Ep

DLa

b

c

d

a

ARRA

KBCPSW

1

tmbecipttlgh2

sOMA

1f

0d

BioSystems 103 (2011) 115–124

Contents lists available at ScienceDirect

BioSystems

journa l homepage: www.e lsev ier .com/ locate /b iosystems

-photosynthesis: Web-based platform for modeling of complex photosyntheticrocesses

avid Safráneka, Jan Cervenyb,c,∗, Matej Klementa, Jana Pospísilováa, Lubos Brima, Dusan Lazárd,adislav Nedbalb,c

Systems Biology Laboratory, Faculty of Informatics Masaryk University, Botanická 68a, CZ-60200 Brno, Czech RepublicInstitute of Systems Biology and Ecology, Academy of Sciences, Zámek 136, CZ-37333 Nové Hrady, Czech RepublicPhoton Systems Instruments, Ltd., Koláckova 39, CZ-62100 Brno, Czech RepublicLaboratory of Biophysics, Faculty of Science, Palacky University, Tr. Svobody 26, 771 46 Olomouc, Czech Republic

r t i c l e i n f o

rticle history:eceived 17 August 2010eceived in revised form 22 October 2010

a b s t r a c t

E-photosynthesis framework is a web-based platform for modeling and analysis of photosynthetic pro-cesses. Compared to its earlier version, the present platform employs advanced software methods andtechnologies to support an effective implementation of vastly diverse kinetic models of photosynthesis.

ccepted 23 October 2010

eywords:iomodels repositoryomputational modelshotosynthesis

We report on the first phase implementation of the tool new version and demonstrate the functionalitiesof model visualization, presentation of model components, rate constants, initial conditions and of modelannotation. The demonstration also includes export of a model to the Systems Biology Markup Languageformat and remote numerical simulation of the model.

© 2010 Elsevier Ireland Ltd. All rights reserved.

ystems biologyeb platform

. Introduction

Most of life forms, including humans, depend on photosynthesishat transforms energy of solar radiation into energy-rich organic

atter, releases oxygen that we breathe, and removes excess car-on dioxide from the atmosphere that would threaten the Earth’snergy balance. The scale of this process is illustrated by the globalarbon cycle: Most of the global carbon emissions (ca. 215 Gt/year),ncluding 9 Gt/year of anthropogenic emissions are assimilated byhotosynthesis of plants, algae, and cyanobacteria into newly syn-hesized organic matter (Lal, 2008; Raven et al., 2008). Adding tohe relevance of photosynthesis, significant expectations emerged

ately in connection with potential human interventions in thelobal carbon cycle – among the considered alternatives are theigher generation biofuels (Brennan and Owende, 2010; Chisti,008), biomineralization by point-source carbon capture (Jansson

Abbreviations: MIASE, Minimal Information about a Simulation Experimenttandard; MIRIAM, Minimum Information Requested In the Annotation of Models;DE, ordinary differential equation; SBML, Systems Biology Markup Language; SED-L, Simulation Experiment Description Markup Language; SOAP, Simple Object

ccess Protocol.∗ Corresponding author at: Institute of Systems Biology and Ecology ASCR, Zámek

36, CZ-37333 Nové Hrady, Czech Republic. Tel.: +420 386 361231;ax: +420 386 361231.

E-mail address: Jan.Cerveny@usbe.cas.cz (J. Cerveny).

303-2647/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved.oi:10.1016/j.biosystems.2010.10.013

and Northen, 2010; Riding, 2009), or enhancing the ocean carbonstorage by microbial carbon pumps (Jiao et al., 2010).

One of the key strategies in addressing these issues aims atincreasing the natural efficiency of photosynthesis (Zhu et al.,2010). This would be hard to achieve without a good mathematicalmodel capturing relevant features of the complex non-linear anddynamic photosynthetic network. The complexity of photosynthe-sis is not a mere result of a large number of components but ratherit results from numerous regulatory interactions that act overvastly different time scales and connect different compartments.The complexity is an inherent property of the photosynthetic pro-cess that must be responsive to a highly dynamic environment(e.g., Baumert and Petzoldt, 2008; Niinemets and Valladares, 2004;Rascher and Nedbal, 2006). Remarkable effort towards a consistentand coherent knowledge base of photosynthetic models material-ized in the recent publication of photosynthetic models that spanfrom femtoseconds to seasons and from individual molecules toforests and oceans (Laisk et al., 2009). However, the models thatare published in the book are of a limited use unless their rigoroustranscription is shared among all potentially interested users.

