Reconstructing biological networks, such as metabolic and signaling networks, is at the heart of systems biology. We present several equation discovery approaches to reconstructing network structure, which recover the full dynamic behavior of a network. These take as input measured time course data, as well as existing domain knowledge, such as partial knowledge of the network structure. We demonstrate the use of these approaches on tasks of rediscovering known networks and proposing models for unknown networks.
COBISS.SI-ID: 21901607
The minimal description length (MDL) principle allows us to find an optimal trade-off between the complexity of a model and its predictive error. We propose an MDL scheme for regression by polynomial equations, which includes coding schemes for polynomials and the errors they make on data. We compare this principled MDL scheme to an ad-hoc MDL scheme and show that it performs better, so that it is comparable in accuracy to other commonly used methods for regression, such as model trees, while producing much smaller models
COBISS.SI-ID: 21912103