A great challenge for microbiologists is to exploit this large and expanding amount of sequence data to provide biological information and ecological context for these new genomes [3] [5].
Understanding how PIGC is involved in vascular diameter and/or remodelling will require further experimental analysis, and presently there are few data to provide biological insights into the observed association.
Finally, we describe an investigation of the use of RNAseq data to provide a biological basis for reducing complexity of the GENCODE transcript set.
Such research projects may also benefit from expression data to provide support to biological conclusions.
One promising solution to both issues is to combine the worlds of the eagle and the frog, by sequentially using different types of approaches; for instance an initial unbiased statistical method with high sensitivity and low specificity is overlapped with biological data to filter information and to provide specificity.
However, faced with such large-scale, heterogeneous, and linked biological data, how to provide an efficient approach to model, integrate, and analyze the big biological network becomes a challenge.
MIRIAM is a project of the international initiative BioModels.net [ 5], which aims are multiple: define agreed-upon standards for model curation, define agreed-upon vocabularies for annotating models with connections to biological data resources and provide a free access to published, peer-reviewed, annotated, computational models.
Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference.
Complex cellular processes can be described and studied with computational or mathematical models., When these models are driven by biological data, they provide reliable platforms for interrogating cell systems in silico.
Our methods applied to real biological data provide new insight.
