Besides such complex plant-environment interactions, latest developments in bioanalytical

research comprising shotgun and next-generation genome sequencing as well as molecular analysis using OMICS technologies have driven the need for computer-assisted analysis and modeling of biological data. Systems biology has evolved in a research field focusing on the system wide understanding of biological networks, like for example the cellular metabolism in a photosynthetically active plant cell. In a systems biology approach, network elements, such as genes, Inhibitors,research,lifescience,medical proteins or metabolites, are considered as interacting components rather than isolated entities in order to deepen the comprehensive understanding of the organization of a complex biological system. A promising way to analyze such complex biological and biochemical networks is formal representation by mathematical models enabling Inhibitors,research,lifescience,medical their computer based handling and making biological data accessible to theoretical methods originating from applied mathematics and systems theory. Numerous

mathematical approaches to model plant metabolic networks have been suggested and discussed, both relying on and emphasizing the importance of the iterative processes of model development, simulation and validation by experimental data [17,18,19,20,21,22]. An overview of several computational approaches to study Inhibitors,research,lifescience,medical metabolic networks with respect to features like topology, stability and dynamical behavior was provided previously [23], classifying Inhibitors,research,lifescience,medical the approaches by their mathematical structure. Thus, qualitative models for a static network description containing no Selleck ATM Kinase Inhibitor kinetic parameters (network/stoichiometric analysis) were distinguished from quantitative approaches applying

a dynamic system description using kinetic parameters (kinetic models) [23]. A main focus of mathematical modeling in biochemistry and plant science has been on the construction of kinetic models where metabolic states are simulated based on the knowledge about network topology, stoichiometry, rate equations and kinetic parameters. Typically, a system of ordinary differential equations (ODEs) is used to describe Inhibitors,research,lifescience,medical the time-dependent changes in state variables, i.e., metabolite concentration, protein abundance or the amount of transcripts. In the context of metabolic systems, the sum of synthesizing and degrading rates these of enzyme reactions defines the time-dependent change in metabolite concentrations. The representation of biological systems by sets of ODEs has successfully been applied to various processes in plant biology and also in the comprehensive analysis of plant-environment interactions, as was already outlined in [24]. While kinetic modeling represents an attractive method to study and beyond that, to potentially predict the behavior of complex metabolic networks, plenty of information about the network topology and the kinetics of metabolite interconverting steps is required for model development and its experimental validation [25].