CyanoFactory Knowledge Base & Synthetic Biology

A Plea for Human Curated Bio-databases

Gabriel Kind, Eric Zuchantke and R

obbe W

unschiers

University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany

Keywords:

Synthetic Biology, Big Data, Omics, Warehouses, Databases, Knowledge-base, Wisdom of the Crowd.

Abstract:

Nowadays, life science research is dominated by two conditions: interdisciplinarity and high-throughput.

The former leads to highly diverse datasets from a data type point of view while high-throughput yields

massive amounts of data. Both aspects are reﬂected by the byte-growth of public bio-databases and the sheer

number of specialised databases or databases of databases (i.e. data warehouses). We provide an insight to the

development of a biodata knowledge base (dubbed CyanoFactory KB) targeted to bio-engineers in the ﬁeld of

synthetic biology and exemplify the need for data type speciﬁc data curation and cross-linking. CyanoFactory

KB is unique in incorporating experimental data from a broad range of scientiﬁc methods that are based on one

strain of Synechocystis sp. PCC 6803. The knowledge base can be accessed upon request via cyanofactory.hs-

mittweida.de.

1 INTRODUCTION

Nowadays, life science research is dominated by two

conditions: interdisciplinarity and high-throughput.

The former leads to highly diverse datasets from a

content point of view while high-throughput yields

massive amounts of data. Both aspects are reﬂected

by the byte-growth of public bio-databases and the di-

versity of specialised databases (see, e.g. the database

issues of the NAR journal). However, quite often

more data leads to less understanding. Driven by

the methodology of systems biology, a holistic view

of genetic and metabolic regulatory processes is de-

manded. One important goal is the application of

these systemic data for in silico modelling of bio-

logical processes or, ultimately, biological systems,

i.e. cells, tissues, organisms. One step towards this

goal was the successful prediction of the phenotype

from the genotype in Mycoplasma genitalium (Karr

et al., 2012). The basis to solve this challenge was a

database named WholeCell Knowledge Base (Whole-

Cell KB) (Karr et al., 2013). It contains experimental

results from over 900 publications and includes more

than 1,900 experimentally observed parameters. Im-

portantly, all data has been validated and curated by

scientists.

Another important manually curated database is

Brenda, which contains almost 1.5 million manu-

ally curated enzyme parameters (as of July 2014,

brenda-enzymes.org). In contrast, GenBank con-

tains 174,108,750 individual and 189,080,419 whole

genome shotgun sequences (as of August 2014,

ncbi.nlm.nih.gov) that are partially manually up-

loaded but not curated. In the ﬁeld of cyanobacte-

ria research, CyanoBase is a well-known manually

curated genome database, including over 5200 refer-

ences (Fujisawa et al., 2014).

With the rising amount of biological data and the

increasing capabilities of computer hardware, many

attempts have been undertaken to automatically har-

vest, store, cross-link and provide biological data in

databases and databases of databases (i.e. data ware-

houses) (Triplet and Butler, 2011). We argue that au-

tomatically generated data collections are of limited

value (the common garbage-in garbage-out problem),

especially in the context of large scale biological en-

gineering as envisioned by the ﬁeld of synthetic biol-

ogy. In this ﬁeld, computer modelling of biological

processes builds the base to targeted (instead of trial-

and-error) genetic engineering.

With this paper we provide an insight in the devel-

opment of a biodata-warehouse (CyanoFactory KB)

targeted to bio-engineers in the ﬁeld of synthetic biol-

ogy. We are in the extraordinary situation to work in

a research consortium that consists of partners from

different scientiﬁc ﬁelds (interdisciplinary) and seven

different countries (multiregional) with the unifying

goal to tinker the cyanobacterium Synechocystis sp.

237

Kind G., Zuchantke E. and Wünschiers R..

CyanoFactory Knowledge Base & Synthetic Biology - A Plea for Human Curated Bio-databases.

DOI: 10.5220/0005285802370242

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2015), pages 237-242

ISBN: 978-989-758-070-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

PCC 6803 to produce photohydrogen. We are par-

ticularly working at the interface between experimen-

tal and computational biology, which implies differ-

ent understanding of data (Figure 1). CyanoFactory

KB, which is a massive expansion of the WholeCell

KB, shall provide a central data hub for members of

the consortium and for disseminating project data to

the research community. Thus, it shall provide ways

for an improved collaboration between all partners

within the CyanoFactory consortium. All partners

are experts in different ﬁelds from microbial biotech-

nology or metabolic modelling up to synthetic biol-

ogy. The goal of the knowledge base is to bridge

the gap between bio-engineers and bioinformaticians

by providing user friendly functionalities for working

with experimental data and for visualising and con-

textualising it in different ways. Besides experimen-

tal data, further data is obtained from other biological

databases.

Figure 1: Different ways of thinking about data.

CyanoFactory KB is continuously improving and

adjusted to new requirements of the research partners.

The warehouse is still under heavy development and

gains new features every month. It is not available

to the general public yet, this step is planned when

the codebase is more matured and more experimental

data of the partners has been integrated.