Such a community-wide model sharing may be facilitated by

Internet provided that the models are translated into a unified for-mat and uniformly annotated so that they can be used in its originalform, compared with each other and with wet-lab experiments, andfurther expanded. To this end, one needs a unified, flexible, andneutral format to fully represent partial models, involved compo-

116 D. Safránek et al. / BioSystems 103 (2011) 115–124

hotosy

nMfcrq

FRT

Fig. 1. Scheme of E-p

ents, as well as their interactions. Here, we choose Systems Biologyarkup Language (SBML) which is a standard and widely accepted

ormat for biological models (Hucka et al., 2003). SBML provides alearly defined scheme that incorporates the model structure, theeaction network, together with appropriate annotation links anduantitative data.

ig. 2. E-photosynthesis Introduction screen. The user login is for users that volunteer to registration is not a pre-requisite for model browsing and visualization. The main menu the links to other features offer addition information resources at the bottom of the wind

nthesis architecture.

The SBML format is, however, not customized for modelingphotosynthesis. In a typical SBML application, the state vari-

ables are concentrations of biochemical species that mutuallyreact according to mass action laws. In contrast, a state of aphotosynthetic reaction center is defined as a Cartesian productmultiplying the degrees of freedom of all contained redox com-

egister for gaining access to advanced simulation and project management features.asks at the top of this window direct the user to Projects, Links, Team, and Contacts.ow.

D. Safránek et al. / BioSystems 103 (2011) 115–124 117

nthes

pfm

tcmOsonrpi

icpaG2(

Fig. 3. E-photosy

onents (Nedbal et al., 2009). Thus, SBML represents a solid baseormat that needs to be further customized for photosynthesis

odeling.Another existing resource to inspire further expansion of pho-

osynthesis modeling are the established web repositories ofurated and well-annotated models traversing already throughany branches of biology (Beard et al., 2009; Le Novere et al., 2006;livier and Snoep, 2004). Unfortunately, photosynthesis models are

trongly underrepresented in these repositories, probably, becausef the natural differences that exist between most of the metabolicetworks and networks that include photosynthetic combinato-ial states. Also it is very hard to develop visualization tools toresent general metabolic networks jointly with photosynthesis

n an illustrative and facile manner.Another challenge that needs to be addressed is the lim-

ted scope and coherence of annotation of photosyntheticomponents and reactions in existing public databases. In

articular, the current state of photosynthesis-related termsvailable in general annotation ontologies and repositories likeene Ontology (http://www.geneontology.org; Ashburner et al.,000), MetaCyc (http://www.metacyc.org; Chisti, 2008), KEGGhttp://www.genome.jp/kegg; Kanehisa et al., 2010) and BioMod-

is Project screen.

els (http://www.genome.jp/kegg; Le Novere et al., 2006), is notsufficient.

To overcome these problems, we initiated some years ago adedicated E-photosynthesis project (Nedbal et al., 2007, 2009;Orr and Govindjee, 2007; Rascher and Nedbal, 2006) aiming atgeneration of a comprehensive modeling space that would be cus-tomized for photosynthesis. This ambitious project had stumbleduntil recently on lacking capacity that was temporarily re-directedtowards writing and editing of the review book on photosynthesismodeling (Laisk et al., 2009). Lately, the E-photosynthesis projecthas resumed by complete remodeling of the web site that is basedon new software tools that are described here in Section 2. Thenewly created features are presented here in “work-in-progress”form of Section 3 demonstrating the features that are already avail-able in the new environment.

2. Methods

2.1. Model simulation methods

SBML-based modeling frameworks connect the models directly to a set ofwell-developed simulation methods. The methodology used to drive the simu-lation tasks in E-photosynthesis is based on ODEPACK algorithms (Hindmarsh,

118 D. Safránek et al. / BioSystems 103 (2011) 115–124

Fig. 4. Zooming in the scheme of photosynthetic structures and reactions.

D. Safránek et al. / BioSystems 103 (2011) 115–124 119

F ponea I datab

1pit(uodp(mu

2

meibiiR

ig. 5. List of components of the “Holzwarth 2006” model. When clicked, the comnd public databases. Here, the examples of public databases include EMBO-EBin/www bget?cpd:C05306).

