Model for Virus Infection

As in the previous model for the virus production we established a model of ordinary differential equation (ODE) based on the law of mass action. The following paragraph explains the reaction scheme and our model assumptions. In the subsequent paragraphs the system of differential equations is specified and the implementation in MathWorks® MATLAB is discussed. Furthermore reasonable model extensions are presented.
The last section deals with our modeling results and gives an outlook to further modeling applications.

Reaction Scheme

To simplify the mathematical description of the reaction scheme we divide the cell into four compartments: the extracellular matrix (all quantities with the index ext), the space in endosomes (end), the cytoplasm (cyt) and the nucleus (nuc).
A target cell is transduced by viral particles (V) in the extracellular matrix. Depending on their degree of modification (m) and thus their specificity they can bind to receptors (R) on the cell surface. Once a receptor has formed a complex with a virus particle (VR) receptor dimerization (R₂) occurs and the whole complex (VR₂) is in and endosomes are formed. The virus is released from the endosome to the cytoplasm and is transported to the nucleus where uncoating of the capsid is initiated and the single stranded DNA (ssDNA) is released.
Finally viral mRNA is processed and transported into the cytoplasm and the enzyme for therapeutic approach (E) is produced.

Reduced Reaction Scheme

This modeling approach neglects the fastest linear transport processes and describes the receptor binding and dimerization in terms of the law of mass action (LMA).

Differential Equations

The ODE model consists of 7 equations containing 9 rate constants. To the equation for the extracellular virus concentration a degradation term with rate constant (k_7,1) is added corresponding to the immune response of the target system. The temporal behavior of ssDNA is supplemented by a linear degradation term with rate constant (k_6,1).

Model Extensions

Since internalization depends on the degree of modification (m) the model is extended by a non-linear dependency in the rate constants. Additionally a decreasing production efficiency reduces the amount of available ssDNA in the nucleus. The assumed functional dependencies are shown in the figure below.

For k(m) we assumed that a minimal modification degree of 15% is necessary for virus receptor complex formation due to the fact that at least two binding sites are required for receptor dimerization. For higher m-values the targeting efficiency saturates because enough binding sites exists that every dimerized receptor can build a complex with a virus. The values of r(m) are normalized to the ratio of infectious particles to empty virus capsids and therefore starts at one. Increasing m leads to a decreasing amount of infections particles until a minimal value is reached for the completely modified virus (m=100%). Future experiments could provide a better understanding of those functional dependencies and therefore improve the model predictions concerning the targeting efficiency dicussed later in this chapter.

Methods and Simulation

The ODE model was implemented in MathWorks® MATLAB R2010b. Integration of the differential equations was achieved using the stiff integration routine ode15s with automatic integration step size management. For initial conditions we took virus concentrations used in the experiments. Rate constants were estimated according to published values. Furthermore experiments have been conducted in order to determine typical time scales corresponding to biological

The used parameters are given in the table below.

Results and Discussion