Computational models in biology are abstract descriptions of complex biological processes at the molecular, cellular or organism level. These constructs represent our human understanding of how a system of interest might work and are particularly useful for knowledge and data integration, and formulation of hypotheses. By definition, a model has to be a simplified representation of a more complex reality, as the main aspect that combines all sorts of models remains their interpretability.
In Systems Biology and Biomedicine, computational models often come in the form of networks consisting of nodes that represent biological entities, such as genes, proteins, processes, cells and edges, which are used to describe their relationships or transitions. The models can be static diagrams depicting complex biological mechanisms using formalized graphical notation languages (SBGN) or they can be dynamic, including mathematical descriptions of the regulations and an appropriate execution scheme. Static diagrams can be used for knowledge assembly and serve as mechanistic encyclopedias in the form of networks, while dynamic models can be used for in silico simulations, perturbations and predictions.
Computational models in biology and medicine greatly contribute to better comprehending the complexity of life, by processing and assembling a vast amount of high and low throughput data with prior knowledge and empirical expertise. Modelling represents a gateway to get mechanistic insight into regulatory pathways, metabolism, disease onset and progression and drug response.
Depending on data availability and scope, models in systems biology and biomedicine can come in many varieties and be represented by different formalisms, e.g. quantitative versus qualitative, or designed for various purposes (static or dynamic). Finally, models vary in the way they are generated: they can be built from data using machine learning and reverse engineering, manually curating the scientific literature, or automatically generated from public repositories using AI algorithms and text-mining.
In this Research Topic, we aim to highlight current practices in creating or exploiting computational models. We welcome the submission of Original Research and Review articles on the following topics:
Mechanistic models, static and dynamic
? Standards and guidelines for curation and annotation of models
? Public repositories and knowledge bases
? Tools and platforms for assisted curation, data retrieval and integration
? Executable models of biological mechanisms
? Omic data integration
? Use of Systems Biology standards and FAIR principles
? Multiscale and multicellular models
? Challenges and perspectives for tackling a novel pandemic
Statistical models (AI, Machine learning etc)
? Learning models from data
? Data-driven model inference
? Interpretability and explainability in AI models
Computational models in biology are abstract descriptions of complex biological processes at the molecular, cellular or organism level. These constructs represent our human understanding of how a system of interest might work and are particularly useful for knowledge and data integration, and formulation of hypotheses. By definition, a model has to be a simplified representation of a more complex reality, as the main aspect that combines all sorts of models remains their interpretability.
In Systems Biology and Biomedicine, computational models often come in the form of networks consisting of nodes that represent biological entities, such as genes, proteins, processes, cells and edges, which are used to describe their relationships or transitions. The models can be static diagrams depicting complex biological mechanisms using formalized graphical notation languages (SBGN) or they can be dynamic, including mathematical descriptions of the regulations and an appropriate execution scheme. Static diagrams can be used for knowledge assembly and serve as mechanistic encyclopedias in the form of networks, while dynamic models can be used for in silico simulations, perturbations and predictions.
Computational models in biology and medicine greatly contribute to better comprehending the complexity of life, by processing and assembling a vast amount of high and low throughput data with prior knowledge and empirical expertise. Modelling represents a gateway to get mechanistic insight into regulatory pathways, metabolism, disease onset and progression and drug response.
Depending on data availability and scope, models in systems biology and biomedicine can come in many varieties and be represented by different formalisms, e.g. quantitative versus qualitative, or designed for various purposes (static or dynamic). Finally, models vary in the way they are generated: they can be built from data using machine learning and reverse engineering, manually curating the scientific literature, or automatically generated from public repositories using AI algorithms and text-mining.
In this Research Topic, we aim to highlight current practices in creating or exploiting computational models. We welcome the submission of Original Research and Review articles on the following topics:
Mechanistic models, static and dynamic
? Standards and guidelines for curation and annotation of models
? Public repositories and knowledge bases
? Tools and platforms for assisted curation, data retrieval and integration
? Executable models of biological mechanisms
? Omic data integration
? Use of Systems Biology standards and FAIR principles
? Multiscale and multicellular models
? Challenges and perspectives for tackling a novel pandemic
Statistical models (AI, Machine learning etc)
? Learning models from data
? Data-driven model inference
? Interpretability and explainability in AI models