980; Hindmarsh, 1983) providing a set of highly parameterized ODE solvers. E-hotosynthesis provides a simulation engine which is based on LSODE methods as

mplemented in the free computational tool Octave 3 (Eaton, 2002). Each simula-ion task is determined by a specific setting of the quantitative model parametersinitial conditions, reaction rates) as well as by appropriately tuned settings of sim-lation parameters. Thus, when dealing with simulations, the proper managementf all relevant data is needed in order to guarantee unique solution and repro-ucibility of simulation experiments. To this end, we employ the methodologyresented in the Minimal Information about a Simulation Experiment standardMIASE, http://biomodels.net/miase) that prescribes what information has to be

aintained together with the model data in order to completely capture the partic-lar simulation tasks.

.2. Model annotation methods

It is important to differentiate between models of the same subsystem thatight differ, e.g., by the time resolution of the detection system in the considered

xperiment. This is achieved in the model annotation that, typically, also includesnformation about the organism or structure that is modeled, the model output toe compared with the measured experimental signal, the time resolution at which

s the modeled signal captured, as well as external constraints such as irradiance orrradiance temporal modulation. To this end, we employ the Minimum Informationequested In the Annotation of Models (MIRIAM, http://biomodels.net/miriam; Le

nts are characterized by a short description and by links to annotations in localbase (http://www.ebi.ac.uk/) and KEGG database (http://www.genome.jp/dbget-

Novere et al., 2005), the proposed standard in systems biology for annotating modelsby model-specific information.

2.3. Framework architecture and technology

The architecture of E-photosynthesis is intended to unify the model presenta-tion with model analysis and model validation in a single framework. By modelpresentation, we mean that the user can simulate modeled dynamic behavior as ithas been already published and export data and the model for further use. Modelanalysis includes also modification of the model parameters, of initial conditions, ofrate constants and, in its most advanced form, also of the model structure. Model val-idation feature is considered as a framework for comparison of simulated behavioragainst wet-lab experiments. In particular, this is achieved by statistical comparisonof time-series data measured on the organism with respect to the data simulatedon the model with initial conditions mimicking the experimental conditions.

A scheme of E-photosynthesis framework is shown in Fig. 1. The two cores ofthe framework are the database engine and the simulation engine. Services of these

central modules are provided to users via web-based presentation and adminis-tration interfaces. The entire system is strongly modularized and segmented intoindividual libraries to facilitate easy future upgrades.

The database engine serves for data storage and as a querying center of twomutually interconnected databases – the annotations and models databases. Bothdatabases are deployed to a MySQL 5 database server (http://www.mysql.com).

120 D. Safránek et al. / BioSystems 103 (2011) 115–124

action

bwRiaief

wcoc

apheH

3

wwt

Fig. 6. List of reactions of the “Holzwarth 2006” model. When clicked, the re

The simulation engine serves the in silico experiment tasks as they are requestedy the users remotely via the presentation interface module. The simulation engineorks with the model translated to a set of ordinary differential equations (ODEs).obust numerical methods are employed to generate time-series data for given

nitial conditions and kinetic parameter sets. The simulation engine is prepared forny MATLAB-compatible ODE solver in terms of the standard API. In the currentmplementation, Octave is employed as computational platform for the simulationngine. The results of the simulation are sent back to the presentation module forurther processing and visualization.

The administration and presentation interfaces are implemented using dynamiceb technologies PHP 5.2 (http://www.php.net) and asynchronous server-to-client

ommunication. For the visual presentation of simulation results, we adapted thepen source Open Flash Chart 2 technology (http://teethgrinder.co.uk/open-flash-hart-2).

To optimize the computational power of the individual services, the toolrchitecture is prepared for deployment of the database, simulation, and bothresentation modules to four independent computing nodes interconnected viaigh-speed network links. Communication between libraries deployed to differ-nt machines is realized using Simple Object Access Protocol (SOAP) employed overTTP links.

. Results

Based on the methodology described in the previous section,e have implemented the second version of E-photosynthesis.orgebsite (http://www.e-photosynthesis.org) that currently offers

he following services available on-line:

s are characterized by initial and final states and by relevant rate constants.

• model repository customized for specific needs of photosynthesisresearch

• MIRIAM-compatible annotation database and ontology ofphotosynthesis-related terms

• graphical environment visualizing the models in a modular andhierarchical manner

• model examination module prepared for further deployment tohigh-performance platforms

• support for user enabled construction of new models by re-usingmodules of already existing model structures

• user specific interactive interface

The graphical user interface of E-photosynthesis starts with theIntroduction window (Fig. 2) that declares its close relationshipwith the “Photosynthesis in Silico” book (Laisk et al., 2009). The toolfunctionality features, “Introduction”, “Projects”, “Links”, “Team”,and “Contact” are available through the main menu tabs placed atthe top of the screen.

