Model Integration and Crosstalk Analysis of Logical Regulatory Networks

Thobe, Kirsten; Streck, Adam; Klarner, Hannes; Siebert, Heike

doi:10.1007/978-3-319-12982-2_3

Cited by 5 publications

(7 citation statements)

References 16 publications

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“…However, this pathway itself is intertwined with many other pathways, like Insulin signalling, but also with e.g. Wnt/β-catenin signalling and Hypoxia [10,17], which demands a systematic approach to integrate biological knowledge and models [27].…”

Section: Discussionmentioning

confidence: 99%

Data-driven optimizations for model checking of multi-valued regulatory networks

2016

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Section: Discussionmentioning

confidence: 99%

Data-driven optimizations for model checking of multi-valued regulatory networks

2016

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“…The formalization of logical modeling for biological systems was introduced by Kauffman ( 1969 ) and further refined by Thomas ( 1991 ), which is the base for our work. However, we expanded this formalism to incorporate uncertain information leading to model pools (Klarner, 2014 ; Thobe et al, 2014 , 2017 ; Streck, 2015 ; Streck et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…In a simple setup, this could mean adding optional edges as hypotheses, but there are also more complex aims that require changes in the PKN. The first objective we identified, was to examine crosstalk between two pathways while preserving the dynamical properties of each pathway, presented in Thobe et al ( 2014 ). Here, we want to present two different objectives: Finding driver mutations and drug testing.…”

Section: Methodsmentioning

confidence: 99%

“…As a result, we receive one or more specific model pools that need to be analyzed. For this aim, different kinds of analysis tools can be employed depending on the aim and the size of the resulting model pool such as statistical analysis (Thobe et al, 2014 ; Streck et al, 2016 ) or optimization (Terfve et al, 2012 ; Videla et al, 2017 ). Here, we want to present an analysis approach that allows a closer look at classes of models as well as single models.…”

Section: Methodsmentioning

confidence: 99%

“…In previous work, we presented parts of this pipeline, i.e. the objective of investigating crosstalk between two signaling pathways in Thobe et al ( 2014 ), as well as challenges for data discretization and analysis in Streck et al ( 2015 ) in context of a specific software. Here, we generalize and expand this pipeline by two additional objectives and analysis methods.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Uncertainty in Signaling Networks Using Logical Modeling

et al. 2018

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Systems biology studies the structure and dynamics of biological systems using mathematical approaches. Bottom-up approaches create models from prior knowledge but usually cannot cope with uncertainty, whereas top-down approaches infer models directly from data using statistical methods but mostly neglect valuable known information from former studies. Here, we want to present a workflow that includes prior knowledge while allowing for uncertainty in the modeling process. We build not one but all possible models that arise from the uncertainty using logical modeling and subsequently filter for those models in agreement with data in a top-down manner. This approach enables us to investigate new and more complex biological research questions, however, the encoding in such a framework is often not obvious and thus not easily accessible for researcher from life sciences. To mitigate this problem, we formulate a pipeline with specific templates to address some research questions common in signaling network analysis. To illustrate the potential of this approach, we applied the pipeline to growth factor signaling processes in two renal cancer cell lines. These two cell lines originate from similar tissue, but surprisingly showed a very different behavior toward the cancer drug Sorafenib. Thus our aim was to explore differences between these cell lines regarding three sources of uncertainty in one analysis: possible targets of Sorafenib, crosstalk between involved pathways, and the effect of a mutation in mammalian target of Rapamycin (mTOR) in one of the cell lines. We were able to show that the model pools from the cell lines are disjoint, thus the discrepancies in behavior originate from differences in the cellular wiring. Also the mutation in mTOR is not affecting its activity in the pathway. The results on Sorafenib, while not fully clarifying the mechanisms involved, illustrate the potential of this analysis for generating new hypotheses.

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