The Center addressed the need for integration across scales and dimensions of systems. “Integration” requires that a multidisciplinary effort be marshaled for understanding complex hierarchical systems as a whole – beyond the characterization and cataloging of its parts.
‘Integration’ refers to either (1) the computational (methodological) integration of distinct datasets, or (2) the conceptual integration in the analysis of complex systems, which is paramount to understanding “why the whole is more than the sum of its parts” – the system level behaviors
Data collected from different approaches, notably, proteomics, transcriptomics, metabolomics, but also, digital micro-imaging, are captured by a variety of technologies and are presented in different structures and analyzed separately. Their functional interrelationship is still established semi-manually in most cases, which creates a bottleneck that prevents the strength of broad systems approaches to be fully realized — namely the generation of new hypotheses based on new patterns in the data, when confronted with different types of data.
A central idea of systems biology is the parallel assessment of activity at all gene loci and the consequences of their expression, as epitomized by the ‘–omics’ approaches. The specific goal is an exhaustive measurement of most (if not all) of the elements of a particular type across the genome: transcriptome, proteome (including posttranslational modifications), interactome and metabolome, etc. These measurements do not, in themselves, constitute integration.
To address the challenge of integration for the Center we chose projects that:
- Cut across signaling, transcription and metabolism to build multiscale predictive models.
- Extend digitization of biology from molecules to single cells.
- Integrate molecular networks in the context of the formation of multicellular structures.