Evaluating Uncertainty in Signaling Networks Using Logical Modeling.

ABSTRACT: 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 ofte

SUBMITTER: Thobe K 

PROVIDER: S-EPMC6191669 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature