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

Liang, Kai Ming; Wang, Qinsi; Telmer, Cheryl A.; Ravichandran, Divyaa; Spirtes, Peter; Miskov-Zivanov, Natasa

doi:10.1007/978-3-319-67471-1_9

Cited by 9 publications

(34 citation statements)

References 11 publications

(16 reference statements)

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“…As mentioned previously, it is preferable to iteratively validate clusters of additions. In DySE, iterative model validation and extension is integrated with simulation and model checking to enable fast and reliable model selection [12]. One of the methods used for model extension includes combination of Markov Clustering algorithm (MCL) and pathfinding strategies to identify clusters within the network of all available or relevant element interactions.…”

Section: Iterative Model Analysis and Improvementmentioning

confidence: 99%

“…The model checking tools use properties written in a format readable by machines [12]. For example, if an element EL is expected to assume value 1 within the first 2000 time steps, and if it needs to hold this value for at least 3000 steps, then we would write this formally as:…”

Section: L Statistical Model Checkingmentioning

confidence: 99%

“…We study the dynamics of the system through model simulations run with the Discrete Stochastic Heterogeneous, DiSH, simulator [9], and in combination with statistical model checking [10,11]. DySE automatically updates and extends models using several clustering algorithms and model selection methods [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic system explanation

Telmer

Sayed

Butchy

et al. 2019

Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse

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The large amount of knowledge contained in the scientific literature can be mined using natural language processing and utilized to automatically construct models of complex networks in order to obtain a greater understanding of complex systems. In this paper, we describe the Dynamic System Explanation (DySE) framework, which configures hybrid models and executes simulations over time, relying on a granular computing approach and a range of different element update functions. A standardized tabular format assembles the collected knowledge into networks for parameterization. The Discrete Stochastic Heterogeneous (DiSH) simulator outputs trajectories of state changes for all model elements, thus providing a means for running thousands of in silico scenarios in seconds. Trajectories are also analyzed using statistical model checking to verify against known or desired system properties, determined from text or numerical data. DySE can automatically extend models when additional knowledge is available, and model extension is integrated with model checking to test the validity of additional interactions and dynamics, and

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Section: Iterative Model Analysis and Improvementmentioning

confidence: 99%

Section: L Statistical Model Checkingmentioning

confidence: 99%

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Dynamic system explanation

Telmer

Sayed

Butchy

et al. 2019

Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse

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“…In this work, we propose ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a tool that identifies useful and relevant information from published literature, and recommends model modifications that lead to closely recapitulating desired system behavior, all in a fully automated manner. Thus, compared to the work in [18], ACCORDION also considers the dynamic behavior, and in contrast to [16] and [17], it focuses on identifying clusters of strongly connected elements in the newly extracted information, that can have a measurable impact on the dynamic behavior of the model.…”

Section: Introductionmentioning

confidence: 99%

Context-aware knowledge selection and reliable model recommendation with ACCORDION

Ahmed

Telmer

Miskov-Zivanov

2022

Preprint

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Querying new information from knowledge sources, in general, and published literature, in particular, aims to provide precise and quick answers to questions raised about a system under study. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and a tool to enable efficient addressing of biological questions, by automatically recommending models that recapitulate desired dynamic behavior. Our approach integrates information extraction from literature, clustering, simulation and formal analysis to allow for automated, consistent, and robust assembling, testing and selection of context-specific models. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. ACCORDION is comprehensive as it can capture all relevant knowledge from literature, obtained by automated literature search and machine reading. At the same time, as our results show, ACCORDION is selective, recommending only the most relevant and useful subset (15-20%) of candidate model extensions found in literature, while guided by baseline model context and goal properties. ACCORDION is very effective, also demonstrated by our results, as it can reduce the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools. In this process, ACCORDION can also suggest more than one highly scored model, thus providing alternative solutions to user questions and novel insights for treatment directions.

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“…These techniques rely on accurate information, and their performance suffers when the interaction information is inaccurate, incomplete, or from a different biological context. Recently, several other methods have been proposed that combine simulations with formal model analysis (29) to automatically expand, test and select the best model, with respect to a given performance metric. These new tools include the Markov clustering approach (30) and genetic algorithm-based model extension (31).…”

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confidence: 99%

FIDDLE: Efficient Assembly of Networks That Satisfy Desired Behavior

Butchy¹,

Telmer²,

Miskov-Zivanov³

2021

Preprint

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Background: Due to the complexity and redundancy of biological systems, computational models are difficult and laborious to create and update. Therefore, machine reading and automated model assembly are of great interest to computational and systems biologists. Here, we describe FIDDLE (Finding Interactions using Diagram Driven modeL Extension), the tool that we built with the goal to automatically assemble or extend models with the knowledge extracted from published literature. The two main methods developed as part of FIDDLE are called Breadth First Addition (BFA) and Depth First Addition (DFA), and they are based on network search algorithms. To assess the advantages and limitations of BFA and DFA, we applied them on Erdös-Rényi random networks (ER) and Barabási-Albert scale-free networks (BA) as models of biological networks. Starting with several different baseline models and different sets of candidate edges, we conducted a comprehensive evaluation of the BFA and DFA algorithms and their ability to either reconstruct a specified network structure or assemble models that reproduce the same behavior as defined golden models.Results: When BFA and DFA are applied to random networks, we are able to show that model extension with BFA is more specific and DFA is a more sensitive approach. When applied to scale-free networks, both BFA and DFA demonstrated very limited success. Conclusion: Results suggest that BFA is a better method for assembling abundant but mixed true and false information, while DFA will better assemble fewer and more truthful interactions. The limited success with scale-free networks suggests a fundamental difficulty with automatically creating biological models due to the inherent redundancy and complexity of biological networks. These methods represent an early attempt and a novel approach to truly autonomous model creation or extension given different types of knowledge. The source code is available at: https://bitbucket.org/biodesignlab/fiddle/.

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

Cited by 9 publications

References 11 publications

Dynamic system explanation

Dynamic system explanation

Context-aware knowledge selection and reliable model recommendation with ACCORDION

FIDDLE: Efficient Assembly of Networks That Satisfy Desired Behavior

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