2 MATERIALS AND METHODS

CyanoFactory KB is a massive expansion of Whole-

Cell KB (Karr et al., 2013) and was further adopted

to the needs of CyanoFactory. WholeCell KB turned

out to be the best suited and matured bio-warehouse

system after intensive scouting and testing of various

known open-source systems (Table 1).

Due to different responsibilities, different tech-

nologies and data types are provided and demanded

by individual partners, e.g. mass spectrometry data,

DNA-microarray- or RNASeq-based transcription

analyses, metabolic analyses, mathematical mod-

ells, sequence based data from genetic engineer-

Table 1: Intensively tested open-source biodata-

warehouses.

Warehouse Citation

BioDWH (T

opel et al., 2008)

BioMart Central Portal (Guberman et al., 2011)

BioWarehouse (Lee et al., 2006)

BioXRT (Zhang et al., 2004)

BN++ (K

untzer et al., 2007)

CoryneRegNet (Baumbach, 2007)

DAWIS-M.D. (Hippe et al., 2010)

InterMine (Lyne et al., 2007)

ONDEX (Taubert et al., 2014)

Open Genome Resource (Klein et al., 2009)

PROFESS (Triplet et al., 2010)

PiPa (Arzt et al., 2011)

RAMEDIS (T

opel et al., 2010)

WholeCell KB (Karr et al., 2013)

ing or climate and experimental data from outdoor

photo-bioreactor experiments. All experiments are

performed using the model organism Synechocys-

tis sp. PCC 6803 as provided from the University

of Uppsala/Sweden (Uppsala subtype). This sub-

type has been resequenced, compared to the orig-

inal sequence on GenBank and analysed for ge-

netic variations. Additional data was obtained from

other biological databases: Organism related infor-

mation from GenBank, pathway data from KEGG

(Kanehisa et al., 2014) and Boehringer Biochemical

Pathways Maps (Michal and Schomburg, 2012) and

protein-protein interactions from STRING (Frances-

chini et al., 2013) and STITCH (Kuhn et al., 2014).

Furthermore CyanoDesign provides a unique inter-

face for metabolic modelling and analyses of the efﬁ-

ciency of enzymatic reactions via ﬂux balance analy-

sis.

3 RESULTS

CyanoFactory KB is a productive knowledge base,

which handles all the information from our partners.

The advantage of our solution is that, besides holding

information, it provides different visualisation tech-

niques and cross-links to other data sources.

Uploading of experimental data is supported in

different formats. The warehouse provides import

functionality for FASTA, GenBank and System Biol-

ogy Markup Language (SBML). The import runs as a

background job and is automatically merged into the

current dataset upon completion. All modiﬁcations to

the knowledge base are stored as revisions, therefore

changes to all items can be retrieved and rolled back if

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necessary. Exporting is possible in the import formats

and furthermore in machine readable XML or JSON

formats. The access to individual resources can be

restricted using permissions.

Besides the hierarchical view of the warehouse the

user can group selected data in “baskets”. A user can

create different baskets and group relevant items in

them.

The general structure of the organism is visual-

ized by using a chromosome viewer (Figure 2). The

viewer is fully interactive and provides ﬁltering func-

tionalities. Additional metadata is displayed beneath

the genes. In our case these are SNPs obtained from

the Uppsala subtype of Synechocystis sp. PCC 6803.

When selecting a gene or SNP additional metadata for

the corresponding component is displayed.

Figure 2: Chromosome viewer showing SNPs (triangles be-

low the genes) from the Uppsala subtype of Synechocystis

sp. PCC 6803.

Due to possible mutations in Synechocystis sp.

PCC 6803 over time the strand used in experiments

by the Uppsala university was resequenced, analyzed

and the sequence modiﬁcations uploaded into the

knowledge base. The newly sequenced genome was

aligned to the reference sequence. Many genes of the

Uppsala strain contain SNPs (Table 2). It is currently

investigated whether these inﬂict noticeable changes

to the metabolic system of the organism.

Table 2: Mutations in chromosome of Synechocystis sp.

PCC 6803.

Chromosome

Number of SNPs 732

SNPs on Genes 511

Genes 6462

Modiﬁed Genes 1028

The metabolic processes of Synechocystis sp.

PCC 6803 and interactions of biochemical compo-

nents are visualized using the Process Description

Language of the Systems Biology Graphical Nota-

tion (SBGN) (Le Novere et al., 2009). SBGN rep-

resents the metabolic model of the organism in a way

detailed enough for biochemists and is machine read-

able, therefore supporting mathematical simulations

inside the model.

It should, however, be noted that SBGN proofed to

be confusing to the human eye. Thus, traditional visu-

alisations such as Boehringer Pathway Maps (Michal

Figure 3: Part of the metabolic model of Synechocystis sp.

PCC 6803 rendered in Systems Biology Graphical Notation

(SBGN).

and Schomburg, 2012) or KEGG maps (Kanehisa

et al., 2014) are to be preferred.

Pathway Visualisation

The enzymes and metabolites of Synechocystis sp.

