Automated Prioritization and Design of Experiments  

Collaborative Research: ABI Innovation: Automated Prioritization and Design of Experiments to Validate and Improve Mathematical Models of Molecular Regulatory Systems

Source of Support: NSF


Abstract: Complex networks of interacting molecules control all the physiological processes that occur in a living cell. It is impossible to deduce the functions of these networks using intuitive reasoning alone. Therefore, scientists construct mathematical models of cellular processes that can be simulated in the computer. Unfortunately, it takes many years of careful study of the scientific literature and steady, incremental progress to construct detailed, comprehensive, and accurate mathematical models. This project will create an integrated computational – experimental framework that will significantly accelerate the process of mathematical modeling. The project will create several scientific innovations including (a) novel approaches to searching the space of model simulations to identify promising predictions, (b) computational techniques to efficiently plan experiments, (c) experimental methods that use these plans to rapidly test model predictions, and (d) automatic techniques to extend and refine the models to accommodate the results of these experiments. The project will benefit science by applying this framework to develop a comprehensive, new model that describes how nutrients control the growth of baker’s yeast cells. Long term benefits to society will accrue from the use of the methods developed by this project to study any complex cellular system, e.g., those implicated in cell proliferation in cancers, wound healing, and tissue regeneration.

Computational cell biologists have constructed detailed, mechanistic, and predictive mathematical models of many physiological processes in living cells. In principle, such models can predict the phenotypes of novel combinations of gene mutations. However, this potential has not been fully realized for three reasons: (a) the number of possible combinations grows explosively, complicating the search and prioritization of informative mutants, (b) it is impossible to manually plan experiments to make and characterize thousands of mutants, and (c) automated techniques that can resolve contradictions between experimental results and model predictions are still under development. The goal of this project is to create a unique, integrated framework that will address these challenges by (a) systematically generating informative predictions from mathematical models, (b) computationally synthesizing high-throughput experimental plans to test these predictions, and (c) rapidly reconciling inconsistencies between model and experiment. The project will apply this framework to models of cell growth and division in budding yeast. This transformative approach will streamline and accelerate the mathematical modeling cycle. The computational approaches developed for synthesizing experimental plans will be broadly applicable to other organisms, including mammalian cells, that can be systematically perturbed using siRNA or CRISPR/Cas9. Because nutrient conditions, metabolic fluxes, energy budgets, protein synthesis, and cell cycle regulation are central to wound healing and tissue regeneration, to the engineering of artificial tissues and organs, and to the expansion and spread of tumors, the methods and models we develop here in the context of budding yeast cell biology will be of great relevance to mammalian biology. The educational component of our project will infuse computational thinking into biology at the undergraduate level and encourage students with backgrounds in life science, engineering, or computation to consider systems biology as a career choice. The project will offer a 10-week summer research institute on ‘Computationally – Driven Experimental Biology’ to six undergraduate students, consisting of lectures on project -related topics and a single collaborative research project. Involving all the students in a single research project will expose them to team science and give them an appreciation of how computer science, mathematics, and experimental cell biology can be seamlessly interwoven to study cellular processes. The results of this project will appear at .

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.



Adames NR, Gallegos JE, Peccoud J (2019) Yeast genetic interaction screens in the age of CRISPR/Cas, Current Genetics 65: 307. DOI:10.1007/S00294-018-0887-8

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