Hybrid Simulation Algorithm Research Articles

A workflow for the hybrid modelling and simulation of multi-timescale biological systems

With the steady advance of in-silico biological experimentation, model construction and simulation becomes a ubiquitous tool to understand and predict the behaviour of many biological systems. However, biological processes may contain components from different types of reaction networks, resulting in models with different (e.g., slow and fast) timescales. Hybrid simulation is one approach which can be employed to efficiently execute multi-timescale models. In this paper, we present a methodology and workflow utilizing (coloured) hybrid Petri nets to construct smaller and more complicated hybrid models. The presented workflow integrates algorithms and ideas from hybrid simulation of biochemical reaction networks as well as Petri nets. We also construct multi-timescale hybrid models and then show how these models can be efficiently executed using three different advanced hybrid simulation algorithms.

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator.

Coarse-grained simulations of phase separation driven by DNA and its sensor protein cGAS

The enzyme cGAS functions as a sensor that recognizes the cytosolic DNA from foreign pathogen. The activation of the protein triggers the transcription of inflammatory genes, leading into the establishment of an antipathogen state. An interesting new discovery is that the detection of DNA by cGAS induced the formation of liquid-like droplets. However how cells regulate the formation of these droplets is still not fully understood. In order to unravel the molecular mechanism beneath the DNA-mediated phase separation of cGAS, we developed a polymer-based coarse-grained model which takes into accounts the basic structural organization in DNA and cGAS, as well as the binding properties between these biomolecules. This model was further integrated into a hybrid simulation algorithm. With this computational method, a multi-step kinetic process of aggregation between cGAS and DNA was observed. Moreover, we systematically tested the model under different concentrations and binding parameters. Our simulation results show that phase separation requires both cGAS dimerization and protein-DNA interactions, whereas polymers can be kinetically trapped in small aggregates under strong binding affinities. Additionally, we demonstrated that supramolecular assembly can be facilitated by increasing the number of functional modules in protein or DNA polymers, suggesting that multivalency and intrinsic disordered regions play positive roles in regulating phase separation. This is consistent to previous experimental evidences. Taken together, this is, to the best of our knowledge, the first computational model to study condensation of cGAS-DNA complexes. While the method can reach the timescale beyond the capability of atomic-level MD simulations, it still includes information about spatial arrangement of functional modules in biopolymers that is missing in the mean-field theory. Our work thereby adds a useful dimension to a suite of existing experimental and computational techniques to study the dynamics of phase separation in biological systems.

HSAEP: a new platform to evaluate hybrid simulation algorithms

HSAEP: a new platform to evaluate hybrid simulation algorithms

Understand the Functions of Scaffold Proteins in Cell Signaling by a Mesoscopic Simulation Method

Scaffold proteins are central players in regulating the spatial-temporal organization of many important signaling pathways in cells. They offer physical platforms to downstream signaling proteins so that their transient interactions in a crowded and heterogeneous environment of cytosol can be greatly facilitated. However, most scaffold proteins tend to simultaneously bind more than one signaling molecule, which leads to the spatial assembly of multimeric protein complexes. The kinetics of these protein oligomerizations are difficult to quantify by traditional experimental approaches. To understand the functions of scaffold proteins in cell signaling, we developed a, to our knowledge, new hybrid simulation algorithm in which both spatial organization and binding kinetics of proteins were implemented. We applied this new technique to a simple network system that contains three molecules. One molecule in the network is a scaffold protein, whereas the other two are its binding targets in the downstream signaling pathway. Each of the three molecules in the system contains two binding motifs that can interact with each other and are connected by a flexible linker. By applying the new simulation method to the model, we show that the scaffold proteins will promote not only thermodynamics but also kinetics of cell signaling given the premise that the interaction between the two signaling molecules is transient. Moreover, by changing the flexibility of the linker between two binding motifs, our results suggest that the conformational fluctuations in a scaffold protein play a positive role in recruiting downstream signaling molecules. In summary, this study showcases the capability of computational simulation in understanding the general principles of scaffold protein functions.

An isomorphic multi-energy flow modeling for integrated power and thermal system considering nonlinear heat transfer constraint

Development of unified electrical and thermal power flow model is critical for effective analysis of integrated power and thermal systems (IPTSs), where existing electric-analogy thermal circuit models of thermal systems are not analogous to that of power systems in physics, and heat transfer constraints of heat exchange facilities (HEFs) are usually simplified with inaccurate assumptions. Inspired by the irreversibility of transport phenomena, this work redefines the thermal resistance of HEFs and proposes a heat current model to comprehensively reflect heat transfer, migration and storage characteristics, which is in consistent with electrical power flow model and yields multi-energy flow model of IPTSs that can be solved using existing power system simulators. A multi-time scale hybrid simulation algorithm is further proposed and applied in simulating an urban IPTS. Results depict the developed method takes consideration of heat transfer characteristics of HEFs and secondary heating network accurately, which are ignored in traditional methods. Besides, introducing electric heat pumps as complementary heat sources to change the operation mode of combined heat and power plant from heat-led to power-led reduces 820.3 MWh wind curtailment by consuming 140.9 MWh electricity, where a 505.6 MWh heat stored in pipelines due to heat migration delay is fully exploited.

