T8 – Fifth International Hands-on Tutorial on Logical Modelling: Exploring the dynamics of biological systems

 

TUTORIAL DETAILS

Date: September 9, 2018

Time: 9:00 – 17:00 (full day tutorial)

Venue: TBA

 

TUTORS

  • Aurelien Naldi, Institut de Biologie de l’École Normale Supérieure, FR
  • Juilee Thakar, University of Rochester, USA
  • Julio Saez-Rodriguez, RWTH Aachen University, DE
  • Tomas Helikar, University of Nebraska, USA​​​​​​

 

SUMMARY

Logical modeling provides a computational approach to the visualization and analysis of the dynamics of biochemical and biological systems complementary to others such as reaction-based or rule-based modeling. One of the main advantages of logical models is their scalability and the relatively easy method of construction. In part due to these attributes, logical models have become increasingly more popular among the computational biology community. This has, in turn, led to the development of different techniques and software tools that enable the construction, simulation, and analysis of logical models and their variants (Boolean, multilevel, deterministic, stochastic, etc.) to address various biological questions.​​​​​​​​​​

The tutorial will begin with an overview of logical modeling, followed by hands-on tutorial sessions on four complementary tools widely used by researchers around the world. Through the tutorials and each tool, you will learn how to construct (manually as well as infer from high-throughput data), simulate, and analyze the dynamics of logical computational models. The objective of the tutorial is that you will be able to begin using logical modeling, and the software tools for your own research. The core logical modeling software tools to be covered in the tutorial include Cell Collective (www.cellcollective.org), CellNOpt (www.cellnopt.org), GINsim (www.ginsim.org), and TBD.

 

TARGET AUDIENCE

This tutorial is open to participants (students and researchers) with no modeling experience as well as to those who are seasoned modelers.

 

REQUIREMENTS

Participants should bring their own laptops.

 

SCHEDULE

Morning Session
9:00-9:15 Welcome and Introduction to logical modeling frameworks
9:15-10:30 CellNOpt: Julio Saez-Rodriguez
10:30-11:00 Coffee break
11:00-12:30 Cell Collective: Tomas Helikar

12:30-13:30 Lunch break

Afternoon Session
13:30-15:00 GINsim: Aurelien Naldi
15:00-15:30 Coffee break
15:30-17:00 TBD Software Tool #4

 

ADDITIONAL INFORMATION ABOUT PRESENTED SOFTWARE TOOLS

Cell Collective (www.cellcollective.org)

Cell Collective is an on-line platform for construction, simulations, and analyses of large-scale computational models in a collaborative fashion. Its user interface enables scientists to build and simulate models without manually creating complex mathematical equations (logical functions) or computer code, enabling those with diverse backgrounds to contribute to the construction of these models. Cell Collective allows users to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test various what-if scenarios.

Cell Collective currently contains hundreds of user-created models, and 71 published peer-reviewed models. These seed models represent biological and biochemical networks in organisms ranging from bacteria and viruses to yeast, flies, plans, and humans. Models in the Cell Collective are fully annotated within its wiki-like system, enabling researchers to track and discuss the biological evidence and assumptions used to construct each model. Finally, Cell Collective models are accessible and share-able not only within the platform, but they are also available for download in a number of open formats, including SBML.

During this tutorial, attendees will learn about some of the major features of Cell Collective, including how to construct a model, run real-time simulations, generate titration curves, conduct perturbation analyses, etc.

CellNOpt (www.cellnopt.org)

CellNOpt  (Terfve et al. 2012) is a software used for creating logic-based models of signal transduction networks using different logic formalisms, including Boolean, Fuzzy, or differential equations (MacNamara et al. 2012). CellNOpt uses information on signaling pathways encoded as a Prior Knowledge Network, and trains it against high-throughput biochemical data to create cell-specific models. CellNOpt is freely available under GPL license in R and Matlab languages. It can be also accessed through a python wrapper, and a Cytoscape plugin called CytoCopter provides a graphical user interface. CellNOpt is compatible with the SBML-qual format (Chaouiya et al. 2013), facilitating exchange with other tools such as Cell Collective.

CellNOpt uses pathway information, normally from literature, that can be retrieved systematically with the tool OmniPath (Türei et al. 2016). It can also find missing links in literature curated pathways (Eduati et al. 2012). Training is performed with the complementary tool MEIGO (Egea et al. 2014).

CellNOpt typically uses phosphoproteomic data obtained from antibody-based technologies, but it has been extended to process mass spectrometry data with the related tool PHONEMeS (Terfve et al. 2015).

During the tutorial, we will discuss the process of building logic models with CellNOPt:

  • Obtain the relevant pathway information,
  • Choose the right mathematical logic formalism
  • Fit models to data
  • Interpret model results

The use of CellNOpt will be illustrated with a recent example where it was used to  find biomarkers and novel therapies in colorectal cancer (Eduati et al. 2017).

GINsim (www.ginsim.org)
GINsim is a free (Java) software application devoted to the logical (multi-valued) modelling of regulatory and signalling networks. It provides a user-friendly graphical interface to define models from scratch. Models can also be imported from different formats. GINsim supports the simulation of logical models and generates the resulting state transition graphs, considering a range of update policies (synchronous, asynchronous, priority updating).

GINsim also offers a number of functionalities to explore the dynamical properties of logical models, some of which (e.g., determination of stable states) can be efficiently analysed without generating the complete network dynamics. GINsim also supports the definition of multi-valued logical models. Besides the explicit construction of State Transition Graphs (for reasonable sizes, i.e., in the order of a few million states), GINsim provides a number of methods to analyse model properties and supports model exports into various formats, in particular for model checking.

During this tutorial, attendees will learn how to use GINsim to build a new model from scratch and to analyse it: computation of stable states, construction of the state transition graph, model reduction, compression of the dynamics, regulatory circuit analysis, etc.

References

Abou-Jaoudé W. et al. (2016). Logical modeling and dynamical analysis of cellular networks.  Frontiers in Genetics 7: 94.

Chaouiya, C. et al. (2013). SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC Systems Biology 7: 135.

Chaouiya C.. Naldi, A. & Thieffry, D. (2012) Logical Modelling of Gene Regulatory Networks with GINsim. Methods in Molecular Biology 804: 463-479.

Eduati, F. et al. (2017). Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models. Cancer Research 77: 3364-3375.

Eduati, F. et al. (2012). Integrating literature-constrained and data-driven inference of signalling networks. Bioinformatics  28: 2311–2317.

Egea, J.A. et al. (2014). MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinformatics 15: 136.

MacNamara, A. et al. (2012). State-time spectrum of signal transduction logic models. Physical Biology 9: 045003.

Terfve, C. et al. (2012). CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Systems Biology 6: 133.

Terfve, C.D.A. et al. (2015). Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data. Nature Communications, 6: 8033.

Türei, D., Korcsmaros, T. & Saez-Rodriguez, J. (2016). OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nature Methods 13: 966–967.