e:Bio Cellemental

Cellular Functions from Elemental Reactions and States




Funding period:        May 2012 - April 2017
Funded by:       
Id code:        0316193
Spokesperson:        Marcus Krantz   , Humboldt-Universitšt zu Berlin (Germany)
Project home:       


Mapping of complex cellular networks

The purpose of this project is to provide a framework to collect, visualise and model experimental data on cellular networks, to implement that framework in a software tool, and to use the framework, and tool to create maps and models of highly relevant cellular networks to promote a system levels understanding of these networks.

The primary challenge is the combinatorial complexity. Signal transduction network components are often influenced by multiple interaction partners and/or modifications such as phosphorylations. These rapidly combine to a large number of possible configurations or specific states of each component. For example, a component with two such configuration options can exist in four (22) specific states, while a component with ten such options can exist in 1024 specific states (210). As the network expands, both the number of components and the number of configuration options per component increase. This combinatorial explosion leads to both computational and biological problems: The networks get tremendously large and we also introduce a large uncertainty in the relationship between the theoretical specific states and the more coarse grained empirical observations they are based on.

In this project, we introduce a fundamentally different state description. Each configuration option or elemental state is described separately. This makes the description scale linearly rather than exponentially. E.g., a component with two configuration options are described by two elemental states, and a system with ten such options are described by ten elemental states. This greatly reduces the combinatorial complexity in the description. Not only does this dramatically increase the size and complexity of the networks we can describe; the description also keeps a one-to-one correspondence to experimental data.

The goal of this project is to provide an easy to use and freely available software tool. The software allows the user to compile a list style definition of the network based on an Excel template which in turn can be used to automatically generate visualisations as well as a mathematical model. The latest version of the tool is available at http://www.rxncon.org/. To date, we have implemented the basic functions in signal transduction pathways (synthesis/degradation, complex formation and covalent modification) while the implementation of spatial (re)localisation and metabolic pathways is in progress.

The added advantage of this method as compared to previous strategies is that it allows (i) more concise mapping adapted to empirical data (ii) individual referencing for each piece of information (iii) visualisation without simplifications or added uncertainty (iv) automatic visualisation in multiple graphs (v) automatic export to mathematical models, and that it provides (vi) the tools required for any user to map and model their favourite pathway from an ordinary Excel sheet.


Ulrike Muenzner - (Subproject)

Magdalena Rother - (Subproject)

Sebastian Thieme - (Subproject)



Related Publication:
Tiger, C.-F., Krause, F., Cedersund, G., Palmér, R., Klipp. E., Hohmann, S., Kitano, H. & Krantz, M.
(2012)
A framework for mapping, visualisation and automatic model creation of signal transduction networks.
Molecular Systems Biology
8
, 578.
Text/Abstract
doi: 10.1038/msb.2012.12