The most important functionality of E-photosynthesis is acces-

sible by clicking the “Projects” button. It supports browsing andexploring of models in the repository database. During the toolconstruction, only two models are publicly available in the modelrepository (http://www.e-photosynthesis.org) that are labelled“Holzwarth 2006” and “Laisk 2009”, indicating the principal

D. Safránek et al. / BioSystems 103 (2011) 115–124 121

nume

atHtisbteritEtmi

t

•

•

•

Fig. 7. List of parameters of the “Holzwarth 2006” model. When clicked, the

uthors and publication year. These two models are used for valida-ion of the construction progress and for testing the functionalities.ere, we are using the first modeling project “Holzwarth 2006”

o give an overview of the tool functionality and to demonstratets practical use. The model targets the primary events of photo-ynthetic energy conversion in Photosystem II that was publishedy Holzwarth et al. (2006). The implementation of the model inhe E-photosynthesis framework was discussed in detail in Nedbalt al. (2007, 2009). The second modeling project “Laisk 2009” is cur-ently presented in a less advanced form because the model alreadynvolves also the Calvin–Benson cycle and nitrogen metabolic pathshat introduce regulatory feedbacks. The model representation at-photosynthesis will be further refined during the next phase ofool development during which the support for multi-component

odeling with regulatory feedbacks among components will bemplemented.

The projects are represented in the graphical user interface ofhe E-photosynthesis tool in several ways (Fig. 3):

By the directory tree in the left part of the screen that presentsthe names of the available projects in terms of an expandable listof their subsystems and/or components (“AVAILABLE MODELINGPROJECTS” in Fig. 3).By a short model description, typically Abstract from the modelpublication (“SHORT MODEL DESCRIPTION” in Fig. 3).By a cartoon scheme (“SCHEME OF REACTIONS AND STRUC-TURES” in Fig. 3) in which individual subsystems or components

are highlighted when the cursor moves over. The functionalityalso highlights the models which include the particular sub-system or component (in Fig. 3, both “Holzwarth 2006” and “Laisk2009” are highlighted because they include the “Light reactions”module which was selected by the mouse pointer). Upon click-

rical values of the initial conditions and of the rate constants are displayed.

ing on the highlighted subsystem, one can zoom in to its detailedgraphical representation (Fig. 4).

• By the Components, Reactions, Parameters, and Annotations tabs(“MODEL DETAILS PANEL” in Fig. 3).

Note that the Project screen also offers simulation and export toSBML (“SIMULATION & EXPORT” in Fig. 3).

Zooming in as shown in Fig. 4 restricts also the selection of mod-els that include the selected component as well as it restricts theinformation about components, reactions, parameters and annota-tions.

The model details are presented in the left bottom partof the Project screen (“MODEL DETAILS PANEL” in Fig. 3)by model components, reactions, parameters and annotations.Short description and annotation links of particular modelcomponents can be opened by clicking (+) in the respectivecomponent list (Fig. 5). The annotations are typically takenfrom the local E-photosynthesis annotation database and frompublic databases like EBI (European Bioinformatics Institute,http://www.ebi.ac.uk/), KEGG (Kyoto Encyclopedia of Genes andGenomes, http://www.genome.jp/kegg/), GO (Gene Ontology,http://www.geneontology.org/) or others.

Further, the Reactions tab (Fig. 6) provides the list of reactionsconsidered in the model together with the reaction rates as theywere published with the respective model. The reaction rates canbe edited for an advanced simulation only by the registered usersand project leaders. The same applies also to the initial conditions

of the model that are available under the Parameters tab (Fig. 7).

The annotations relevant to the model are partly listed in theComponents tab and will also be included in the Reactions tab assoon as the reaction annotations are provided by the annotators.The remaining annotations describing the modeled experimen-

122 D. Safránek et al. / BioSystems 103 (2011) 115–124

GENE ONTOLOGY UniProt TAXONOMY

Components Reactions Parameters AnnotationsComponents

+ publication: Holzwarth et al., 2006

- cellular component: photosystem II

- cellular organism: Synechococcus elongatus

- cellular organism: Spinacia oleracea

- cellular component: membrane-derived thylakoid PSII

- cellular component: chloroplast

+ publication: Nedbal et al., 2007

- biological process: photosynthesis

- biological process: photosynthesis, light harvesting

A photosystem that contains a pheophytin-quinone reaction center with associated accessory pigments and electron carriers. In cyanobacteria and chloroplasts, in the presence of light, PSII functions as a water-plastoquinone oxidoreductase, transferring electrons from water to plastoquinone, whereas other photosynthetic bacteria carry out anoxygenic photosynthesis and oxidize other compounds to re-reduce the photoreaction center.GO:0009523

