Chapters authored
Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research
Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers. Mathematical modeling guides the rational design of synthetic gene circuits. These systems are then assembled into unified constructs from existing and/or modified genetic components from a range of organisms. In this chapter, we describe modeling tools for the design and characterization of chemical- and light-inducible synthetic gene circuits in different organisms and highlight how synthetic gene circuits are advancing biomedical research. Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.
Part of the book: Synthetic Genomics
Machine Learning for Antimicrobial Resistance Research and Drug Development
Machine learning is a subfield of artificial intelligence which combines sophisticated algorithms and data to develop predictive models with minimal human interference. This chapter focuses on research that trains machine learning models to study antimicrobial resistance and to discover antimicrobial drugs. An emphasis is placed on applying machine learning models to detect drug resistance among bacterial and fungal pathogens. The role of machine learning in antibacterial and antifungal drug discovery and design is explored. Finally, the challenges and prospects of applying machine learning to advance basic research on and treatment of antimicrobial resistance are discussed. Overall, machine learning promises to advance antimicrobial resistance research and to facilitate the development of antibacterial and antifungal drugs.
Part of the book: The Global Antimicrobial Resistance Epidemic