Data-driven mechanistic modeling for complex systems design

​My primary research goal is to connect the speed and automation of top-down data-driven model selection methods with the explanatory power of bottom-up mechanistic modeling. Mechanistic modeling provides a powerful framework for systems design and optimization, but relies on close integration of model development and experimental validation. Data science methods promise rapid, automated, descriptions and predictions for complex systems, generally without knowledge of underlying mechanism. By combining the two, I can more rapidly develop explanatory mechanistic models that can be used to optimize system performance and design engineering solutions.

Data driven methods for inferring nonlinear dynamics on biological networks. 

Inferring the structure and dynamics of biological networks is critical to understanding and controlling their functionality. Implicit-SINDy is a data-driven method for inferring nonlinear dynamical systems which combines a compact feature library, implicit formulation to enable discovery of rational functions, and sparsity promoting non-convex optimization.  In contrast to other algorithms, the models constructed using implicit-SINDy are biologically interpretable in terms of the same network motifs that would be used in bottom-up mechanistic modeling frameworks. Thus, these rapidly constructed models provide mechanistic understanding that could be leveraged for therapeutic gene modulation—to treat genetic diseases, metabolic engineering—to design novel biochemical pathways and products, and intervention in infectious disease network—such as outbreak detection and response.
Spatial organization in cells enhances throughput of biochemical pathways
Bacteria can perform a wide variety of chemsitry. Bioengineering can tap into these natural biochemical factories, producing high value products like biofuels, alternatives to plastic, and pharmaceuticals. Engineering photosynthetic cyanobacteria in particular, would allow production of compounds from inorganic CO2 and sunlight. In natural systems bacteria organize their biochemical reactions within the cell to reduce leakage of substrates out of the cell, minimize the eff ects of volatile or toxic intermediates, and avoiding unwanted competitive reactions.​ By mapping out which of these organizational strategies work best for a variety of chemical pathways, we can formulate design rules for bioengineering.
Systems-level modeling to optimize renewable energy systems
I am passionate about research which enhances alternative energy. My interest in biological metabolic networks was originally inspired by the potential to bio-engineer living bio-fuel factories. I have also worked on design rules for solar cells and wind turbine arrays.