During the fifty-four years since the elucidation of the structure of DNA, there has been a remarkable increase in the pace of biological discovery. With the huge increase in the amount and scope of biological information available—at levels of complexity ranging from molecules, through assemblies of molecules, to cells—new scientific partnerships are evolving that will shape the future of biological research. These partnerships are breaking down barriers between traditional disciplines and creating strong interdisciplinary links between molecular, cellular and developmental biology on one hand, and the physical sciences, mathematics, computer science, and engineering on the other.

As biology explores more complex phenomena and biological data becomes more quantifiable, the need for mathematical and computational approaches to organize this information and provide predictive models becomes more acute. BioMaPS Institute faculty are deeply engaged in this effort. BioMaPS is an acronym for Biology at the Interface with the Mathematical and Physical Sciences. There are three central scientific themes that bring together the BioMaPS research groups working on a varied set of biological problems at the interface between the life and natural sciences. The unifying concept is the study of new phenomena emerging out of the interactions among many components studied on different length and time scales.

First and foremost, there is a focus on large biomolecular complexes associated with gene regulation. Rutgers has a tradition of strength in the area of structural biology, as evidenced both by the large group of internationally prominent researchers in the area as well as by the fact that Rutgers is the home of the Protein Data Bank (PDB), the single most important repository for biological macromolecular structure data in the world. Research on the structural basis for transcription and transcriptional regulation, from the interactions of DNA with polymerase and transcription factors, up through the study of chromatin structure and implications for gene regulation has engaged several BioMaPS-affiliated research groups in collaborative research and was the basis of an Institute sponsored NIH P20 Center grant. The approaches taken range from molecular level analysis to more knowledge-based methods. In recent years, there has been a convergence between these two extremes. Hybrid methods that combine insights from biophysical understanding are being used to build more interpretable machine learning tools for predicting intermolecular interactions. The synergy between the investigation of three dimensional structures, molecular modeling, and bioinformatics in the context of the Proteomics Building planned for the Busch campus creates a rich environment for cutting edge research on frontier problems in structural biology.

The second convergent theme has to do with the modeling of biomolecular networks at a cellular level. The challenge here is to understand how complex cellular dynamics becomes possible through the interactions of simpler components involving enzymatic control of elementary chemical steps. Several BioMaPS faculty members, with interest in nonlinear dynamics, have been investigating how structures of such networks determine possible dynamical behavior, with an emphasis on nontrivial qualitative predictions that are robust to refinements of the biological model. Applications of these tools range from understanding the regulation of metabolism in bacteria to the exploration of the “landscape” in which apparently irreversible cell fate specification takes place in eukaryotes. The possibilities of interactions with stem cell related activities on campus are evident and will stimulate this effort to gain further strength.

The third common theme bridges across many scales: from molecular to cellular to the organismal level. BioMaPS Institute researchers have been actively collaborating with the Cancer Institute of New Jersey to bring quantitative tools to cancer research. The subjects include bioinformatic approaches to identify cancer subtypes, the study of biomarkers, tissue-level modeling, and the study of the population genetics of cancer related SNPs. Approaching translational biomedical research using modern computational and mathematical tools from multiple perspectives is a major developing theme of the BioMaPS Institute for Quantitative Biology.