Simbiotics is a spatially explicit multiscale modeling platform for the design, simulation and analysis of bacterial populations. Systems ranging from planktonic cells and colonies, to biofilm formation and development may be modeled. Representation of biological systems in Simbiotics is flexible, and user-defined processes may be in a variety of forms depending on desired model abstraction. Simbiotics provides a library of modules such as cell geometries, physical force dynamics, genetic circuits, metabolic pathways, chemical diffusion and cell interactions. Model defined processes are integrated and scheduled for parallel multithread and multi-CPU execution. A virtual lab provides the modeler with analysis modules and some simulated lab equipment, enabling automation of sample interaction and data collection. An extendable and modular framework allows for the platform to be updated as novel models of bacteria are developed, coupled with an intuitive user interface to allow for model definitions with minimal programming experience. Simbiotics can integrate existing standards such as SBML, and process microscopy images to initialize the 3D spatial configuration of bacteria consortia. Two case studies, used to illustrate the platform flexibility, focus on the physical properties of the biosystems modeled. These pilot case studies demonstrate Simbiotics versatility in modeling and analysis of natural systems and as a CAD tool for synthetic biology.
Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.
Autocatalytic sets are self-sustaining and collectively catalytic chemical reaction networks which are believed to have played an important role in the origin of life. Simulation studies have shown that autocatalytic sets are, in principle, evolvable if multiple autocatalytic subsets can exist in different combinations within compartments, i.e., so-called protocells. However, these previous studies have so far not explicitly modeled the emergence and dynamics of autocatalytic sets in populations of compartments in a spatial environment. Here, we use a recently developed software tool to simulate exactly this scenario, as an important first step towards more realistic simulations and experiments on autocatalytic sets in protocells.
JN wrote the manuscript, developed the software and models and performed data analysis. HF supervised the work, designed the overall study, specified the software functionality, and curated the manuscript. YD developed the microscopy image processing module. WM performed experiments for coaggregation study. NJ supervised coaggregation study and curated the manuscript. JM performed experiments for biofilm study and wrote the experimental description. CB supervised the biofilm study. PW curated the manuscript. NK directed and supervised the work, designed the overall study, specified the software functionality, edited the manuscript and secured funding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.