CELLMICROCOSMOS 4.1

An Interactive Approach to Integrating Spatially Localized

Metabolic Networks into a Virtual 3D Cell Environment

Björn Sommer*, Jörn Künsemöller, Norbert Sand, Arne Husemann, Madis Rumming

and Benjamin Kormeier

Bio-/Medical Informatics Department, University of Bielefeld, Germany

Keywords: Cell visualization, Pathway visualization in 3D, KEGG, BRENDA, UniProt.

Abstract: The high potential of Bioinformatics research concerning quantitative and qualitative data acquisition such

as data warehouses, spatial structure prediction and 3D microscopy conveys the vision of generating a

computational virtual cell. This paper discusses an approach which allows the creation and exploration of an

abstract compartmented cell environment, which can be used for (semi-)automatic, species- and organelle-

specific mapping and the comparison of metabolic data.

1 INTRODUCTION

During the last few years initial alternatives to the

well-established 2D view of networks have been

developed. The growing amount of data led to the

idea of introducing techniques to extend the two

visual dimensions widely used in the scientific

community by a third one: Biological pathways are

now visualized with different layout algorithms to

give a better understanding of the biological data

structure. On the other hand there is the cell

visualization and simulation area which already

partially deals with three dimensional cell models,

but none of these approaches tries to visualize

compartmental interrelationships cell internally in 3D.

CELLmicrocosmos 4.1 PathwayIntegration

(CmPI) introduces a novel approach of interactively

combining abstract or 3D Microscopy based cell

components with metabolic networks. To take full

advantage of 3D visualization, different comparing

methods and layouts, a user-optimized navigation

and 3D Stereoscopy have been integrated. To

support the correlation of organelle internal

localization levels with the involved enzymes,

information provided in the databases BRENDA

(Chang et al., 2009) and UniProt (The UniProt

Consortium, 2008) are obtained.

The CELLmicrocosmos (Cm) project is intended

to develop tools to provide the creation and

exploration of synthetic cell environments for

different purposes. It is intended to be applied to

scientific as well as educational purposes in the future.

2 RELATED WORKS

The scope of the application introduced here is

defined by two main areas: the pathway visualization

in 3D and the cell visualization and simulation.

2.1 Pathway Visualization in 3D

One of the initial approaches to visualize metabolic

pathways in 3D was already aimed at the avoidance

of unaesthetic aspects known from 2D visualizations

like line crossing and the reduction of topological

constraints (Rojdestvenski, 2003). Additionally, it

provided an opportunity to export the results to

VRML (Virtual Reality Modeling Language) and

followed the Focus+Context paradigm (Robinson &

Flores, 1997): The user can focus on a point of

interest (POI) while still able to keep track of the

complex network structure. The mentioned features

are also implemented in CmPI.

Another approach lies in the 2.5D Visualization

of metabolic networks (Fung et al., 2008), which

offers comparison methods for two different

biological networks: On the first 2D layer a

metabolic pathway is presented, on layer two a

protein interaction network and on the third layer,

located in the middle of the 3D space, the

overlapping nodes are shown. In other 2.5D

Sommer B., Künsemöller J., Sand N., Husemann A., Rumming M. and Kormeier B. (2010).

CELLMICROCOSMOS 4.1 - An Interactive Approach to Integrating Spatially Localized Metabolic Networks into a Virtual 3D Cell Environment.

In Proceedings of the First International Conference on Bioinformatics, pages 90-95

DOI: 10.5220/0002692500900095

 SciTePress

visualization approaches the layer concept is used

for the inter-organismic (Brandes, Dwyer &

Schreiber, 2004) or inter-domain large-scale

(Pavlopoulos et al., 2008) comparison of related

metabolic networks. CmPI distributes pathways of

different organisms in three dimensional space and

also integrates a layer paradigm, but focused on the

spatial segmentation of cellular components.

Another analogy with those 2.5D approaches is the

use of KEGG (Kanehisa et al., 2008) as the

metabolic data source.

MetNetVR introduced a possibility of visualizing

complex large-scale, hierarchical networks

interactively by implementing different 3D layout

algorithms (Yang et al., 2006). Virtual Reality

techniques are used to extend displays into the third

dimension. For CmPI, 3D Stereoscopy has been

integrated which is compatible with workstation as

well as professional back-projection concepts. In

addition, the network layouts of MetNetVR may

follow the cellular compartimentation. But in

comparison to the CmPI approach, it is not possible

to distinguish between different cell component

layers or to integrate different cell component

models and localizations interactively.

BioCichlid is another tool which visualizes and

animates time-dependent gene expression data,

correlated with protein interaction, signalling and

regulatory networks in 3D (Ishiwata et al., 2009).

