Dataset Information

A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps.

ABSTRACT: Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity to describe the underlying kinetic processes. We propose a hybrid modeling approach that integrates ordinary differential equations and logical formalism to describe distinct biological layers and their communication. We focused on a multicellular system as a case study by applying the hybrid formalism to the well-known Delta-Notch signaling pathway. We used a differential equation model to describe the intracellular pathways while the cell-cell interactions were defined by logic rules. The hybrid approach herein employed allows us to combine the pros of different modeling techniques by overcoming the lack of quantitative information with a qualitative description that discretizes activation and inhibition processes, thus avoiding complexity.

SUBMITTER: Selvaggio G

PROVIDER: S-EPMC8721169 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps.

Selvaggio Gianluca G Cristellon Serena S Marchetti Luca L

Frontiers in molecular biosciences 20211220

Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity ...[more]

PMID: 34988115

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Project description:BackgroundSystems science approaches like simulation modeling can offer an opportunity for community voice to shape policies. In the episteme of many communities there are elders, leaders, and researchers who are seen as bearers of historic knowledge and can contextualize and interpret contemporary research using knowledge systems of the community. There is a need for a systematic methodology to collaborate with community Knowledge Bearers and Knowledge Interpreters. In this paper we report the results of piloting a systematic methodology for collaborating with a community Knowledge-Bearer and Knowledge-Interpreter to develop a conceptual model revealing the local-level influences and architecture of systems shaping community realities. The use case for this pilot is 'persistent poverty' in the United States, specifically within the inner-city African American community in Baltimore City.MethodsThis pilot of a participatory modeling approach was conducted over a span of 7 sessions and included the following steps, each with an associated script: Step 1: Knowledge-Bearer and Knowledge-Interpreter recruitment Step 2: Relationship building Step 3: Session introduction, Vignette development & enrichment Step 4: Vignette analysis & constructing architecture of systems map Step 5: Augmenting architecture of systems map RESULTS: Each step of the participatory modeling approach resulted in artifacts that were valuable for both the communities and the research effort. Vignette construction resulted in narratives representing a spectrum of lived experiences, trajectories, and outcomes within a community. The collaborative analysis of vignettes yielded the Architecture of Systemic Factors map, that revealed how factors inter-relate to form a system in which lived experience of poverty occurs. A literature search provided an opportunity for the community to contextualize existing research about them using realities of lived experience.ConclusionThis methodology showed that a community Knowledge Bearer can function as communicators and interpreters of their community's knowledge base, can develop coherent narratives of lived experiences within which research and knowledge is contextualized, and can collaboratively construct conceptual mappings necessary for simulation modeling. This participatory modeling approach showed that even if there already exists a vast body of research about a community, collaborating with community gives context to that research and brings together disparate findings within narratives of lived experience.

Project description:In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO2) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO2 solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO2 solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO2 solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO2 and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO2 solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO2 solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO2 solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.

Dataset Information

A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps.

Publications

A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps.

Similar Datasets

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