Phase II monitoring of multivariate profiles with estimated parameters and optimal phase I subgroups

In this paper, we investigate the effect of parameter estimation from in-control Phase I samples on the in-control and out-of-control performance of the four Phase II control charts used for monitoring multivariate multiple linear profiles. These methods are evaluated in terms of out-of-control performance using corrected limits. The monitoring approaches investigated are then compared using the statistical properties of the ARL distribution. Furthermore, two optimization models are proposed for finding the optimum number of Phase I samples needed to achieve proper parameter estimations of desired accuracy and solved by using a novel hybrid simulation algorithm named HBSO.

Nonuniversal critical aging scaling in three-dimensional Heisenberg antiferromagnets

We numerically investigate the stationary and non-equilibrium critical dynamics in three-dimensional isotropic Heisenberg antiferromagnets. Since the non-conserved staggered magnetization couples dynamically to the conserved magnetization density, we employ a hybrid simulation algorithm that combines reversible spin precession with relaxational Kawasaki spin exchange processes. We measure the dynamic critical exponent and identify a suitable intermediate time window to obtain the aging scaling exponents. Our results support an earlier renormalization group prediction: While the critical aging collapse exponent assumes a universal value, the associated temporal decay exponent in the two-time spin autocorrelation function depends on the initial distribution of the conserved fields; here, specifically on the width of the initial spin orientation distribution.

Hybrid simulation algorithm for efficient numerical solution of population balance equations

Cell-to-cell variability of multicellular systems affects many processes from bioprocess engineering, systems biology and systems medicine. Multiscale modeling of these cellular heterogeneities with population balance equations is a valuable tool to understand the interplay of individual cell dynamics (intracellular level), interactions of cells with each other (population level) and interactions of cells with other molecular species (extracellular level). The resulting mathematical models represent high dimensional partial integro-differential equations which are rarely solvable analytically. As alternative to classical numerical solution methods which are not well suited for efficient solution, we propose a hybrid simulation algorithm in this contribution. This technique is based on a combination of the method of partial characteristics and approximate moment methods using monomial cubatures. The technique’s efficiency and accuracy is shown for a multicellular system describing cell-to-cell signaling in osteochondro-switch models.

Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning.

The reaction-diffusion master equation (RDME) is a model that allows for efficient on-lattice simulation of spatially resolved stochastic chemical kinetics. Compared to off-lattice hard-sphere simulations with Brownian dynamics or Green's function reaction dynamics, the RDME can be orders of magnitude faster if the lattice spacing can be chosen coarse enough. However, strongly diffusion-controlled reactions mandate a very fine mesh resolution for acceptable accuracy. It is common that reactions in the same model differ in their degree of diffusion control and therefore require different degrees of mesh resolution. This renders mesoscopic simulation inefficient for systems with multiscale properties. Mesoscopic-microscopic hybrid methods address this problem by resolving the most challenging reactions with a microscale, off-lattice simulation. However, all methods to date require manual partitioning of a system, effectively limiting their usefulness as "black-box" simulation codes. In this paper, we propose a hybrid simulation algorithm with automatic system partitioning based on indirect a priori error estimates. We demonstrate the accuracy and efficiency of the method on models of diffusion-controlled networks in 3D.

Snoopy\u2019s hybrid simulator: a tool to construct and simulate hybrid biological models

BackgroundHybrid simulation of (computational) biochemical reaction networks, which combines stochastic and deterministic dynamics, is an important direction to tackle future challenges due to complex and multi-scale models. Inherently hybrid computational models of biochemical networks entail two time scales: fast and slow. Therefore, it is intricate to efficiently and accurately analyse them using only either deterministic or stochastic simulation. However, there are only a few software tools that support such an approach. These tools are often limited with respect to the number as well as the functionalities of the provided hybrid simulation algorithms.ResultsWe present Snoopy’s hybrid simulator, an efficient hybrid simulation software which builds on Snoopy, a tool to construct and simulate Petri nets. Snoopy’s hybrid simulator provides a wide range of state-of-the-art hybrid simulation algorithms. Using this tool, a computational model of biochemical networks can be constructed using a (coloured) hybrid Petri net’s graphical notations, or imported from other compatible formats (e.g. SBML), and afterwards executed via dynamic or static hybrid simulation.ConclusionSnoopy’s hybrid simulator is a platform-independent tool providing an accurate and efficient simulation of hybrid (biological) models. It can be downloaded free of charge as part of Snoopy from http://www-dssz.informatik.tu-cottbus.de/DSSZ/Software/Snoopy.