PCC 6803 are displayed on different pathway maps.

Biochemical Pathways provides a detailed overview

about chemical reactions. A small excerpt with high-

lighted enzymes contained in Synechocystis sp. PCC

6803 is visualised in Figure 4.

All found enzymes and metabolites are high-

lighted on individual KEGG pathway maps (Figure

5). When an item was detected the image is ﬁlled in

green for enzymes and red for metabolites. Pathways

are cross-linked to each other.

Custom searches, independent of the organism,

are supported on all pathway maps. All search queries

can be saved for later reuse.

Figure 4: Boehringer Pathway Map.

Metabolic Modelling

Metabolic modelling is provided as part of Cyano-

Design. Flux balance analysis (FBA) is used for the

reconstruction of metabolic networks of organisms

(Figure 6). A metabolic network consists of multi-

ple enzymatic reactions with metabolites contained in

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Figure 5: KEGG Pathway Map.

a stoichiometric matrix (positive for production, neg-

ative for degradation). This network is solved us-

ing a linear solving method. The motivation behind

CyanoDesign is allowing the bio-engineer to change

the metabolic network in silico and to get a predic-

tion how the organism will behave. A modelling ap-

proach saves valuable time because it gives hints how

mutants of the organism behave, resulting in a high

amount of saved work in the lab. FBA is done using

the library PyNetMet (Gamermann et al., 2014). For

improved quality of the simulation results the addi-

tion of more advanced algorithms like “Minimization

of Metabolic Adjustment” (MOMA) is planned.

Figure 6: Metabolic model with calculated ﬂuxes. The cal-

culated amount of ﬂux is displayed on top of the arrows and

indicated by there thickness.

Interactions

CyanoInteraction provides visualisation of protein-

protein-interactions and protein-metabolite-interac-

tions of selected proteins and metabolites from Syne-

chocystis sp. PCC 6803 (Figure 7). The dataset used

is based on data from STRING (Franceschini et al.,

2013) and STITCH (Kuhn et al., 2014). The in-

teractions are displayed as undirected graphs. Only

the most signiﬁcant interaction partners are displayed.

The signiﬁcance is calculated based on the STRING

and STITCH interaction scores. This score is based

on, among others, homology, coexpression and text

mining. The visualisation is completely interactive

and adjustable to the users needs.

Figure 7: Graph displaying protein and chemical interac-

tions. Shorter edges between nodes mean higher scores.

4 CONCLUSION

With the help of the web-based CyanoFactory knowl-

edge base it is possible to concentrate information

from a single organism and its derived mutants. The

more information and the more cross-links between

these information can be established, the better is

the understanding of the selected organism. The

CyanoFactory KB is still under development. Thus,

later versions of the warehouse will see more func-

tions and experimental data.

From previous interactions between, and from ex-

periences by the partners in the research consortium

it became clear that automatically collected data sets

are too error prone. Thus, the CyanoFactory KB sets

the stage to examine the effort and value of human

curated databases and warehouses.

It is important to note the importance to put

experimental and computational results about one

particular bacterial strain under one umbrella. This

demands management decisions within a research

consortium or even a research community. The re-

search consortium CyanoFactory (cyanofactory.eu)

works with the model organism Synechocystis sp.

PCC 6803. The Kazusa strain of Synechocystis sp.

PCC 6803 was the ﬁrst photosynthetic prokaryote

whose genome sequence has been determined in 1996

(Kaneko et al., 1996). Besides the sister strains PCC

(Pasteur Culture Collection), ATCC (American Type

Culture Collection) and GT (glucose tolerant), the

Kazusa strain has been derived from one original Cal-

ifornia freshwater isolate from 1971, the Berkeley

strain (Stanier et al., 1971). Recently, it has been

shown that all sister strains can be distinguished by

single nucleotide polymorphisms and indels (Ikeuchi

and Tabata, 2001; Kanesaki et al., 2012; Trautmann

et al., 2012). Furthermore, many sub- or laboratory

strains have been derived from all four strains. This

leads to experimental and computational results based

on different genetic backgrounds. Ultimately, this

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may lead to non-comparable results. Thus, when inte-

grating data in a knowledge base, detailed information

about the experimental setup and the genetic back-

ground are necessary. Only then, valid and consistent

metabolic models can be derived.

The CyanoFactory KB takes a ﬁrst step in the di-

rection of strain speciﬁc databases. This, however,

requires a high investment in man-month of skilled

personnel. Another important problem that we are

currently addressing is the way of data integration

from diverse data sources. While the systems biology

markup language (SBML) provides a good founda-

tion for data exchange, strong effort has to be invested

in data-pipeline setup. On the long run, research con-

sortia need special funding solely directed to manual

data(base) curation. In return, a sustainable and co-

herent data source for follow-up research can be es-

tablished.

ACKNOWLEDGEMENT

We want to thank Eric Frenzel for the creation of the

SBGN networks. This project has received funding

from the European Union’s Seventh Programme for

research, technological development and demonstra-

tion under grant agreement No 308518, CyanoFac-

tory.

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