The synthesis by organisms of organic chemical compounds, especially carbohydrates, from carbon dioxide (CO2) using energy obtained from light rather than from the oxidation of chemical compounds.GO:0015979

Absorption and transfer of the energy absorbed from light photons between photosystem reaction centers. [source: GOC:sm] GO:0009765

Chloroplasts are semiautonomous arganelles comprising an envelope formed of two membranes, an aqueous matrix known as stroma, and an extensive system of internal membranes known as thylakoids. All of the light-harvesting and energy-transducing functions are located in the thylakoids." [source: local, PMID:15187262]

taxonomy:32046

taxonomy:3562

GO:0030096

F e annp logy.o(

tAn

tetcis

ibuTDS

taUtia

ig. 8. List of annotations of the “Holzwarth 2006” model. When clicked, thles of the databases include Gene Ontology database (http://amigo.geneontohttp://www.uniprot.org/taxonomy/32046).

al system or a particular experimental protocol are given in thennotations tab (Fig. 8). The links to public databases also offeravigation in an ontology tree.

Simulation environment gives access to quick on-line exploita-ion of model behavior based on predefined parameter sets. Forach project, the numerical simulation is accessible directly fromhe initial “Project” page (“SIMULATION & EXPORT” in Fig. 3). Thealculation takes place in the remote simulation engine (Fig. 1) ands presented to the user who requested the simulation by the screenhown in Fig. 9.

The graph presenting the simulation can be modified by adjust-ng the graph upper and lower limits (“ZOOMING” in Fig. 9) andy hiding some of the curves (click the legend). The numerical val-es can be inspected by moving the cursor over a particular curve.he simulation results can also be exported by clicking either “CSVata” or “XLS Data” at the bottom of the simulation graph (Comma-eparated Values or MS Excel formats).

Another important feature of the E-photosynthesis tool ishe export of the model in the SBML format. This feature is

vailable by clicking Export in the initial “Project” page (“SIM-LATION & EXPORT” in Fig. 3). The exported file is encoded in

he XML format that can be read by variety of solvers listedn http://sbml.org/SBML Software Guide. In this way, the modelvailable in E-photosynthesis repository can be further exploited

otation and links to the corresponding databases are displayed. Here exam-rg/cgi-bin/amigo/term-details.cgi?term=GO:0009523) and the UniProt database

and modified by the user outside the E-photosynthesis framework.As an alternative, advanced users may also change the model

parameters while remaining in the E-photosynthesis framework toobtain alternative simulations such as with different initial condi-tions and/or reaction rates. The results of such simulations togetherwith the respective model parameters can be stored, used later orshared with other registered participants – always connected tothe profile of the registered user. In this way, E-photosynthesis mayfacilitate not only storage and presentation of models from litera-ture but also support further development and continuous updateof the models.

E-photosynthesis also includes the administration interface (notshown) which is available to project leaders and managers. Itsmain purpose is to provide a platform for maintaining the assignedmodels. The interface simplifies project workflow by effectivemanipulation with individual model parts including creation andediting of reactions, component states, and parameter values. Inthis way, the model can be also expanded and its structure changed.Another important part of the administration is selection of the

numerical methods used for simulation of particular models. Thedefault parameters of ODE solver can be reset by the project man-agers to ensure robust simulation. Together with the simulationparameters, the project managers can also customize the visual-ization of calculated time-series.

D. Safránek et al. / BioSystems 103 (2011) 115–124 123

e “Ho

4

i(duimmfto

afdeom

pdpoitntrMqatLs

Fig. 9. Simulation of th

. Discussion

The biggest challenge in generating tools like E-photosynthesiss in errors that often occur in published models. Hucka and Schaff2009) estimated that 60–95% of published biological models areefective. Very often, the model is correct in the software that issed by the creator of the model but its description in the paper

s erroneous. In other cases, important model parameter can beissing in the paper. An individual reader cannot reconstruct theodel without excessive effort or without a constructive assistance

rom the model author. The same amount of work must be done byhe E-photosynthesis team but the result is then shared by all usersf the tool.