FPGA Support in Hybrid Simulation Using Finite Element Method

Hybrid simulation, or hybrid testing, is understood here as a numerical technique for investigating dynamic properties of mechanical systems with components that are difficult to model numerically. This technique can be also referred to as hardware-in-the-loop simulation.The idea of hybrid simulation is that the non-linear part of the system that is difficult to model numerically is tested experimentally and the rest of the system is modeled numerically. Therefore two models are created – the experimental model and the analytical model. During the simulation both models are in strict cooperation in order to act like the undivided system.The following paper focuses on utilizing FPGA (field-programmable gate array) in the computations performed in the analytical model. FPGA is an integrated circuit that is configured by the user. Physically FPGA is an array of logic blocks that the user can program and reconfigure interconnects that will allow to connect logic blocks in the desired way in order to perform complex functions. The most important advantages of using FPGA are flexibility, highly parallel data processing (thanks to simultaneous operation of independent circuits), high efficiency and reliability. Therefore complex digital computations that are implemented to FPGA can be executed with great speed.In the presented research the non-experimental part of the mechanical system is modeled using Finite Element Method (FEM). In order to control the experimental model in a closed loop, the FEM computations must be performed in real time. As FEM is computationally demanding method, the computations must be conducted as efficiently as possible in order to enable real-time without losing accuracy. The following paper describes increasing the time performance of the hybrid simulation algorithm by executing it on the microprocessor of a microcontroller equipped with real-time operating system while performing the most computationally demanding operations on the FPGA.

Interfacing an EMT‐type modular multilevel converter HVDC model in transient stability simulation

This study proposes a hybrid electromechanical and electro-magnetic transient (EMT) simulation algorithm, which interfaces the EMT-type modular multilevel converter (MMC) high-voltage direction current (HVDC) model into transient stability simulation. The proposed hybrid simulation algorithm offers high fidelity simulation results for the fast transient phenomenon of the MMC HVDC using the EMT program (EMTP). At the same time, the hybrid simulation algorithm utilises the transient stability program (TSP) to represent a large external AC network connected to the MMC HVDC. Thus, the proposed hybrid simulator combines the advantages of the TSP and EMTP simulations and offers accurate and efficient solutions for the integrated AC and HVDC systems. A case study of MMC HVDC system connected to 10-generator 39-bus New England AC System is simulated using hybrid and full-EMTP simulations in PSCAD/EMTDC. The simulation results demonstrate the proposed hybrid simulator produces accurate simulation results compared to the full-EMTP solutions.

Hybrid transient simulation platform for interconnected transmission and distribution system based on powerfactory and PSASP

With the development of active distribution system, the interactions between transmission and distribution (T&D) system become more closely and complicated. To study coupled dynamics of the interconnected T&D systems, hybrid simulation is highly required. This study presents a general hybrid simulation algorithm for the interconnected T&D system. A hybrid transient simulation platform is developed based on the proposed algorithm and two commercial software, i.e. PowerFactory and power system analysis software package (PSASP). The distribution system is modelled and analysed in PowerFactory using electromagnetic transient simulation, while the large-scale transmission system executes transient stability simulation in PSASP. The proposed algorithm and the developed platform are verified through a small test case, and are applied to analyse a typical distribution system in Beijing. Results show good coincidence with the fault recording waveform, which validates the availability of the hybrid simulation platform.

Structural Optimization of Solar Driven Energy and Desalination Systems

Planning and dimensioning of desalination plants is usually done by engineers based on an estimation of the capacity of makeup water that is needed for a certain site. Planning a solar- powered system of water and energy supply is complicated and requires a lot of experience from the executing engineer. Optimization methods can support the process of planning complex energy and water supply systems in many ways. Structural optimization is a way to determine an optimized size and configuration for a given task. In this paper a methodology is presented that allows for structural optimization of energy and water supply systems with a focus on a high share of solar energy use. The methodology has been implemented using a software framework that contains functionality for modelling, simulating, optimizing and analyzing energy and water supply systems. Based on load profiles for energy and water as well as technical and economical parameters of the components, a linear optimization is carried out in order to calculate an optimized structure of the system. Furthermore the optimization calculates the capacities of the desalination and energy conversion components and an optimized mode of operation depending on the primary energy prices and solar yield. The methodology uses a MILP algorithm to solve the optimization problem based on linear component models. The linear optimization is coupled with an algebraic equation solver to allow solving of nonlinear equations as well, thus forming a hybrid simulation and optimization algorithm.

