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Ingalls, B. P.

Publications and source records attributed to Ingalls, B. P..

2 recordsLinked to original sources

Kinetic modelling of β-cell metabolism reveals control points in the insulin-regulating pyruvate cycling pathways

Insulin, a key hormone in the regulation of glucose homeostasis, is secreted by pancreatic {beta}-cells in response to elevated glucose levels. Insulin is released in a biphasic manner in response to glucose metabolism in {beta}-cells. The first phase of insulin secretion is triggered by an increase in the ATP:ADP ratio; the second phase occurs in response to both a rise in ATP:ADP as well as other key metabolic signals, including a rise in the NADPH:NADP+ ratio. Experimental evidence indicates that pyruvate-cycling pathways play an important role in the elevation of the NADPH:NADP+ ratio in response to glucose. In this work we developed a kinetic model for the tricarboxylic acid cycle and pyruvate cycling pathways. We successfully validated our model against recent experimental observations and performed local and global sensitivity analysis to identify key regulatory interactions in the system. The model predicts that the dicarboxylate carrier (DIC) and pyruvate transporter (PYC) are the most important regulators of pyruvate cycling and NADPH production. In contrast, our analysis showed that variation in the pyruvate carboxylase (PC) flux was compensated by a response in the activity of mitochondrial isocitrate dehydrogenase (ICDm) resulting in minimal effect on overall pyruvate cycling flux. The model predictions suggest starting points for further experimental investigation, as well as potential drug targets for treatment of type 2 diabetes.

systems biology↗

NLoed: A Python package for nonlinear optimal experimental design in systems biology

MotivationModelling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data, and on how well-suited the available data is to a particular modelling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modelling objective. However, implementation of OED is limited by currently available software tools that are not well-suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. ResultsHere we present the NLoed software package. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models, and facilitates modelling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modelling workflow. NLoed offers an accessible, modular, and flexible OED tool-set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLOEDs capabilities by applying it to experimental design for characterization of a bacterial optogenetic system. AvailabilityNLoed is available via pip from the PyPi repository; https://pypi.org/project/nloed/. Source code, documentation and examples can be found on Github at https://github.com/ingallslab/NLoed. Contactbingalls@uwaterloo.ca Supplementary informationSupplementary materials are available online.

systems biology↗