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.

Nucleic acid‐templated chemistry opens the intriguing prospect of triggering the synthesis of drugs only in diseased cells. Herein, we explore the feasibility of using RNA‐templated chemical reactions for the activation of a known Smac peptidomimetic compound (SMC), which has proapoptotic activity. Two peptide nucleic acid (PNA) conjugates were used to enable conditional activation of a masked SMC by reduction of an azide either by Staudinger reduction or catalytic photoreduction using a ruthenium complex. The latter provided ~135 nM SMC‐PNA on as little as 10 nM (0.01 eq.) template. For the evaluation of the templated azido‐SMC reduction system in cellulo, a stable HEK 293 cell line was generated, which overexpressed a truncated, non‐functional form of the XIAP mRNA target. We furthermore describe the development of electroporation protocols that enable a robust delivery of PNA conjugates into HEK 293 cells. The action of the reactive PNA conjugates was evaluated by viability and flow cytometric apoptosis assays. In addition, electroporated probes were re‐isolated and analyzed by ultra‐high performance liquid chromatography (UPLC). Unfortunately, the ruthenium‐PNA conjugate proved phototoxic, and treatment of cells with PNA‐linked reducing agent and the azido‐masked SMC conjugate did not result in a greater viability loss than treatment with scrambled sequence controls. Intracellular product formation was not detectable. A control experiment in total cellular RNA isolate indicated that the templated reaction can in principle proceed in a complex system. The results of this first‐of‐its‐kind study reveal the numerous hurdles that must be overcome if RNA molecules are to trigger the synthesis of pro‐apoptotic drugs inside cells.