scholarly journals Hierarchical semantic composition of biosimulation models using bond graphs

2021 ◽  
Vol 17 (5) ◽  
pp. e1008859
Author(s):  
Niloofar Shahidi ◽  
Michael Pan ◽  
Soroush Safaei ◽  
Kenneth Tran ◽  
Edmund J. Crampin ◽  
...  
Keyword(s):  
Computational Models ◽  
Model Development ◽  
Bond Graph ◽  
Composite Model ◽  
Model Composition ◽  
Bond Graphs ◽  
Arterial Circulation ◽  
General Methodology ◽  
Major Benefit ◽  
Physiological Systems

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.

scholarly journals Hierarchical semantic composition of biosimulation models using bond graphs

2021 ◽  
Author(s):  
Niloofar Shahidi ◽  
Michael Pan ◽  
Soroush Safaei ◽  
Kenneth Tran ◽  
Edmund J. Crampin ◽  
...  
Keyword(s):  
Computational Models ◽  
Reference Model ◽  
Model Development ◽  
Circulatory System ◽  
Model Composition ◽  
Bond Graphs ◽  
Arterial Circulation ◽  
Systems Modelling ◽  
Underlying Processes ◽  
Physiological Systems

AbstractSimulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.Author summaryBiological and physiological systems usually involve multiple underlying processes, mechanisms, structures, and phenomena, referred to here as sub-systems. Modelling the whole system every time from scratch requires a huge amount of effort. An alternative is to model each sub-system in a modular fashion, i.e., containing meaningful interfaces for connecting to other modules. Such modules are readily combined to produce a whole-system model. For the combined model to be consistent, modules must be described using the same modelling scheme. One way to achieve this is to use energy-based models that are consistent with the conservation laws of physics. Here, we present an approach that achieves this using bond graphs, which allows modules to be combined faster and more efficiently. First, physically plausible modules are generated using a small number of template modules. Then a meaningful interface is added to each module to automate connection. This approach is illustrated by applying this method to an existing model of the circulatory system and verifying the results against the reference model.

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

Bond Graph Sign Conventions

1975 ◽  
Vol 97 (2) ◽  
pp. 184-188 ◽  
Author(s):  
A. S. Perelson
Keyword(s):  
Graph Model ◽  
Bond Graph ◽  
Equivalence Classes ◽  
Parameter System ◽  
Bond Graphs ◽  
Lumped Parameter ◽  
Oriented Object

The lack of arbitrariness in the choice of bond graph sign conventions is established. It is shown that an unoriented bond graph may have no unique meaning and that with certain choices of orientation a bond graph may not correspond to any lumped parameter system constructed from the same set of elements. Network interpretations of these two facts are given. Defining a bond graph as an oriented object leads to the consideration of equivalence classes of oriented bond graphs which represent the same system. It is also shown that only changes in the orientation of bonds connecting 0-junctions and 1-junctions can lead to changes in the observable properties of a bond graph model.

scholarly journals Analysing and simulating energy-based models in biology using BondGraphTools

2021 ◽  
Author(s):  
Peter Cudmore ◽  
Michael Pan ◽  
Peter J. Gawthrop ◽  
Edmund J. Crampin
Keyword(s):  
Systems Biology ◽  
Mathematical Models ◽  
Bond Graph ◽  
Experimental Measurements ◽  
Bond Graphs ◽  
Graph Models ◽  
Conservation Of Energy ◽  
Conservation Of Mass ◽  
Physical Systems ◽  
Complex Interactions

AbstractLike all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.

scholarly journals Meeting the multiscale challenge: representing physiology processes over ApiNATOMY circuits using bond graphs

2017 ◽  
Vol 8 (1) ◽  
pp. 20170026 ◽  
Author(s):  
B. de Bono ◽  
S. Safaei ◽  
P. Grenon ◽  
P. Hunter
Keyword(s):  
Bond Graph ◽  
Bond Graphs ◽  
Acid Base ◽  
Distinct Process ◽  
Biophysical Processes ◽  
Biological Structures

We introduce, and provide examples of, the application of the bond graph formalism to explicitly represent biophysical processes between and within modular biological compartments in ApiNATOMY. In particular, we focus on modelling scenarios from acid–base physiology to link distinct process modalities as bond graphs over an ApiNATOMY circuit of multiscale compartments. The embedding of bond graphs onto ApiNATOMY compartments provides a semantically and mathematically explicit basis for the coherent representation, integration and visualisation of multiscale physiology processes together with the compartmental topology of those biological structures that convey these processes.