CmPI does not implement time-related data so far.

2.2 Cell Visualization and Simulation

Cell simulation environments have been extended

from 2D to 3D during the last few years:

CompuCell3D is a software framework to

simulate the development of multicellular organisms

with stochastic rules and differential equations

(Merks & Glazier, 2005). E-Cell3D is implementing

meta-algorithms also based on differential equations

to simulate nonlinear interactions between functional

modules. In addition, parallelization of complex

mathematical analysis is provided (Sugimoto et al.,

2005). The Virtual Cell simulation environment

(VCell) allows the formulation and simulation of cell

biological models in 3D (Loew & Schaff, 2001).

The cell models of the mentioned approaches are

based on a very high grade of simplification,

because the effort for the computation increases with

the model complexity. In contrast, CmPI focuses on a

single cell at a structurally higher detail level, taking

cell components and their layers into account, but it

is not intended as a simulation environment so far.

Meta!Blast is a pedagogic entertainment

software in development. The aim is to create a

gaming experience for students, which links the

molecular and anatomic level (Call et al., 2006).

3 IMPLEMENTATION

3.1 The 3D Cell Environment

For many visualization applications it is sufficient to

integrate abstractly designed cell components which

are based on 2D electron and/or confocal laser

microscopy scans, supporting a clear data

arrangement and high-performance rendering.

Because the results of 3D microscopy are improving

and partly made freely available to the community

(Martone et al., 2002), we exemplarily integrated a

mitochondria model based on 3D electron

tomography (Figure 2) to create a linkage between

abstract/functional and realistic/structural data.

The cell components are imported as VRML97

models and provide different layers. For instance,

the mitochondrion consists of five shapes featuring

capability to map data to the matrices, the

inner/outer membranes, the intermembrane spaces

and clouds (Table 3).

3.2 Data Integration

The system should be open to make use of a wide

range of data sources: It connects to a data

warehouse integrating a number of life-science-

relevant databases: BioDWH (Töpel et al., 2008).

Metabolic data and their relations are visualized

as networks that are generated by an integrated

version of KEGG (Kanehisa et al., 2008). The 3D

cell environment contains enzymes and compounds

(products and substrates) which are involved in

different metabolic pathways. The connecting lines

show the reactions including the direction.

The localization information needed for the

spatial placement of enzymes is retrieved from

BRENDA (Chang et al., 2009) and UniProt (The

UniProt Consortium, 2008).

3.3 Data Mapping

3.3.1 Cell Component Related Mapping

Table 1 shows an overview of automatic localization

results coming exclusively from BRENDA or UniProt.

Best results are achieved by combining the data for

the Citrate Cycle: All enzymes are localized (Table 2).

Focussing exemplary on mitochondria, UniProt

contains more than 50, BRENDA more than 20

CELLMICROCOSMOS 4.1 - An Interactive Approach to Integrating Spatially Localized Metabolic Networks into a

Virtual 3D Cell Environment

different localization definitions; Table 3 shows in

the left column an aperture.

Table 1: A comparison between the results achieved

exclusively from BRENDA or UnitProt for homo sapiens.

Pathway BRENDA UniProt

Glycolysis 62% 70%

Citrate Cycle 70% 82%

Fatty Acid Biosynthesis 33% 16%

Fatty Acid Elongation in

Mitochondria

100% 80%

Fatty Acid Metabolism 91% 71%

Pyruvate Metabolism 70% 70%

Table 2: An overview of combined localization results

coming from BRENDA and UnitProt for homo sapiens.

Pathway (KEGG identifier) Localization

Glycolysis (hsa00010) 22/24 (91%)

Citrate Cycle (hsa00020) 17/17 (100%)

Fatty Acid Biosynthesis (hsa00061) 2/6 (33%)

Fatty Acid Elongation in Mitochondria

(hsa00062)

5/5 (100%)

Fatty Acid Metabolism (hsa00071) 12/12 (100%)

Pyruvate Metabolism (hsa00620) 19/20 (95%)

Investigating definitions, the problem evolves

that different terms belong to the same localization.

The mapping information for CmPI is stored in a

HashMap. For example ‘mitochondrial inter-

membrane space’ and ‘mitochondrial lumen’ are

both mapped onto the 3

mitochondrial layers

(Table 3, right column).

Table 3: An exemplary comparison of the mitochondrial

localizations found in BRENDA and the mapping to cell

components in CmPI.

