Relational programming with CrocoPat
Proceedings of the 28th international conference on Software engineering
Proving stabilization of biological systems
VMCAI'11 Proceedings of the 12th international conference on Verification, model checking, and abstract interpretation
BMA: visual tool for modeling and analyzing biological networks
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
Model-Checking signal transduction networks through decreasing reachability sets
CAV'13 Proceedings of the 25th international conference on Computer Aided Verification
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In many complex biological processes quantitative data is scarce, which makes it problematic to create accurate quantitative models of the system under study. In this work, we suggest that the Qualitative Networks (QNs) framework is an appropriate approach for modeling biological networks when only little quantitative data is available. Using QNs we model a metabolic network related to fat metabolism, which plays an important role in type-2 diabetes and obesity. The model is based on gene expression data of the regulatory network of a key transcription factor Mlxipl. Our model reproduces the experimental data and allows in-silico testing of new hypotheses. Specifically, the QN framework allows to predict new modes of interactions between components within the network. Furthermore, we demonstrate the value of the QNs approach in directing future experiments and its potential to facilitate our understanding of the modeled system.