Bridging the gap between molecular –omics studies and multiscale modeling

Bioengineering is focused on advancing human health and promoting environmental sustainability, two of the greatest challenges for our world. Modeling complex living systems is a mandatory step towards these objectives and incorporation of models for intracellular genetic and biochemical regulatory networks as a component of cells models is an important step in bridging the molecular details of biology with organs and higher order models. In the last two decades high-throughput –omics techniques such as sequencing, microarray and mass spectrometry have experienced a huge diffusion; however, as biomedical research evolves towards -omics sciences, specific needs have emerged, including modeling the molecular basis of diseases, integrating -omics data with patient clinical data, and, ultimately, translating -omics data analysis results in medical knowledge. In this talk, I will give examples of –omics data modeling within an integrative multiscale approach, from Genome Wide Association Studies (GWAS) to microarrays, from protein signaling to transcription activation studies, and of how this helps understanding the biological machinery underlying diseases and gaining sufficient reliability for clinical/pharmaceutical applications. GWAS search for patterns of genetic variation, in the form of Single Nucleotide Polymorphisms (SNPs), between a population of affected individuals (cases) and a healthy (control) population. Although these studies have successfully identified a number of significant SNP-disease associations, they were able to explain only a small fraction of disease hereditability. One of the reasons is that complex pathologies, such as cancer, diabetes or neurological disorders, are indeed heterogeneous and multi-causal, as a result of the alteration of multiple regulatory pathways and of the interplay between different genes and the environment, rather than referable to a single dysfunctional gene like in monogenic diseases. In this context, identification of functional pathways underlying the observed phenotype is a major challenge in Systems Biology. We have developed a method to identify pathways associated to the disease in GWAS, based on the Entropy concept. In particular, Joint Entropy between pairs of SNPs belonging to the same functional pathway is calculated and the difference in cases vs. control is averaged across the pairs of genes in the pathway. This allows ranking pathways based on how widely the mapped SNPs are associated to the disease. Our method was applied to the WTCCC (Wellcome Trust Case Control Consortium. Nature, 2007) type 1 and 2 diabetes GWAS dataset and selected a number of SNPs associated to diabetes, previously identified either by WTCCC study or in different T1D and T2D GWAS (www.genome.gov/GWAStudies), showing the ability of generalizing the results and identifying rare variants. Once identified, genetic biomarkers can be integrated at different levels with phenotypic biomarkers, transcriptomic and protein signaling data to understand better the mechanism beyond the pathogenesis of a disease. For example, the consistency between SNPs associated to diseases in specific pathways and gene expression can be evaluated by analyzing whether the SNP markers influence expression of specific transcripts, and if so, whether those downstream targets can be further developed as biomarkers related to specific phenotypes. Finally, at the basis of transcription and translation at cell level, and of development, functioning and homeostasis at tissue level, there is the cell signaling in response to microenvironment. A quantitative model of insulin signaling in muscle cells treated with insulin has been developed to analyze the changes in the activity of multiple proteins in normal and insulin resistant muscle cells. In particular, using a mass action model of early signaling, we have analyzed the differences in the kinetics of the two main signaling cascade pathways: PI3K/Akt and GRB2 / MEK1/2 / ERK1/2. The modeling approach has yielded important insights into reciprocal relationships between insulin resistance and changes in PI3K/Akt and GRB2 / MEK1/2 / ERK1/2 pathways that might be relevant for generating novel therapeutic approaches. A key challenge that biology is facing is the integration of data within and across domains and levels of granularity in a multiscale approach. Methods, validation procedures, simulation models as well as standards for the exchange of data and results are needed in the near future. Bioengineering has a specific role and a privileged position in multiscale modeling approaches, for its intrinsic multidisciplinarity and the availability of researchers that weaves together biology, physics, chemistry, computer science and medicine, ranging from the molecular scale to entire organisms