BRENDA Localization CmPI Localization

mitochondrial matrix 1. Matrix

mitochondrial inner

membrane

2. Inner Membrane

mitochondrial respiratory

chain complex I

mitochondrial

intermembrane space

3. Intermembrane Space

mitochondrial lumen

mitochondrial outer

membrane

4. Outer Membrane

mitochondria

mitochondrial cloud 5. Cloud

3.3.2 Localizing an Enzyme

An interactive localization table offers opportunities

to compare and inspect corresponding references

and, if necessary, to adjust the automatic localization

settings. Loading the Citrate Cycle for Homo

Sapiens from KEGG, the enzyme 1.2.4.2

(Oxoglutarate dehydrogenase) will appear in the

table in addition to other enzymes. CmPI provides

all organism- and enzyme-specific localizations

derived from BRENDA and UniPro and also allows

the user to predict manually the localization.

Figure 1: Using BRENDA for the localization of EC

1.2.4.2, the term ‘mitochondrial inner membrane’ was

extracted from the publication shown in the Pop-up frame

and in the left column. The number in brackets indicates

the different publications pointing to the same localization.

In the right column the number of different localizations is

shown. For the selected enzyme, two localizations have

been found and the first one is selected.

The user can choose onto which cell component

the enzyme should be placed. There are three

qualitative mapping options:

 Reviewed Localization (BRENDA and

UnitProt): The database entry has been

reviewed by a curator.

 Unreviewed Localization (UnitProt): The

database entry has not been reviewed.

 Predicted Localization: No database entry has

been found, the user has to localize manually.

In addition the user can decide onto which cell

component layer the enzyme should be mapped. If

the localization term is imprecise, there are two

options for the user to check the localization: He

may read the commentaries which are sometimes

provided by the database and shown in the

localization table or he may review the publications

(Figure 1), which are additionally often directly

linked to the abstracts of the PubMed webpage.

If the database entry contains only

‘mitochondria’ and there are no additional

comments coming from the database or the

publication, the user has to predict the layer. In case

of the mitochondrion, CmPI automatically maps an

enzyme with the term ‘mitochondria’ onto the outer

membrane (Table 3).

Even if there is a precise localization term, it

may be reasonable to read the corresponding

publications found in the localization table: For

enzyme 1.2.4.2 Hunter and Lindsay (1986) (Figure

1) mentioned, that it might be localized at the

mitochondrial inner membrane as well as the matrix.

If there are too few localization results for some

organism/pathway combinations, the user can

download the localization for all organisms and try

to search for comparable results.

BIOINFORMATICS 2010 - International Conference on Bioinformatics

Figure 2: A mitochondria model manually segmented and

modelled from a tomographic reconstruction of isolated

liver mitochondria from Yamaguchi et al. (2008),

correlated with the citrate cycle of homo sapiens. The

importance of a well-defined navigation is shown by the

spatial complexity: It dissolves when navigating in 3D,

especially if 3D Stereoscopy is used. Figure 3 shows the

same network in 2D.

3.3.3 Placing Nodes in 3D Space

For the mapping of enzyme nodes in 3D space the

Inverted Self-Organizing Graphs (ISOM) layout

algorithm was adapted, distributing nodes onto Unit

Hypersphere (Meyer, B., 1998). This ensures that

connected nodes are placed in proximity to each

other. Because these nodes need to be mapped onto

partially complex-shaped cell components, they are

placed first on a surrounding sphere. Then they are

moved to the location lying on the shape between

the original computed ISOM point and the center of

the cell component. The compound nodes are placed

dependent on the position(s) of the connected

enzyme(s). For the placement of cell components as

well as the nodes we utilized a seed-based random

number generator, ensuring a consistent spatial

placement.

3.4 Spatial Data Browsing

3.4.1 3D View and Navigation

An easy-to-use and powerful six degrees of freedom

(6DoF) navigation offers three different modes:

The Floating Mode provides keyboard-based

movement in the 3D view and a free mouse cursor

for cell component and node interaction, e.g.

selecting and jumping to an object. Enabling the

Flight Mode locks the mouse cursor in a centered

position. Vessel-like behaviour allows an intuitive

Figure 3: The 2D Visualization of the pathway based on

the KEGG layout. The Pop-up menu shows options for the

clicked node. The active node is EC 1.2.4.2.

navigation through the cell. Mouse buttons

accelerate and mouse movement rotates the view. To

provide exploration of single cell components and

nodes, the Object-Bound Mode allows binding the

navigation to such an element. The view orbits

around the bound object. The mouse cursor is free

for interaction and provides movement via click and

drag. Another implemented features is a tour editor.

To take full advantage of the 3D perspective, 3D

Stereoscopy is implemented, compatible to e.g.

nVidia® Quadro® FX cards.

3.4.2 2D View and GUI Correlation

Additionally, a 2D view has been created by using

the JUNG library (O'Madadhain, 2009). In order to

give biologists a well-known alternative interface,

the KEGG map layout can be used to navigate

through 3D space (Figure 3).

