Toward Engineering Biosystems With Emergent Collective Functions

Area of Science:

• Synthetic Biology
• Systems Biology
• Computational Biology

Background:

• Complex biological functions emerge from interacting populations of molecules or cells.
• Engineering these emergent collective phenomena is challenging due to multi-level feedback across scales.
• Bioengineers are interested in collective phenomena for their robustness and scalability.

Purpose of the Study:

• To present multi-agent modeling as a design framework for synthetic biology.
• To demonstrate how multi-agent modeling can address challenges in engineering emergent collective functions.
• To explore the potential of multi-agent modeling for creating multi-scale biological systems.

Main Methods:

• Utilizing multi-agent modeling as a design framework.
• Applying the framework to case studies in synthetic ecologies, biological computation, and synthetic cellularity.
• Analyzing complex multi-scale systems to identify underlying mechanisms.

Main Results:

• Multi-agent modeling captures core features of complex multi-scale biological systems.
• The approach provides insights into mechanisms guiding emergent functionalities across scales.
• Identified design rules for engineering collective behaviors.

Conclusions:

• Multi-agent modeling offers a pathway to overcome challenges in engineering emergent collective functions.
• This framework facilitates the design of synthetic biology systems beyond single components.
• Enables the creation of systems with functions emerging from multi-scale collectives.