Analysing Cell Line Specific EGFR Signalling via Optimized Automata Based Model Checking

Streck, Adam; Thobe, Kirsten; Siebert, Heike

doi:10.1007/978-3-319-23401-4_22

Cited by 7 publications

(10 citation statements)

References 9 publications

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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%

“…A different approach was developed by our group, where uncertainty in parameters of the model, such as an uncertain sign of an edge, is encoded into the model definition (Klarner et al, 2012 ) and all possible models that arise from this uncertainty are enumerated. Subsequently the models are tested for satisfiability for data without an optimality criterion (Figure 1A ), which was implemented using efficient formal verification techniques in Tremppi (Streck et al, 2015 ) and in TomClass (Klarner, 2014 ). Even though we employ the software from our group for the analysis in this paper, one could apply different software along the pipeline for building the model pool or analyzing it.…”

Section: Introductionmentioning

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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating Uncertainty in Signaling Networks Using Logical Modeling

et al. 2018

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“…This article is an extension of a conference contribution [25]. Here, we extend our method from the Boolean networks to multi-valued and add new properties.…”

Section: Introductionmentioning

confidence: 91%

“…In Section 4 we then evaluate the results of the study. Section 3 is the main contribution of this article, compared to [25], and provides a full formal description of the encoding procedures together with the related proofs. However, while the section provides additional insights into the problem, it is not necessary for understanding the framework.…”

Section: Introductionmentioning

confidence: 99%