Advances in high-throughput technologies in genomics-related fields have changed the approach to the understanding of biological phenomena.The vast amounts of data made available by the -genomics revolution is generating torrents of data, often referred to as big data. Dealing with big data in turn calls for generation of techniques, technologies, models, storage infrastructures and collaborative platforms to capture the often unexpected features of complex biological systems.
Analyses of large scale -genomics data sets requires the integration of these data under mathematical and relational models that can describe mechanistically the relationships between their components.
Bringing Maths to Life (BMTL) 2017 will bring together leading scientists in mathematics, biology and computational biology working in different fields to create a scientific platform for data sharing, to learn about big data and -genomics novel developments, and to confront ideas and realities through case studies and practical experiences.
The workshop, as a mélange of different fields, will consist of selected contributions for open discussion to present and confront the most updated results at the interface of mathematics and biology in three main sessions:
2) Information: data analysis, novel -genomics technologies, the application of deep learning to different -genomics data sets. The integration of experimental data prior to their analysis requires full understanding of the algorithms that need to be used in order to ensure a correct application in answering biological questions. Case studies of this session are two examples of meta genomics analyses. The first is the analysis of data collected during the Tara Oceans expedition including samples from 210 stations across the world oceans (see: http://taraexpeditions-it.blogspot.it/)). The project is deciphering how the most complex organisms evolved from primordial bacteria and in future it will tell us about the fate of the myriad organisms present today. The second concern the analysis of human gut microbiome.
3) Knowledge: integration of different -genomics data, system biology of -genomics data. Methods for the integrative analysis of multi-genomics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. Unveiling the interactions between diverse types of data allows to fully exploit their information. The keynote lectures of this session will show the integration of genomic, biochemical, and metabolic data under comprehensive mathematical models.
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