I use tools from theoretical physics and applied mathematics to model these networks and analyze experimental data.
To model these networks, models with different updating schemes have been proposed, namely synchronous (Fauré et al., 2006; Naldi et al., 2007; Remy et al., 2006)., asynchronous (Devloo et al., 2003; Fauré et al., 2006; Garg et al., 2007 Thomas, 1991) and semi-asynchronous models (Fauré et al.,, 2006).
As the complexity of these networks grows, a need for tools to model these networks becomes more evident (Bernot et al., 2004; Chabrier et al., 2004; Devloo et al., 2003; Garg et al., 2008; Karlebach and Shamir, 2008; Klamt et al., 2006; Naldi et al., 2007; Remy et al., 2006).
We did not try to model these differential network kinematics but for technical reasons assumed incompressibility of the network.
As we wish to use LTL to analyze the dynamics of complex networks and we model these networks as HDS, we tailor our definition of LTL to be compatible with this setting: Definition 2.3: The syntax of LTL consists of.
The HINT polar component score is similar for both the CNS+electrostatic and CNS+HINT structures, which suggests that either protocol adequately models these networks.
Petri nets have been used for modeling all these network types.
Process algebra is a form of computation that can handle multiple simultaneous interdependent steps and this makes it perfect for modelling these tricky biochemical networks and the feedback loops that drive them.
Recent developments in object-oriented programming languages provide opportunities for effective computational modeling of these networks.
