surement technologies, pharmaceutical companies their extensive databases, and nonprofit disease research foundations their refined expertise. By providing the framework for these communities to work together to address the challenge of understanding the genotype-phenotype connection, the New Biology can accelerate fundamental understanding of the systems that underlie health and the development of the tools and technologies that will in turn lead to more efficient approaches to developing therapeutics (Box 2.3). And just as with sequencing technology, the technological and conceptual breakthroughs that emerge from these efforts could revolutionize the capacity and sophistication of all biological research.
The future holds truly imposing challenges for humankind: efficiently improving the sustainable productivity of diverse food crops, producing sustainable substitutes for fossil fuels, monitoring and restoring ecosystem services, and understanding and promoting human health. The New Biology described in this report, if properly nurtured and supported, has the potential to contribute to real progress in meeting these challenges and many tools and approaches will be shared for all four problem areas. The projected impacts are significant, from both a societal and economic perspective. Furthermore, the importance of the challenges to which the New Biology will contribute ensure
Developing Therapeutics to Prevent, Treat, and Cure Disease
The future of therapeutics lies in the application of new technologies as tools for detecting and treating diseases. Therapeutic efforts will also benefit from an increased understanding of networks. Therapeutics that focus on a single driver may miss both the critical role played by other genes as well as the ease with which a malignant cell, for instance, may utilize alternative parts of the larger network to side-step the drug’s effect, and thus continue to thrive. Similarly, adverse side effects can result when intervention in one network causes unforeseen changes in others. Complicated as these networks are, we are now in a position to study the response of complex systems to a range of perturbagens (both natural mutations and introduced chemicals), providing an important opportunity to probe the pattern of interactions and refine the model. This approach may also identify underappreciated network pressure points—possible drug targets or biomarkers that are less evident in traditional linear models.