Interconnected cellular processes visualized as a complex network, with mathematical equations floating amidst.

Decoding Life: How Systems Biology and Ontologies are Revolutionizing Our Understanding of Cells

Theo Raines in Science & Nature June 2025 • 4 min read.

"Explore the cutting-edge research integrating systems biology and bio-ontologies to unlock the secrets of cellular processes and pave the way for whole-cell modeling."

In an era defined by high-throughput technologies churning out massive amounts of biological data, the challenge lies in making sense of it all. Structuring this heterogeneous information and integrating it with existing biological knowledge is a critical task that requires innovative tools and methods. This is where bio-ontologies come in, providing a framework to extract and share valuable insights.

Recent advancements in whole-cell modeling, leveraging systemic cell descriptions, offer new avenues for data integration. Imagine merging a systemic cell description within a bio-ontology—this synergy could significantly accelerate data integration and modeling efforts, leading to a more holistic understanding of cellular functions.

New research introduces BiPON, an ontology that integrates a multi-scale systemic representation of bacterial cellular processes. BiPON’s innovative approach provides a structure for biological information, intertwining biological processes with mathematical models. This novel method is poised to transform how we understand and interact with biological systems.

BiPON: A Multi-Scale Systemic Representation

Interconnected cellular processes visualized as a complex network, with mathematical equations floating amidst.

BiPON consists of two sub-ontologies: bioBiPON and modelBiPON. BioBiPON organizes the systemic description of biological information, while modelBiPON describes the mathematical models (including parameters) associated with biological processes. The relationship between bioBiPON and modelBiPON is established using bridge rules on classes during automatic reasoning, meaning that biological processes are automatically linked to mathematical models.

A significant portion—37%—of BiPON classes are derived from well-established bio-ontologies, while the remainder has been manually defined and curated. Currently, BiPON integrates the main processes involved in bacterial gene expression. Its capacity to formally combine systems biology and bio-ontology marks a significant leap forward, offering both flexibility and generic applicability. This means that most known cellular processes, along with new participants or models, can be readily incorporated into BiPON.

Key features of BiPON:

Integrates multi-scale systemic representation.
Links biological processes to mathematical models.
Offers high flexibility and generic applicability.
Facilitates knowledge integration and sharing.

The applications of BiPON are far-reaching, offering new perspectives for biologists, systems and computational biologists, and the emerging community of whole-cell modeling. This tool can be instrumental in driving advancements in various fields, from basic biological research to biotechnological applications.

The Future of Cellular Understanding

BiPON represents a significant step toward integrating complex biological data and mathematical models, opening doors to new discoveries and applications. As systems biology continues to evolve, tools like BiPON will play a crucial role in unraveling the complexities of cellular life, driving innovation, and ultimately transforming our understanding of health and disease.

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Everything You Need To Know

What is the main goal of systems biology and why is it important in the context of increasing biological data?

Systems biology focuses on understanding biological systems as a whole, rather than just individual components. It involves integrating different levels of information, such as molecular, cellular, and organismal data, to create comprehensive models of how biological systems function. This holistic approach allows researchers to uncover emergent properties and complex interactions that would be missed by studying individual parts in isolation. Systems biology is crucial for making sense of the vast amounts of biological data generated by high-throughput technologies.

What are bio-ontologies, and how do they contribute to advancing our understanding of cells in conjunction with systems biology?

Bio-ontologies provide a structured framework for organizing and standardizing biological knowledge. They use controlled vocabularies and defined relationships to represent biological entities, processes, and functions in a consistent and machine-readable format. By integrating bio-ontologies with systems biology, researchers can link biological data to existing knowledge, enabling more effective data integration, analysis, and modeling. This synergy helps in extracting valuable insights and facilitates knowledge sharing across the scientific community. Without bio-ontologies, biological data would be much harder to interpret and utilize effectively.

What are the key components of BiPON, and how do they work together to represent bacterial cellular processes?

What are the key features of BiPON that make it useful for researchers studying cellular processes?

BiPON integrates multi-scale systemic representations, linking biological processes to mathematical models. It offers high flexibility and generic applicability and facilitates knowledge integration and sharing. A significant portion—37%—of BiPON classes are derived from well-established bio-ontologies, while the remainder has been manually defined and curated. These features help in creating more comprehensive and accurate models of cellular processes.

Why is the link between biological processes and mathematical models, as exemplified by BiPON, significant, and what are its broader implications for fields like biotechnology and medicine?

The creation of BiPON addresses the need to link biological processes directly to mathematical models. This is achieved by structuring biological information through bioBiPON and modelBiPON, then linking these sub-ontologies using bridge rules. This capability is significant because it allows for a more integrated and quantitative understanding of how cellular processes function, enabling researchers to not just describe biological events but also simulate and predict their behavior. The implications extend to biotechnology, drug development, and personalized medicine where precise modeling can accelerate discoveries and optimize treatments.