Lewis Baker, Department of Applied Mathematics, University of Colorado Boulder
Rules Rule! Rule-Based Modeling in Biochemistry
Rule-based modeling is becoming increasingly recognized for its potential to heuristically simplify problems involving large interacting networks. It has recently been implemented to study the complex multi-ligand dynamics of receptor tyrosine kinase signaling pathways, including the epithelial growth factor receptor (EGFR) and insulin receptor (IR) pathways. In this talk I will present the mathematical underpinnings of rule-based modeling and present successful applications of the method in the context of biochemical signaling.
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Harry Dudley, Department of Applied Mathematics, University of Colorado Boulder
Sensitivity and Bifurcation Analysis of a Differential-Algebraic Equation Model for a Microbial Electrolysis Cell
Microbial electrolysis cells (MECs) are a promising new technology
for producing hydrogen cheaply, efficiently, and sustainably. The technology is based on microbial fuel cells in which bacteria oxidize an organic substrate to generate current, providing decreased electricity costs when compared to direct electrolysis. MECs are also more efficient than fermentation methods and can be fed fermentation effluent or cheap and readily available wastewater. However, to scale up this technology, we need a better understanding of the processes in the devices. In this effort, we present an index-one differential-algebraic equation (DAE) model of a microbial electrolysis cell with an algebraic constraint on current. We then perform sensitivity and bifurcation analysis for this DAE system.听 The sensitivity analysis yields temporal regions wherein reactor adjustments will have the largest impact on productivity.听 For the bifurcation analysis, we present results concerning the classification of transcritical bifurcations in the input flow rate.听 Overall, our conclusions provide guidance on optimizing performance of batch-fed and continuous-flow reactors.