A more specific challenge of model repositories is in gener-ting functional connection between related models to obtainunctional modeling framework. It is because, almost as a rule,ifferent authors consider different assumptions and apply differ-nt approaches, e.g., order of a reaction and dimension and valuef related rate constant, use of absolute or relative quantities forodel state variables – see, e.g., Lazár and Jablonsky (2009).A meaningful integration of a new model to the E-

hotosynthesis modeling framework also requires a rigorousefinition of experimental quantities to be modeled. For exam-le, chlorophyll fluorescence emission is a radiative deactivationf excited states of chlorophylls and therefore, only models whichnclude the excited states as model state variables can adopthis definition. On the other hand, chlorophyll fluorescence sig-al is very often assumed to be proportional to the amount ofhe so-called closed reaction centers of Photosystem II (PSII). Thisaises a question of what are the closed reaction centers of PSII?ostly, these are considered to be the centers with the primary

uinone electron acceptor reduced (QA−), however, some author

lso include the centers with Pheo− or with Pheo−QA− (Pheo is

he primary electron acceptor, pheophytin; for more details seeazár and Schansker (2009)). Moreover, a term semi-closed (oremi-open) PSII centers (Pheo−QA or PheoQA

−) also appears in a

lzwarth 2006” model.

definition of fluorescence signal as well as a possibility of “dele-tion” of a closed status of PSII center by presence of oxidative stateof the donor side of PSII caused by the so-called donor side quench-ing (for more details see Vredenberg (2004)). Again, only modelswhich include QA and/or Pheo and the donor side of PSII can usethese definitions. Thus, the modeled kinetics of fluorescence emis-sion depends on the definition that is used for closed reaction ofPSII.

These challenges are addressed by designing E-photosynthesisas a modeling tool which keeps domain-specific models togetherwith precise annotation data. The quality of the annotation isboosted by the internal E-photosynthesis annotation database thatis used whenever the annotation data in open public databases areunavailable or insufficient. The internal annotation database rep-resents a significant contribution of E-photosynthesis to meetingthe MIRIAM criteria also in photosynthesis models.

Considering model analysis, the central concept of E-photosynthesis is on-line simulation. Advantages of including thisfunctionality directly by means of an on-line service are two-fold.First, the user can avoid multi-step process of exporting the modeland loading it into a particular off-line tool. That not only savestime but also reduces errors that often occur proportionally to thenumber of involved manipulations. Moreover, the numerical sim-ulation procedures in E-photosynthesis are already tuned by theproject administrators to give stable results for particular models.Thus, the properly tuned simulation engine of E-photosynthesis canabstract the user from non-trivial and counterintuitive settings ofthe simulation procedure – an advantage that is particularly impor-tant in the case of large-scale models requiring high-performancecomputational power.

Owing to the fact that the first development phase of E-

photosynthesis has been focused mainly on model presentationaspects, we leave another important functionality, the model val-idation support, for the nearest future work. In the preliminaryprototype implementation of E-photosynthesis, we already madefirst steps towards this goal. The realization of model validation

1 ystem

wfsTc

afavadeMKtdy2

A

AFMFE

R

A

B

B

B

B

EH

H

H

H

24 D. Safránek et al. / BioS

ill be realized in two aspects. The first aspect is to extend theeatures of simulation graph applet to allow quick visual compari-on of simulation curves against uploaded wet-lab measured data.he second aspect we aim to implement is more detailed statisticalomparison of simulated data against wet-lab data.

We believe that due to the strictly modular and loosely coupledrchitecture, the E-photosynthesis framework is well-prepared forurther extensions. We plan to further improve the tool function-lity, especially in the simulation engine and simulation resultsisualization. To allow extensive and tool independent analysis, weim to enable MIASE-compliant export of the simulated time-seriesata. To this end, we prepare the tool for exporting the simulationxperiments data in the form of Simulation Experiment Descriptionarkup Language (SED-ML, http://www.biomodels.net/sed-ml;

ohn and Le Novere, 2008). We also aim to support novel analysisechniques adapted from computer science. Especially, methods ofiscrete abstraction have proven fruitful for property-driven anal-sis and tuning of differential models (Barnat et al., 2010; Rizk et al.,008).

cknowledgements

LN and JC were supported by grants AV0Z60870520 (Czechcademy of Sciences), and by GACR 206/09/1284 (Czech Scienceoundation) as well as by Photon Systems Instruments, Ltd. DS,K, JP and LB were supported by GACR 201/09/1389 (Czech Science

oundation). DL was supported by MSM 6198959215 (Ministry ofducation of the Czech Republic).

