The goal of this project is to help make algorithms a preferred language for describing problem-solving strategies used by biological systems. Algorithms have long been the language of computing, and all biological systems must compute (i.e., process information) to survive. Thus, the study of "algorithms in nature" can represent a new field of interdisciplinary research between computer science and biology. More specifically, this project seeks to uncover fundamental network design strategies and optimization principles shared by branching structures in nature, including plant shoot (above-ground) and root (below-ground) architectures, as well as neural branching arbors in the brain. Understanding the basic patterns that evolution has used to design these systems has bi-directional benefits; it can lead to improved understanding of how these natural networks process information and function in both health and disease, and it can lead to new computational strategies for building better engineered networks. Educationally, making the study of algorithms a requirement in life science curricula can help educate the next-generation of interdisciplinary scientists.
This project has three Aims. The first Aim is to discover principles governing how plant architectures grow and adapt to changing environments. This Aim will: (1) study how different network optimization trade-offs sculpt the shape of plant shoot architectures using the theory of Pareto optimality, and how different trade-off objectives are prioritized depending on the environment and species; (2) determine the molecular mechanisms (genes) that drive prioritizations; and (3) determine what search algorithms are used by plant shoots to find resources and to strategize growth. These questions will be studied using 3D laser scanning of crop species (tomato, tobacco, sorghum, corn, rice) grown across multiple conditions and time-points, and of model species with different genetic backgrounds. Overall, this Aim will link network design principles commonly studied in computer science with those driving network formation and adaptation in plants, and may help design and evaluate breeding strategies to enhance crop yield. The second Aim is to develop models of network warfare to study plant-plant competition. This Aim will: (1) create "gladiator-style" arenas to study how two plants battle for limited light; (2) develop game theory methods to assess whether dominant or stable strategies emerge; and (3) quantify how competition strategies differ based on the species of the two plants and their growth environment. This Aim will lead to predictive models of plant social interactions that can inform the design of polyculture farming spaces based on which plants "get along" the best. The third Aim is to test the generality of these principles to other biological and engineered branching structures. This Aim will test if the branching properties learned from plant shoot architectures also dictate the structure of plant root architectures below ground and neural (axonal and dendritic) architectures in the brain. For example, are root architectures also Pareto optimal? What search algorithms do they use to find nutrients? How does competition affect these strategies? This will also lead to the first quantitative comparison of branching structures across two kingdoms of life, from plants to neurons. Finally, this Aim will also study human-engineered networks that also must adapt their structure to resource availability and demand in dynamic environments. Insights from biology could reveal new strategies for optimal reconstruction of damaged infrastructure after war or natural disasters, or extension of existing infrastructure into developing areas.
URL: www.snl.salk.edu/~navlakha/
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
