Translating the logical network models into ordinary differential equation models, and intervention target validation for the ordinary differential equation models.

Validation of the intervention targets in Table 1 for the T -LGL leukemia differential equation network model and single interventions from control sets with more than one node in Table 1 for the T -LGL leukemia differential equation network model.

Validation of the stable motif control intervention targets in Table 2 for the helper T cell differential equation network model.

To address this, we translate the studied Boolean network models into ordinary differential equation (ODE) models using the method described by Wittmann et al.

By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms.

Ordinary differential equation (ODE) modeling utilizes the entire information of the time resolved data.

To analyze the impact of crosstalk and feedback regulation, dynamic modeling approaches using coupled ordinary differential equations (ODEs) are most suited and allow quantitative insights [24, 35—38].

Ordinary differential equation model selection

Ordinary differential equation modeling

The time needed to travel from each point x E Voronoi(v) to the verteX v is found as the solution to the partial differential equation : with boundary condition T (v) = O.

This equation is a well known partial differential equation known as the Eikonal equation.

[15, 16] have built a Partial Differential Equation framework to filter image noise, segment cell nuclei, locate the cell membrane and model the evolution of cell shapes to track the cells.

A 2D simplification of the 3D Partial Differential Equation framework introduced in [15] has been reported in [22].