Some Key Issues in Using Bond Graphs and Genetic Programming for Mechatronic System Design

2001 ◽  
Author(s):  
R. C. Rosenberg ◽  
E. D. Goodman ◽  
Kisung Seo
Keyword(s):  
Genetic Programming ◽  
System Design ◽  
Design Space ◽  
Bond Graph ◽  
Mechatronic System ◽  
Bond Graphs ◽  
Mechatronic Systems ◽  
Energy Behavior ◽  
Key Issues ◽  
Bond Graph Modeling

Abstract Mechatronic system design differs from design of single-domain systems, such as electronic circuits, mechanisms, and fluid power systems, in part because of the need to integrate the several distinct domain characteristics in predicting system behavior. The goal of our work is to develop an automated procedure that can explore mechatronic design space in a topologically open-ended manner, yet still find appropriate configurations efficiently enough to be useful. Our approach combines bond graphs for model representation with genetic programming for generating suitable design candidates as a means of exploring the design space. Bond graphs allow us to capture the common energy behavior underlying the several physical domains of mechatronic systems in a uniform notation. Genetic programming is an effective way to generate design candidates in an open-ended, but statistically structured, manner. Our initial goal is to identify the key issues in merging the bond graph modeling tool with genetic programming for searching. The first design problem we chose is that of finding a model that has a specified set of eigenvalues. The problem can be studied using a restricted set of bond graph elements to represent suitable topologies. We present the initial results of our studies and identify key issues in advancing the approach toward becoming an effective and efficient open-ended design tool for mechatronic systems.

Demonstrating the Generation of Bond Graphs From 3D Assemblies

2020 ◽  
Author(s):  
Corey J. Alicchio ◽  
Justin S. Vitiello ◽  
Pradeep Radhakrishnan
Keyword(s):  
Bond Graph ◽  
Dynamic Responses ◽  
Bond Graphs ◽  
Mechatronic Systems ◽  
3D Cad ◽  
Related Information ◽  
Grammar Rules ◽  
Gear Trains ◽  
System Graph ◽  
Future Work

Abstract The bond graph method provides a generic and simple way to compute differential equations and dynamic responses for complex mechatronic systems. This paper will illustrate the process of automatically generating bond graphs from 3D CAD assemblies of gear-trains. Using appropriate CAD application programming interfaces (APIs), information on parts and mates within an existing assembly is extracted. The extracted information is stored as an identity graph, which also stores all geometry and mass related information of every part. Grammar rules are then used to transform the identity graph to a system graph, which is then converted to bond graph using an existing bond graph generation program. The paper will discuss the process, challenges and planned future work.

Open Systems Formulation and Gaining Insight Using Lagrangian Bond Graphs

2000 ◽  
Author(s):  
Robin C. Redfield
Keyword(s):  
System Dynamics ◽  
Open Systems ◽  
Classical Method ◽  
System Modeling ◽  
Bond Graph ◽  
Small Scale ◽  
Bond Graphs ◽  
Model Modification ◽  
System Equations ◽  
Insight Into

Abstract Models of a small-scale water rocket are developed as an example of open system modeling by both the bond graph approach and a more classical method. One goal of the development is to determine the benefits of the bond graph approach into affording insight into the system dynamics. Both modeling approaches yield equivalent differential equations as they should, while the bond graph approach yields significantly more insight into the system dynamics. If a modeling goal is to simply find the system equations and predict behavior, the classical approach may be more expeditious. If insight and ease of model modification are desired, the bond graph technique is probably the better choice. But then you have to learn it!

Multilink for bilingual language production

2018 ◽  
Vol 22 (04) ◽  
pp. 687-688 ◽  
Author(s):  
IVA IVANOVA ◽  
DANIEL KLEINMAN
Keyword(s):  
Lexical Decision ◽  
Language Production ◽  
Computational Models ◽  
Current Model ◽  
Word Naming ◽  
Major Benefit ◽  
Theoretical Assumptions

A major benefit of computational models is their ability to demonstrate which theoretical assumptions are truly necessary to explain a pattern of data. Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (in press) have impressively shown with Multilink that it is possible to account for a range of findings from bilingual lexical decision, word naming, and forward and backward translation tasks with an integrated lexicon, without lateral connections between translation equivalents, and without inhibition. In this commentary, we consider the applicability of the current model to other multilingual language production tasks, and note where the model's assumptions might need revision as its scope is expanded.

scholarly journals Bond graph modelling of the cardiac action potential: implications for drift and non-unique steady states

2018 ◽  
Vol 474 (2214) ◽  
pp. 20180106 ◽  
Author(s):  
Michael Pan ◽  
Peter J. Gawthrop ◽  
Kenneth Tran ◽  
Joseph Cursons ◽  
Edmund J. Crampin
Keyword(s):  
Action Potential ◽  
Conservation Laws ◽  
Cardiac Electrophysiology ◽  
Bond Graph ◽  
Steady States ◽  
Cardiac Action Potential ◽  
Bond Graphs ◽  
Charge Conservation ◽  
Cardiac Action ◽  
Wide Range

Mathematical models of cardiac action potentials have become increasingly important in the study of heart disease and pharmacology, but concerns linger over their robustness during long periods of simulation, in particular due to issues such as model drift and non-unique steady states. Previous studies have linked these to violation of conservation laws, but only explored those issues with respect to charge conservation in specific models. Here, we propose a general and systematic method of identifying conservation laws hidden in models of cardiac electrophysiology by using bond graphs, and develop a bond graph model of the cardiac action potential to study long-term behaviour. Bond graphs provide an explicit energy-based framework for modelling physical systems, which makes them well suited for examining conservation within electrophysiological models. We find that the charge conservation laws derived in previous studies are examples of the more general concept of a ‘conserved moiety’. Conserved moieties explain model drift and non-unique steady states, generalizing the results from previous studies. The bond graph approach provides a rigorous method to check for drift and non-unique steady states in a wide range of cardiac action potential models, and can be extended to examine behaviours of other excitable systems.

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