SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology

Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment. The R-package SEEDS implements two recently developed algorithms to infer structural model errors and unknown inputs from output measurements. This information can facilitate efficient model recalibration as well as experimental design in the case of misfits between the initial model and data.

Automated summary

Dynamic models formulated as ordinary differential equations can provide insights into mechanistic and causal interactions in biological systems, but inaccurate knowledge about interaction structure and parameters poses a major obstacle. The open nature of biological systems is also a challenge. SEEDS algorithms can help analyze structural model errors and unknown inputs, facilitating model recalibration and experimental design.