eferences

shburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis,A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L.,Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M.,Sherlock, G., 2000. Gene ontology: tool for the unification of biology. Nat. Genet.25 (1), 25–29.

arnat, J., Brim, L., Safranek, D., 2010. High-performance analysis of biological sys-tems dynamics with the DiVinE model checker. Brief. Bioinform. 11 (3), 301–312.

aumert, H.Z., Petzoldt, T., 2008. The role of temperature, cellular quota and nutri-ent concentrations for photosynthesis, growth and light-dark acclimation inphytoplankton. Limnol. Ecol. Manage. Inland Waters 38 (3–4), 313–326.

eard, D.A., Britten, R., Cooling, M.T., Garny, A., Halstead, M.D.B., Hunter, P.J., Lawson,J., Lloyd, C.M., Marsh, J., Miller, A., Nickerson, D.P., Nielsen, P.M.F., Nomura, T.,Subramanium, S., Wimalaratne, S.M., Yu, T., 2009. CellML metadata standards,associated tools and repositories. Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci.367 (1895), 1845–1867.

rennan, L., Owende, P., 2010. Biofuels from microalgae – a review of technologiesfor production, processing, and extractions of biofuels and co-products. Renew.Sust. Energy Rev. 14 (2), 557–577.

aton, J.W., 2002. GNU Octave Manualed. Network Theory Limited.indmarsh, A.C., 1980. LSODE and LSODI, two new initial value ordinary differnetial

equation solvers. SIGNUM Newsl. 15 (4), 10–11.indmarsh, A.C., 1983. In: Stepleman, R.S., et al. (Eds.), ODEPACK, a Systematized

Collection of ODE Solvers. IMACS Transactions on Scientific Computation, vol. 1.North-Holland, Amsterdam, pp. 55–64.

olzwarth, A.R., Müller, M.G., Reus, M., Nowaczyk, M., Sander, J., Rögner, M., 2006.Kinetics and mechanism of electron transfer in intact photosystem II and in theisolated reaction center: pheophytin is the primary electron acceptor. Proc. Natl.Acad. Sci. U.S.A. 103 (18), 6895–6900.

ucka, M., Finney, A., Sauro, H., Bolouri, H., Doyle, J., Kitano, H., Arkin, A., Bornstein,B., Bray, D., Cornish-Bowden, A., Cuellar, A., Dronov, S., Gilles, E., Ginkel, M.,Gor, V., Goryanin, I., Hedley, W., Hodgman, T., Hofmeyr, J.-H., Hunter, P., Juty, N.,Kasberger, J., Kremling, A., Kummer, U., Le Novére, N., Loew, L., Lucio, D., Mendes,P., Minch, E., Mjolsness, E., Nakayama, Y., Nelson, M., Nielsen, P., Sakurada, T.,Schaff, J., Shapiro, B., Shimizu, T., Spence, H., Stelling, J., Takahashi, K., Tomita,

s 103 (2011) 115–124

M., Wagner, J., Wang, J., 2003. The systems biology markup language (SBML):a medium for representation and exchange of biochemical network models.Bioinformatics 19 (4), 524–531.

Hucka, M., Schaff, J., 2009. Trends and tools for modeling in modern biology. In:Laisk, A., Nedbal, L., Govindjee (Eds.), Photosynthesis in Silico: UnderstandingComplexity from Molecules to Ecosystems. Springer, Netherlands, Dordrecht,pp. 3–15.

Chisti, Y., 2008. Biodiesel from microalgae beats bioethanol. Trends Biotechnol. 26(3), 126–131.

Jansson, C., Northen, T., 2010. Calcifying cyanobacteria – the potential of biomineral-ization for carbon capture and storage. Curr. Opin. Biotechnol. 21 (3), 365–371.

Jiao, N., Herndl, G.J., Hansell, D.A., Benner, R., Kattner, G., Wilhelm, S.W., Kirchman,D.L., Weinbauer, M.G., Luo, T., Chen, F., Azam, F., 2010. Microbial production ofrecalcitrant dissolved organic matter: long-term carbon storage in the globalocean. Nat. Rev. Microbiol. 8 (8), 593–599.

Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., Hirakawa, M., 2010. KEGG forrepresentation and analysis of molecular networks involving diseases and drugs.Nucleic Acids Res. 38 (Suppl. 1), D355–360.

Kohn, D., Le Novere, N., 2008. SED-ML: an XML format for the implementation ofthe MIASE. In: Computational Methods in Systems Biology. Springer-Verlag,Rostock, Germany, pp. 176–190.

