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Unveiling toxicological adverse outcomes: toward construction and simulation of large-scale networks.

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Title: Unveiling toxicological adverse outcomes: toward construction and simulation of large-scale networks.
Authors: Ikonomi, Nensi1 (AUTHOR) nensi.ikonomi@boehringer-ingelheim.com, Ketter, Natalie1 (AUTHOR), Pepe, Mario A A1 (AUTHOR)
Source: Toxicological Sciences. Apr2026, Vol. 209 Issue 4, p1-9. 8p.
Subjects: Dynamic models, Boolean networks, Poisons, Biological networks, Systems biology, Multiomics
Abstract: Predicting toxicological adverse outcomes is crucial for advancing in silico toxicology strategies. Modern toxicology increasingly relies on systems biology approaches to model and interpret these outcomes. Adverse outcome pathways (AOPs) focus on systems-level descriptions and causal linear relations among initiating, key, and adverse outcome events. Key characteristics (KC)-based topologies capture mechanistic breadth via interconnected property-based modules without assuming linear causality. From another perspective, emerging physiological maps dive deeper into toxicological mechanisms by mapping them at the detailed molecular level. To capture the dynamic nature of toxicological responses, especially their time- and dose-dependent behaviors, there is growing interest in integrating systems biology and mathematical modeling strategies. Although dynamic models have been applied to small-scale AOPs, larger regulatory networks remain largely unexplored from a dynamic perspective. In this review, we highlight recent efforts to combine systems and network biology approaches for predicting toxicological adverse outcomes, covering network construction, analysis, and dynamic predictions. We also explore the aspect of dynamically simulating large-scale molecular networks and its potential contribution to systems toxicology. Specifically, we charter the use of logic-based models (Boolean networks) as an integrative approach to understand molecular crosstalk and cellular phenotypes, highlighting the potential repurpose of existing models. To this end, we show 2 use cases on toxicological applications of Boolean network models. Finally, we prospectively discuss the importance and need of bridging molecular and systemic scales and integrating these modeling strategies with high-dimensional data sources, including omics and multi-omics datasets. [ABSTRACT FROM AUTHOR]
Copyright of Toxicological Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194059241
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Unveiling toxicological adverse outcomes: toward construction and simulation of large-scale networks.
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  Data: <searchLink fieldCode="AR" term="%22Ikonomi%2C+Nensi%22">Ikonomi, Nensi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nensi.ikonomi@boehringer-ingelheim.com</i><br /><searchLink fieldCode="AR" term="%22Ketter%2C+Natalie%22">Ketter, Natalie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pepe%2C+Mario+A+A%22">Pepe, Mario A A</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Toxicological+Sciences%22">Toxicological Sciences</searchLink>. Apr2026, Vol. 209 Issue 4, p1-9. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Dynamic+models%22">Dynamic models</searchLink><br /><searchLink fieldCode="DE" term="%22Boolean+networks%22">Boolean networks</searchLink><br /><searchLink fieldCode="DE" term="%22Poisons%22">Poisons</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+networks%22">Biological networks</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+biology%22">Systems biology</searchLink><br /><searchLink fieldCode="DE" term="%22Multiomics%22">Multiomics</searchLink>
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  Label: Abstract
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  Data: Predicting toxicological adverse outcomes is crucial for advancing in silico toxicology strategies. Modern toxicology increasingly relies on systems biology approaches to model and interpret these outcomes. Adverse outcome pathways (AOPs) focus on systems-level descriptions and causal linear relations among initiating, key, and adverse outcome events. Key characteristics (KC)-based topologies capture mechanistic breadth via interconnected property-based modules without assuming linear causality. From another perspective, emerging physiological maps dive deeper into toxicological mechanisms by mapping them at the detailed molecular level. To capture the dynamic nature of toxicological responses, especially their time- and dose-dependent behaviors, there is growing interest in integrating systems biology and mathematical modeling strategies. Although dynamic models have been applied to small-scale AOPs, larger regulatory networks remain largely unexplored from a dynamic perspective. In this review, we highlight recent efforts to combine systems and network biology approaches for predicting toxicological adverse outcomes, covering network construction, analysis, and dynamic predictions. We also explore the aspect of dynamically simulating large-scale molecular networks and its potential contribution to systems toxicology. Specifically, we charter the use of logic-based models (Boolean networks) as an integrative approach to understand molecular crosstalk and cellular phenotypes, highlighting the potential repurpose of existing models. To this end, we show 2 use cases on toxicological applications of Boolean network models. Finally, we prospectively discuss the importance and need of bridging molecular and systemic scales and integrating these modeling strategies with high-dimensional data sources, including omics and multi-omics datasets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Toxicological Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194059241
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        Value: 10.1093/toxsci/kfag043
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        Text: English
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      – SubjectFull: Poisons
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      – SubjectFull: Multiomics
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              M: 04
              Text: Apr2026
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              Y: 2026
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