DiSH simulator: Capturing dynamics of cellular signaling with heterogeneous knowledge

Sayed, K. El; Kuo, Yu-Hsin; Kulkarni, Anuva; Miskov-Zivanov, Natasa

doi:10.1109/wsc.2017.8247841

Cited by 16 publications

(31 citation statements)

References 26 publications

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“…The simulator described in Section 4.1 is implemented in Java [5]. We use PRISM [11] as our statistical model checker, which is a C++ tool for formal modeling and analysis of stochastic systems.…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

“…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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“…The model is described in [5], where it was analyzed using the SW simulator described in [6]. In this work, we compared HW and SW simulation of this model using simulation schemes described in [7], where an executable model consists of elements or element groups that are updated simultaneously within the group. Each element has an associated update rule, and elements/groups have a certain probability of their rules being executed in a given simulation step or round.…”

Section: Methodsmentioning

confidence: 99%

A Faster DiSH: Hardware Implementation of a Discrete Cell Signaling Network Simulator

Gilboy

Sayed

Sundaram

et al. 2018

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

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Development of fast methods to conduct in silico experiments using computational models of cellular signaling is a promising approach toward advances in personalized medicine. However, software-based cellular network simulation has runtimes plagued by wasted CPU cycles and unnecessary processes. Hardware-based simulation affords substantial speedup, but prior attempts at hardware-based biological simulation have been limited in scope and have suffered from inaccuracies due to poor random number generation. In this work, we propose several hardware-based simulation schemes utilizing novel random update index generation techniques for step-based and roundbased stochastic simulations of cellular networks. Our results show improved runtimes while maintaining simulation accuracy compared to software implementations.

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“…The models can be analyzed formally or simulated in a deterministic or a stochastic manner. Different from the commonly used simultaneous (synchronous) update schemes [4], we model the stochasticity by applying random-order sequential update scheme [5], which is better suited for studying biological networks.…”

Section: List Of Figuresmentioning

confidence: 99%

Sensitivity Analysis of Discrete Models and Application in Biological Networks

Zhou

Liang

Miskov-Zivanov

2018

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Understanding sensitivity is an important step to study system robustness against perturbations and adaptability to the environment. In this work, we model and investigate intracellular networks via discrete modeling approach, and we propose a framework to study sensitivity in these models. The discrete modeling approach assigns a set of discrete values and an update rule to each model element. The models can be analyzed formally or simulated in a deterministic or a stochastic manner. In our framework, we define element activity and sensitivity with respect to the state distribution of the modeled system. Previous sensitivity analysis approaches assume uniform state distribution, which is usually not true in biology. We perform both static and dynamic sensitivity analysis, the former assuming uniform state distribution, and the latter using a distribution estimated from stochastic simulation trajectories under a particular scenario. Within our sensitivity analysis framework, we first compute element-to-element influences, then we extend the element update functions to include weights according to these computed influences. Adding weights to element interaction rules helps to identify key elements in the model and dominant signaling pathways that determine the behavior of the overall model. When studying cellular signaling networks, we are particularly interested in the response of elements to perturbations, as our goal is often to reach the desired model state via least number of interventions. We have applied our sensitivity analysis framework on pathway extraction and evaluation in the intra-cellular networks that controls T cell differentiation. Additionally, we propose four different ranking algorithms to extract the most iv important pathways from a given source element to a given target element. We then evaluate these four algorithms using cross validation of corresponding extraction results. Our results show that, in different application occasions, different pathway extraction and evaluation algorithms should be adopted to help find "globally valid" or "globally effective" pathways.

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DiSH simulator: Capturing dynamics of cellular signaling with heterogeneous knowledge

Cited by 16 publications

References 26 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

A Faster DiSH: Hardware Implementation of a Discrete Cell Signaling Network Simulator

Sensitivity Analysis of Discrete Models and Application in Biological Networks

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