Following the Focus+Context paradigm

(Robinson & Flores, 1997), Pop-up menus are

integrated in the 2D and 3D view as well as in the

localization table. The program provides a well-

defined correlation between GUI and the 3D

environment. Clicking on a 3D node triggers a panel

showing related information, e.g. alternative names,

references for the localization, links to open a

browser with corresponding websites and to

neighbor nodes. These links can also be used to

navigate in the 3D view. The localization table also

marks the active enzyme and pathway.

CELLMICROCOSMOS 4.1 - An Interactive Approach to Integrating Spatially Localized Metabolic Networks into a

Virtual 3D Cell Environment

3.5 Comparing Methods

To compare the localization of enzymes involved in

different pathways, it is possible to load different

pathways into one cell environment. The edges of

those pathways are drawn in different colors (Figure

4). Type-identical Enzymes at the same cell

component have the same position (Section 2.3.3). If

the user does not want different pathways to be

placed on the same cell component, he may build a

cell environment containing multiple copies of a

specific cell component which are lying parallel to

each other (Figure 5).

Figure 4: EC 1.2.4.1 (Pyruvate Dehydrogenase) involved

in the Citrate Cycle (green) and the Pyruvate Metabolism

(red) correlated with a more abstract mitochondria model.

3.6 Data Exchange

Import and export capabilities are provided for cell

compositions by packaging a native XML format

with linked VRML cell component models (suffix:

.Cm3) as well as for metabolic pathways including

all localization settings (suffix: .Cm4). The whole

model can be exported in VRML97 in order to be

used by other 3D visualization programs.

3.7 Implementation Details

The webstart compatible project is implemented in

Java 6/Java3D. It needs only Java preinstalled and

an internet connection for the initial downloading

and optional updating process. The CmPI module is

integrated into the Cm 1 CellExplorer application,

also includes the Cm 3 CellEditor module providing

capabilities to create abstract cell environments by

using different cell component VRML models.

The Webstart application is located at:

http://Cm4.CELLmicrocosmos.org

4 RESULTS

We combined two substantial areas of

Bioinformatics: Cell and Network Visualization. For

this purpose we developed a framework which

enables researchers to localize, visualize and explore

organism- and organelle-specific enzymes in 3D space.

The data integration of KEGG ensures a broad

application area. The localization of enzymes using

BRENDA and UniProt achieved good results for the

organelle specific mapping. Because the database

entries are often imprecise, the automatic mapping at

the organelle internal layer is still problematic. To

dissolve the spatial complexity of networks in 3D,

the ISOM layout algorithm and a 6DoF navigation

has been implemented. Additionally, we showed the

potential of linking abstract/functional and

realistic/structural data by correlating pathways with

3D models derived from 3D microscopy.

Figure 5: Left mitochondrion: Citrate Cycle, Center:

Pyruvate Metabolism, Right: Glycolysis; Some nodes are

localized at the cell membrane (red), the cytosol

(transparent) and those not localized with the white

sphere.

5 OUTLOOK

To improve the qualitative and quantitative amount

of localization data, alternative databases should be

integrated, providing data of different organisms and

reliability or even different types of networks, for

example signalling and/or protein-protein interaction

data. The network visualization will be improved by

adding applied layout algorithms.

Another exciting challenge to take is the

simplification of the import process of 3D cell

component models or whole cells extracted from 3D

Microscopy (Section 2.2).

BIOINFORMATICS 2010 - International Conference on Bioinformatics

The export and import capabilities will be

expanded to support SBML in the future. The

advantage of the implemented 2D View (Section

2.4.2) is that networks and layouts created by other

applications may be imported into CmPI and

localized and explored in 3D space.

The cell environment will be extended in

different directions: The CellEditor will provide the

possibility of building cell models of different

species to be used in other applications as well,

while the CellExplorer will be advanced to an

interactive learning environment which will offer a

way to give students a spatial idea of the cell.

Another advantage of 3D visualizations is the

ability to show the realistic 3D structure of

components. Therefore, the integration of the

molecular level is intended, correlated with

organelles as well as networks. Combined with

capabilities to simulate these networks, this

framework would be another interesting contribution

to Synthetic Biology.

ACKNOWLEDGEMENTS

This work has been funded by the DFG (German

Research Foundation): The Graduate College

Bioinformatics (GK635). Our thanks go to all people

supporting or participating in this project and to the

Bio-/Medical Informatics Group of Bielefeld

University, where this work has been realized:

http://team.CELLmicrocosmos.org

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CELLMICROCOSMOS 4.1 - An Interactive Approach to Integrating Spatially Localized Metabolic Networks into a

Virtual 3D Cell Environment