THiMED: Time in Hierarchical Model Extraction and Design

Miskov-Zivanov, Natasa; Wei, Peter; Loh, Chang Sheng Clement

doi:10.1007/978-3-319-12982-2_22

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…Next, we use update rules to compute new variable values, that is, new states of all model elements. The simulator we use is publicly available [4,5]. In the simulator, several different simulation schemes were designed to reflect different timing and element update approaches occurring in biological systems.…”

Section: Stochastic Simulationmentioning

confidence: 99%

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Liang

Wang

Telmer

et al. 2017

Computational Methods in Systems Biology

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Abstract. Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-theart biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process.

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Section: Stochastic Simulationmentioning

confidence: 99%

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Liang

Wang

Telmer

et al. 2017

Computational Methods in Systems Biology

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“…In addressing such challenges, literature reveals that, there are few formal approaches has been occupied namely, Boolean Networks (BN) and its extensions (i.e. Qualitative Networks (QN), Gene Regulatory Networks (GRN) [22][23][24], Petri Nets (PR), Cellular Automata (CA), population P systems (PPS), etc. [28].…”

Section: Disease Modelling With Formal Methodsmentioning

confidence: 99%

Modelling Interrelationship between Diseases with Communicating Stream X-Machines

Jayatilake¹,

Phung²,

Ogunshile³

et al. 2022

Proceedings of ISP RAS

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The world is moving towards alternative medicine and behavioural alteration for treating, managing, and preventing chronical diseases. In the last few decades, diagrammatical models have been extensively used to describe and understand the behaviour of biological organisms (biological agents) due to their simplicity and comprehensiveness. However, these models can only offer a static picture of the corresponding biological systems with limited scalability. As a result, there is an increasing demand to integrate formalism into more dynamic forms that can be more scalable and can capture complex time-dependent processes. In this paper, we introduce a generic disease model called Communicating Stream X-Machine Disease Model (CSXMDM), which has been developed based on X-Machine and Communicating X-Machine theories. We conducted an experiment on modelling an actual disease using a case study of Type II Diabetes. The results of the experiment demonstrate that the proposed CSXMDM is capable of modelling chronic diseases.

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“…One such recent example includes automated information extraction, assembly of many model variants, model selection and analysis, with a feedback to guide further information extraction when needed, for the purpose of system understanding, explanation and prediction (Cohen, 2015;Miskov-Zivanov, 2015). Discrete modeling approach has been shown to be advantageous in all these situations (Miskov-Zivanov et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The details of the notation are beyond the scope of this paper. Finally, given that logical models do not represent the time of biological events in the same manner as ODE models, methods exist to incorporate timing into these models (Miskov-Zivanov et al, 2014). Our simulation schemes described in the next section are designed to account for these delay modeling methods.…”

Section: Introductionmentioning

confidence: 99%

DiSH simulator: Capturing dynamics of cellular signaling with heterogeneous knowledge

Sayed

Kuo

Kulkarni

et al. 2017

2017 Winter Simulation Conference (WSC)

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We present DiSH-Sim, a simulator for large discrete models of biological signal transduction pathways, capable of simulating networks with multi-valued elements in both deterministic and stochastic manner. We focus on order of update and thus incorporate information about timing, taking into account that biological processes are not synchronized and certain biochemical changes occur slower than others. Another feature of our simulator is the use of grouped rules to model multi-valued elements and delays. The DiSH-Sim is publicly available and is being used to validate discrete cancer microenvironment and infectious disease models. It is also incorporated within a large architecture that includes natural language processing tools that read biological literature to assemble logical models. This paper demonstrates the functionalities and ease of use of DiSH-Sim, making it a very useful tool for discrete modeling.

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THiMED: Time in Hierarchical Model Extraction and Design

Cited by 8 publications

References 5 publications

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Modelling Interrelationship between Diseases with Communicating Stream X-Machines

DiSH simulator: Capturing dynamics of cellular signaling with heterogeneous knowledge

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