life science data. J
Integr Bioinform 4: 63.
- Do LH, Esteves FF, Karten HJ, Bier E (2010) Booly: a new data integration
platform. BMC Bioinformatics 11:513.
- Liu GG, Fong E, Zeng X (2010) GNCPro: navigate human genes and
relationships through net-walking. Adv Exp Med Biol 680: 253-259.
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. (2003) Cytoscape:
a software environment for integrated models of biomolecular interaction
networks. Genome Res 13: 2498-2504.
- Clemente JC, Nakagawa S, Ikeo K, Gojobori T (2009) Genome Network Project:
An Integrated Genomic Platform, 3rd International Biocuration Conference.
- Funahashi A, Tanimura N, Morohashi M, Kitano H (2003) CellDesigner:
a process diagram editor for gene-regulatory and biochemical networks
BIOSILICO 1:159-162.
- Marchisio MA, Stelling J (2008) Computational design of synthetic gene circuits
with composable parts. Bioinformatics 24: 1903-1910.
- Xia J, Wishart DS (2010) MetPA: a web-based metabolomics tool for pathway
analysis and visualization. Bioinformatics 26: 2342-2344.
- Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, et al. (2005)
Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:
428-432.
- Shapiro BA, Kasprzak W (1996) STRUCTURELAB: a heterogeneous
bioinformatics system for RNA structure analysis. J Mol Graph 14: 194-205,
222-224.
- Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg A, et al. (2002)
BioArray Software Environment (BASE): a platform for comprehensive
management and analysis of microarray data. Genome Biol 3: 3.
- Gay S, Soliman S, Fages F (2010) A graphical method for reducing and relating
models in systems biology. Bioinformatics 26: 575-581.
- Webb-Robertson BJ, McCue LA, Beagley N, McDermott JE, Wunschel DS,
et al. (2009) A Bayesian integration model of high-throughput proteomics and
metabolomics data for improved early detection of microbial infections. Pac
Symp Biocomput 2009: 451-463.
- Bellgard MI, Hiew HL, Hunter A, Wiebrands M (1999) ORBIT: an integrated
environment for user-customized bioinformatics tools. Bioinformatics 15: 847-
851.
- Li C , Courtot M , Le Novère N , Laibe C (2010) BioModels.net Web Services,
a free and integrated toolkit for computational modelling software. Brief
Bioinformatics 11: 270-277.
- Romero-Campero FJ, Twycross J, Camara M, Bennett M, Gheorghe M et al.
(2009) Modular Assembly of Cell Systems Biology Models Using P Systems. International Journal of Foundations of Computer Science 20: 427-442.
- Barnat J, Brim L, Ceska M, Rockai P (2010) " 2010 Ninth International
Workshop on, and High Performance Computational Systems Biology, Second
International Workshop on, pp. 4-7, 2010 Ninth International Workshop on
Parallel and Distributed Methods in Verification, and Second International
Workshop on High Performance Computational Systems Biology, 2010.
- Smith B, Ashburner M, Rosse C, Bard J, Bug W, et al. (2007) The OBO Foundry:
coordinated evolution of ontologies to support biomedical data integration. Nat
Biotechnol 25: 1251-1255.
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene
ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25-29.
- Hoffmann R, Valencia A (2005) Implementing the iHOP concept for navigation
of biomedical literature. Bioinformatics 21: 252-258.
- Jia P, Sun J, Guo AY, Zhao Z (2010) SZGR: a comprehensive schizophrenia
gene resource. Mol Psychiatry 15: 453-462.
- Antezana E, Venkatesan A, Mungall C, Mironov V, Kuiper M (2010) ONTOToolKit:
enabling bio-ontology engineering via Galaxy. BMC Bioinformatics 11:
12-S8.
- Cote RG, Jones P, Apweiler R, Hermjakob H (2006) The ontology lookup
service, a lightweight cross-platform tool for controlled vocabulary queries. BMC Bioinformatics 7: 97.
- Khatri P, Sellamuthu S, Malhotra P, Amin K, Done A, st al. (2005) Recent
additions and improvements to the Onto-Tools. Nucleic Acids Res 33: 762-
765.
- Weile J, Pocock M, Cockell SJ, Lord P, Dewar JM, et al. (2011) Customisable
views on semantically integrated networks for systems biology. Bioinformatics
27: 1299-1306.
- Mewes HW, Ruepp A, Theis F, Rattei T, Walter M, et al. (2011) MIPS: curated
databases and comprehensive secondary data resources in 2010. Nuc Acids
Res 39: 220-224.
- Klemm J, Basu A, Fore I, Floratos A, Komatsoulis G (2010) The caBIG® Life
Sciences Distribution Biomedical Informatics for Cancer Research 2: 253-
266.
- Erson E, Cavusoglu M (2010) Design of a framework for modeling, integration
and simulation of physiological models. Conf Proc IEEE Eng Med Biol Soc
2010: 1485-1489.
- Alexey Solovyev, Maxim Mikheev, Leming Zhou, Joyeeta Dutta-Moscato,
Cordelia Ziraldo Gary An ,et al. (2010) SPARK: a framework for multi-scale
agent-based biomedical modeling Proceeding SpringSim '10 Proceedings of
the 2010 Spring Simulation Multiconference ACM New York, NY, USA ©2010.
- Biocarta.
- Graphviz.
- Ibisa Infrastructures, Biologie, Santé et Agronomie.
- Ingenuity.
- Li F, Li P, Xu W, Peng Y, Bo X, et al. (2010) PerturbationAnalyzer: a tool for
investigating the effects of concentration perturbation on protein interaction
networks. Bioinformatics 26: 275-277.
- MetaCore.
- Pathway.
- Renabi réseau national des plateformes bioinformatiques.
- Simpheny
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