Laisk, A., Nedbal, L., Govindjee, 2009. Photosynthesis in Silico: Understanding Com-plexity from Molecules to Ecosystems, first ed. Springer, Dordrecht, Netherlands.

Lal, R., 2008. Carbon sequestration. Philos. Trans. Roy. Soc. B: Biol. Sci. 363 (1492),815–830.

Lazár, D., Jablonsky, J., 2009. On the approaches applied in formulation of a kineticmodel of photosystem II: different approaches lead to different simulations ofthe chlorophyll a fluorescence transients. J. Theor. Biol. 257 (2), 260–269.

Lazár, D., Schansker, G., 2009. Models of chlorophyll a fluorescence transients. In:Laisk, A., Nedbal, L., Govindjee (Eds.), Photosynthesis in Silico: UnderstandingComplexity from Molecules to Ecosystems. Springer, Dordrecht, Netherlands,pp. 85–123.

Le Novere, N., Finney, A., Hucka, M., Bhalla, U.S., Campagne, F., Collado-Vides, J.,Crampin, E.J., Halstead, M., Klipp, E., Mendes, P., Nielsen, P., Sauro, H., Shapiro, B.,Snoep, J.L., Spence, H.D., Wanner, B.L., 2005. Minimum information requestedin the annotation of biochemical models (MIRIAM). Nat. Biotechnol. 23 (12),1509–1515.

Le Novere, N., Bornstein, B., Broicher, A., Courtot, M., Donizelli, M., Dharuri, H., Li,L., Sauro, H., Schilstra, M., Shapiro, B., Snoep, J.L., Hucka, M., 2006. BioModelsdatabase: a free, centralized database of curated, published, quantitative kineticmodels of biochemical and cellular systems. Nucleic Acids Res. 34 (suppl 1),D689–691.

Nedbal, L., Cerveny, J., Rascher, U., Schmidt, H., 2007. E-photosynthesis: a compre-hensive modeling approach to understand chlorophyll fluorescence transientsand other complex dynamic features of photosynthesis in fluctuating light. Pho-tosynth. Res. 93 (1), 223–234.

Nedbal, L., Cerveny, J., Schmidt, H., 2009. Scaling and integration of kinetic models ofphotosynthesis: towards comprehensive E-photosynthesis. In: Laisk, A., Nedbal,L., Govindjee (Eds.), Photosynthesis in Silico: Understanding Complexity fromMolecules to Ecosystems. Springer, Dordrecht, Netherlands, pp. 17–29.

Niinemets, Ü., Valladares, F., 2004. Photosynthetic acclimation to simultaneous andinteracting environmental stresses along natural light gradients: optimality andconstraints. Plant Biol. (Stuttg.) 6 (03), 254–268.

Olivier, B.G., Snoep, J.L., 2004. Web-based kinetic modelling using JWS Online. Bioin-formatics 20 (13), 2143–2144.

Orr, L., Govindjee, 2007. Photosynthesis and the Web: 2008. Photosynth. Res. 91 (2),107–131.

Rascher, U., Nedbal, L., 2006. Dynamics of photosynthesis in fluctuating light. Curr.Opin. Plant Biol. 9 (6), 671–678.

Raven, J.A., Cockell, C.S., De La Rocha, C.L., 2008. The evolution of inorganic carbonconcentrating mechanisms in photosynthesis. Philos. Trans. Roy. Soc. B: Biol. Sci.363 (1504), 2641–2650.

Riding, R., 2009. An atmospheric stimulus for cyanobacterial-bioinduced calcifica-tion ca 350 million years ago? Palaios 24 (10), 685–696.

Rizk, A., Batt, G., Fages, F., Soliman, S., 2008. On a continuous degree of satisfactionof temporal logic formulae with applications to systems biology. In: Heiner,M., Uhrmacher, A. (Eds.), Computational Methods in Systems Biology. Springer-Verlag, Berlin Heidelberg, pp. 251–268.

Vredenberg, W.J., 2004. System analysis and photoelectrochemical control of chloro-phyll fluorescence. terms of trapping models of photosystem II: a challengingview. In: Papageorgiou, G.C., Govindjee (Eds.), Chlorophyll a Fluorescence: ASignature of Photosynthesis. Springer, Dordrecht, Netherlands, pp. 133–172.

Zhu, X.-G., Long, S.P., Ort, D.R., 2010. Improving Photosynthetic Efficiency for GreaterYield. Annu. Rev. Plant Biol. 61 (1), 235–261.