A composite computational model of liver glucose homeostasis. II. Exploring system behaviour

Sumner, Tom; Hetherington, James; Seymour, Robert M.; Li, L.; Varela‐Rey, Marta; Yamaji, Sachie; Saffrey, Peter; Margoninski, O; Bogle, David; Finkelstein, A.; Warner, Alan

doi:10.1098/rsif.2011.0783

Cited by 17 publications

(13 citation statements)

References 14 publications

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“…Because sensitivity analysis can be an effective tool for systematically identifying the most and the least significant parameters, it can provide valuable information on the robustness of biological systems. Summer et al ., [ 54 , 55 ] implemented a SA method by exploring a group-based procedure, where SA is applied to groups of parameters. The result of this analysis can be used to identify the components of the model that are most important in determining the model behavior.…”

Section: Methodsmentioning

confidence: 99%

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

et al. 2015

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Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

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Section: Methodsmentioning

confidence: 99%

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

et al. 2015

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“…The model consists of seven models drawn from different sources incorporated into a model management system which allows data and models to be updated as improved information becomes available. [32][33][34] The model was able to predict the naturally occurring oscillations, known as ultradian oscillations. Previously these were thought to be produced by the pancreas alone but the model demonstrated that this is in fact a systems phenomenon resulting from interactions between the pancreas and the liver.…”

Section: Modeling and Analysis Of Basic Liver System Functionmentioning

confidence: 99%

Liver function as an engineering system

et al. 2016

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Process Systems Engineering has tackled a wide range of problems including manufacturing, the environment, and advanced materials design. Here we discuss how tools can be deployed to tackle medical problems which involve complex chemical transformations and spatial phenomena looking in particular at the liver system, the body's chemical factory. We show how an existing model has been developed to model distributed behavior necessary to predict the behavior of drugs for treating liver disease. The model has been used to predict the effects of suppression of de novo lipogenesis, stimulation of b-oxidation and a combination of the two. A reduced model has also been used to explore the prediction of behavior of hormones in the blood stream controlling glucose levels to ensure that levels are kept within safe bounds using interval methods. The predictions are made resulting from uncertainty in two key parameters with oscillating input resulting from regular feeding. V C 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3285-3297, 2016 Keywords: mathematical modeling, biomedical engineering, systems biology, medical, controlThe liver, its regulation, and its diseases Why systems engineering of the liver?Systems Engineering is the discipline of the management of complex engineering systems over their whole life cycle. Process Systems Engineering is the study of complex process engineering systems involving chemical and physical change. Prof Roger Sargent was a pioneer from the 1950s in seeing the potential that computers could have to revolutionize the way that we tackled these problems.1 He also saw the way Chemical Engineering was broadening its base into molecular and biological systems to improve manufacturing. 1,2The liver is one part of a very complex processing system which ensures that all parts of the body receive the energy and nutrients that they need, that waste products are removed efficiently from the various streams, and that short and long term well being are maintained. It is the body's central chemical processing organ. Considering the liver system as one which controls chemical and physical change within the body, particularly of nutrition, makes it a legitimate area of study for Process Systems Engineers. A number of Chemical Engineers have also contributed to the use for Process Systems Engineering to a range of medical applications. Bogle 3 reviews recent

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“…Fisher, Harel and Henzinger have done computational modeling of biological systems as reactive systems [17]. Hetherington et al and Sumner et al have composed an advanced computational model of a biological system from sub-models describing its di↵erent aspects [22,48].…”

Section: Related Workmentioning

confidence: 99%

Automated requirements analysis for a molecular watchdog timer

Ellis

Henderson

Klinge

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Dynamic systems in DNA nanotechnology are often programmed using a chemical reaction network (CRN) model as an intermediate level of abstraction. In this paper, we design and analyze a CRN model of a watchdog timer, a device commonly used to monitor the health of a safety critical system. Our process uses incremental design practices with goal-oriented requirements engineering, software verification tools, and custom software to help automate the software engineering process. The watchdog timer is comprised of three components: an absence detector, a threshold filter, and a signal amplifier. These components are separately designed and verified, and only then composed to create the molecular watchdog timer. During the requirements-design iterations, simulation, model checking, and analysis are used to verify the system. Using this methodology several incomplete requirements and design flaws were found, and the final verified model helped determine specific parameters for biological experiments.

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A composite computational model of liver glucose homeostasis. II. Exploring system behaviour

Cited by 17 publications

References 14 publications

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

Liver function as an engineering system

Automated requirements analysis for a molecular watchdog timer

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