Given the vast amounts of complicated data generated by these techniques, their meaningful interpretation and even their storage present significant challenges that call for new approaches. Beyond current bioinformatics approaches, we need to develop new methods to discover meaningful patterns in these large data sets. Moreover, we need mathematical modeling, and one option here is to apply inverse modeling techniques to derive gene regulatory networks to better understand how gene expression changes over time and space and how cells and deposits of biominerals (e.g., bone formation in vertebrates, silicification in diatoms and sponges, and calcification in corals) grow and form structures in space and time.

Model-based reconstruction of genetic networks can be used to systematically organize gene expression data and guide future data collection. A key challenge here is to understand how gene regulation controls fundamental biological processes such as biomineralization and embryogenesis. The subprocesses such as gene regulation, organic molecules interacting with the mineral deposition process, cellular processes, physiology and other processes at the tissue and environmental levels are interconnected. Rather than being driven by a central control mechanism, biomineralization and embryogenesis can be viewed as emergent behavior arising from a complex system in which different subprocesses at very different temporal and spatial scales (ranging from nanometers and nanoseconds to meters and years) are connected in a multiscale system. One of the few available options for understanding such systems is by developing a multi-scale model of the system.

Source: uva.computationalscience.com