Modelling, analysis and verification of the biological coherent feedforward loop network

Abstract

The world of molecular biology is composed by a complex network of interactions that are analogous to electric circuits. They govern the functions required for life, from metabolism to locomotion. In these networks, the presence of network motifs were identified, recurring elements supposedly kept by evolution. One of them is called the feedforward loop and has the function of a sign-sensitive delay element or noise-filter. Moreover, different combinations of several types of feedforward loops were identified in the transcription networks of Escherichia coli and Saccharomyces cerevisiae, called complex feedforward loops. From this finding a question arises: do different types of combined feedforward loops have a specific function? Would this identified function be useful in synthetic biology applications? Answering these questions is the ultimate goal of a research direction in systems biology, studied at the Institute of Complex Molecular Systems at Eindhoven University of Technology. However, biological experiments are difficult to setup and conduct in a suitable manner to generate relevant results. Therefore, it would be highly effective to be able to predict the nonlinear dynamical behaviour of these (combined) feedforward loops. Nevertheless, in order to be able to achieve this, first a single feedforward loop must be fully modelled, calibrated and analysed. This master thesis focuses on this goal and is composed of three main elements: modelling, parameter estimation and structural analysis. The modelling section comprises of the methodology derived in order to transpose the biochemical reactions into equations and perform model reduction on the feedforward loop built at the ICMS. Then, a hybrid parameter estimation method was applied successfully and made it possible to perform numerical simulations of the system. Lastly, the focus was directed to structural analysis and obtaining insights about the behaviour of the network without knowledge of the parameters. This included the adaptation of metabolic network analysis tools, elementary flux mode analysis and flux balance analysis to be used on gene expression networks. As a result, it was possible to link the nonlinearity of the steady-states observed in the experimental data with the accumulation of certain compounds.