Introduction

The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) representation format was introduced to facilitate seamless human-machine interaction when creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is a tabular format most suitable for models that have a directed graph as their underlying structure.

Networks such as the one in the figure below (part A) can be represented with the BioRECIPE format. When creating interaction lists or models in the BioRECIPE format, information and data can be obtained from different sources, and input into BioRECIPE tables or spreadsheets automatically or manually (part B, left). Interaction lists and models written in the BioRECIPE format are convenient for different types of analysis and use (part B, right), either with automated tools, or manually, when human input is needed. (ER-endoplasmic reticulum)

The BioRECIPE format can be used to represent:

• event-based lists of interactions

• element-based models

Examples

A few examples of interaction lists are provided in the table below. In these examples, each row corresponds to one interaction and columns correspond to various interaction attributes. The details of all interaction attributes used in interaction lists can be found here,

Table 1 Example interaction lists in the BioRECIPE format

Description	BioRECIPE representation
75K interactions from 6K papers obtained through INDRA	download
138K interactions from 6K papers obtained with REACH	download
small interaction list example obtained from INDRA	download
small interaction list example obtained from REACH	download

A few examples of executable models are provided in the table below. In these examples, each row corresponds to one model element and columns correspond to various element, regulation, and context attributes. The details for these attributes used to represent executable models can be found here.

Table 2 Example models in the BioRECIPE format

System	BioRECIPE representation	Publication
T cell	download	Tcell_SciSignal_2013
pancreatic cancer cell	download	AI4SynBio_2021
colon cancer cell	download	Cancer Immunology Research 2020

BioRECIPE features and use

The tables below provide the description of BioRECIPE features and types of models that can be represented with BioRECIPE, model analysis that can be conducted on these models, and the descriptions of how BioRECIPE satisfies the FAIR principles.

Table 3 BioRECIPE features

Feature	Description
Standardization	The documentation is available, and definitions provided to standardize the representation of biological entities and events, ensuring consistency and interoperability across different tools and studies.
Expressiveness	Captures a wide range of biological phenomena, from molecular interactions to cellular processes.
Scalability	Represents systems of varying complexity, from simple pathways to whole-cell models.
Extensibility	Additional attributes, new types of data and relationships can be added as our understanding of biological systems evolves.
Machine-readable	Can be processed by computer programs, facilitating automated analysis and simulation.
Human-readable	The tabular spreadsheet format and the underlying graph structure are readable, visualizable, and understandable by humans.
Support for quantitative data	Incorporates a range of quantitative parameters essential for dynamic simulations.
Annotations and metadata	It allows for rich annotations and metadata, linking model attributes to biological databases and literature, which is crucial for model validation and reuse.
Compatibility	Interoperable with a range of tools and platforms, enabling seamless data exchange and integration.
Facilitates collaboration	Provides a common format for computational modelers and biologists, enabling collaboration and sharing of models.
Version control and evolution	The documentation and translators are open access and available on ReadtheDocs and GitHub enabling version control and updating.

Table 4 BioRECIPE use - Modeling

Modeling aspect	Description
Hybrid models	Elements within the same model can have different types of update functions, e.g., Boolean, discrete, qualitative or weighted sums.
Granular computing	Complex entities that emerge through data abstraction and the process of extracting knowledge from information or data is referred to as information granules. These entities are clustered based on factors such as similarity, functional or physical proximity, indistinguishability, and coherence.
Networks	Representation of models that have an underlying network (graph) structure, including both undirected or directed graphs.
Dynamics	Simulation parameters are included in the format and are used to setup scenarios and initialize and run simulations.

Table 5 BioRECIPE use - Analysis

Analysis type	Description
Simulation	It is compatible with the DiSH simulator which is a versatile simulator with deterministic and stochastic simulation schemes.
Explanations,Understanding, Interpretation, Predictions, Interventions, Discovery, Decisions, Reasoning, Optimization	As it is readable by both machines and humans, and it includes the metadata, the models and interactions can be explored both manually and automatically. The information about interactions and metadata is available in a standardized format, and the compatibility with the simulator and a suite of other tools that provide insights into the behavior of all model elements under a range of scenarios, altogether (a) improve understanding of the model and the modeled system and allow for (b) explanations and interpretations of observed behaviors, (c) predictions of future behaviors, as well as (d) explorations of interventions informing decisions, (e) leading to discovery and (f) guiding and optimizing future experiments.

Table 6 BioRECIPE use - Knowledge

FAIR aspect	Description
Curation	Compatibility with tools that filter information from literature, verify and validate models enables automated curation.
Reproducibility	Detailed annotation and metadata in models enable reproducibility of both experimental data and model results.
Findability	All related files are provided on GitHub, making the code and the examples easy to find by both humans and computers. All components of the format are defined and described in detail in the documentation on ReadtheDocs.
Accessibility	All relevant files are open access.
Interoperability	The format and the included translators facilitate interoperability of models with a range of tools or workflows for analysis, storage, and processing.
Reusability	The detailed documentation enables reuse of published data through standardized models and replication of model results within same or different settings.