The intestinal homeostasis of dietary lipids plays a pivotal role in controlling dietary lipid uptake from ingested foods. The transcription factor PPARα, a member of the nuclear receptor superfamily, is a master regulator in intestinal fatty acid sensing, lipid absorption, and metabolism.
Extensive research in our laboratories over the last ten years has provided a wealth of data on the components (genes, proteins and processes) involved and/or modulated by PPARα in the intestine, and their effects on metabolic homeostasis. However, the sheer number and complexity of the many interactions among these components has prevented gaining comprehensive insights into the mechanisms and true nature of the underlying network, being most of the work thus far essential descriptive and static. Hence, here we aim to develop an experimentally validated, basic modular multi-scale computational framework for the PPARα controlled part in intestinal lipid absorption and homeostasis in mice. The four levels that will be used for modelling approaches are the i)
mitochondrion, ii) enterocytes, iii) intestinal segments and ultimately iv) the whole small intestine. Both existing and to-be generated high-throughput (transcriptomics and shot-gun proteomics) data will undergo, iteratively, thorough reverse engineering analyses for the inference of the regulatory circuitry underlying the intestinal PPARα network. These analyses will generate hypotheses and pin-point key nodes and pathways that play a pivotal role in modulating the network. For a selected number of those networks Boolean and/or hybrid dynamic-Boolean models will be developed and experimentally tested to assist in dissecting the underlying mechanisms. The PhD project herein described is essentially
computational, being the extensive experimental testing and validation effort undertaken in on-going research in the scope of a number of matching projects.