Medical Systems Biology

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Changing internal and external conditions can influence the long-term behavior of the Boolean network model. The perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another component can lead to different attractors. Obviously, the number of possible perturbations and combinations of perturbations increases with the size of the network. Manual screening a set of possible components for combinations, that have a desired effect on the long-term behavior, can be time consuming. We developed a method to automatically screen for perturbations that lead to a user-specified change in the networks behavior.
Simulation of the network under perturbation conditions allows to get a deeper understanding of the dynamics in the Boolean network model. 

 

Downloads : 

  runnable jar-file

  source code

 

Getting Started : 

The downloaded jar file is runnable and can be started by double-clicking. To run the application a java version (JRE) 8.71 or later is required (https://java.com/download/).

After launching the application an example can be loaded by clicking "load example network" in the middle of the window. 
A network model of the mammalian cellcycle and an exemplary simulation setup are loaded.
 
You can also download the example file cellcycle.visibool (Right click and save link as...). In some browsers the file extension might be changed. The file then has to be renamed to cellcycle.visibool again. 

 

For automatic screening for perturbations switch to the exhaustive simulation panel via the simulation menubar. On the right side the attractors of the network are displayed. The button "Perturbation Screening" starts the automatic screening routine. This routine is sperated into three major steps :

  1. Select the desired changes in the long-term behavior of the system. Attractors can be either selected to delete, to be kept or unselected if this attractors is not relevant.
  2. After pressing "Next", choose the maximum number of components to be in the set of perturbations. Below the components that are possible targets for the perturbation can be selected.
    - Grey means normal behavior
    - Red means permanent knock-out
    - Green means permanent over-expression
    - Blue mean knock-out and over-expression is tested in the set (default for perturbation)
  3. After pressing "Next", the components of interest can be selected. The selected components are used for comparison of the long-term behavior.
    Pressing "Simulate" starts the screening process.

Finally, the perturbation sets which show the previously selected effects on the long-term behavior of the network are shown.
By double-clicking a perturbation set of interest the resulting attractors are displayed.
The "Save"-Button stores a log-file with the settings of the perturbation screening and its results.

 

Job Openings

Wissenschaftlicher Mitarbeiter (m/w/d) 

 

Latest News

 

Our paper "GatekeepR: an R shiny application for the identification of nodes with high dynamic impact in boolean networks" has been published online first in Bioinformatics.

 

Our paper "The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices" has been published in JMIR Medical Informatics.

 

"Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients" has been published in npj Precision Oncology.

 

Our paper "AMBAR-interactive alteration annotations for molecular tumor boards" has been published in Computer Methods and Programs in Biomedicine.

 

"A protocol for the use of cloud-based quantum computers for logical network analysis of biological systems" has been published in STAR Protocols.

 

Our paper "A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective" has been published in npj systems biology and applications.

 

"Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician" has been published in PLoS One.

 

"Self-Assessment of Having COVID-19 With the Corona Check Mhealth App" has been published in IEEE Journal of Biomedical and Health Informatics.


Our first quantum computing paper "Leveraging quantum computing for dynamic analyses of logical networks in systems biology" has been published in Patterns.