Hybrid stochastic simulation of reaction-diffusion systems with slow and fast dynamics.

In this paper, we present a novel hybrid method to simulate discrete stochastic reaction-diffusion models arising in biochemical signaling pathways. We study moderately stiff systems, for which we can partition each reaction or diffusion channel into either a slow or fast subset, based on its propensity. Numerical approaches missing this distinction are often limited with respect to computational run time or approximation quality. We design an approximate scheme that remedies these pitfalls by using a new blending strategy of the well-established inhomogeneous stochastic simulation algorithm and the tau-leaping simulation method. The advantages of our hybrid simulation algorithm are demonstrated on three benchmarking systems, with special focus on approximation accuracy and efficiency.

A hybrid continuum-particle solver for unsteady rarefied gas flows

The implementation of a hybrid simulation algorithm based on the compressible Navier–Stokes equations and the Direct Simulation Monte Carlo (DSMC) method is discussed, with emphasis on problems of transient nature. The coupling of the two methods at their common interface is achieved by the generation of particles according to the local Maxwellian distribution and the imposition of macroscopic quantities for the continuum solver. Dynamic characteristics of the continuum-particle decomposition scheme lead to a flexible and fully configurable determination of the two regions. The parallelization scheme and the ability to consider arbitrary geometries allow the consideration of the method for non-trivial, practical applications. The validation of the code is performed for two benchmark problems, namely those of shock tube flow and unsteady flow through an orifice.

A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients

With the pressing demand for improving patient accessibility, the traditional scheduling system may not be effective for mitigating the adverse effects caused by no-shows, appointment cancellations and late arrivals. For this reason, open access scheduling, which specifies that a portion of clinic appointment slots be reserved for short-notice appointments, was proposed and adopted in recent years. In literature, many studies have developed a variety of approaches and models to optimize the open access scheduling systems, while few considers the inclusion of walk-in patients and the optimal allocation of reserved slots on the scheduling template under the open access configuration. In this paper, we propose a Discrete Event Simulation and Genetic Algorithm (DES–GA) approach to find the heuristic optimal scheduling template under the clinic setting that allows both open access and walk-in patients. The solution can provide scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient. In this approach, the cost is measured by the weighted summation of patient waiting time, provider idle time, and provider overtime. A case study and sensitivity analysis are conducted to show how the heuristic optimal scheduling template generated from the proposed approach could vary under different scenarios. This also illustrates the viability of our model. The results show that the heuristic optimal scheduling templates are significantly affected by the patient attendance rate, level of demands of same-day appointment and walk-in admissions, as well as the cost coefficients associated with patient waiting time, provider idle time and provider overtime.

Modified Full Operator Hybrid Simulation Algorithm and its Application to the Seismic Response Simulation of a Composite Coupled Wall System

A new version of the Full Operator Method (FOM) is proposed in this work. The numerical characteristics of the modified FOM (mFOM) are investigated, both theoretically and analytically. It is found that mFOM is unconditionally stable when the estimated stiffness of the structure is larger than or equal to the actual stiffness. Simulations using two numerical examples are carried out to demonstrate the capability of the mFOM. The seismic response simulation of a composite coupled wall system suggests that the mFOM is capable of generating reasonably accurate solutions despite the presence of structural complexity, material nonlinearity, and displacement control errors.

Efficient simulation under a population genetics model of carcinogenesis

Cancer is well known to be the end result of somatic mutations that disrupt normal cell division. The number of such mutations that have to be accumulated in a cell before cancer develops depends on the type of cancer. The waiting time T(m) until the appearance of m mutations in a cell is thus an important quantity in population genetics models of carcinogenesis. Such models are often difficult to analyze theoretically because of the complex interactions of mutation, drift and selection. They are also computationally expensive to simulate because of the large number of cells and the low mutation rate. We develop an efficient algorithm for simulating the waiting time T(m) until m mutations under a population genetics model of cancer development. We use an exact algorithm to simulate evolution of small cell populations and coarse-grained τ-leaping approximation to handle large populations. We compared our hybrid simulation algorithm with the exact algorithm in small populations and with available asymptotic results for large populations. The comparison suggested that our algorithm is accurate and computationally efficient. We used the algorithm to study the waiting time for up to 20 mutations under a Moran model with variable population sizes. Our new algorithm may be useful for studying realistic models of carcinogenesis, which incorporates variable mutation rates and fitness effects.