Role of communication network topology in controlling microbial population in consortia
Xinying Ren, Richard M. Murray
AI summary
95% confidenceInternational MES research in biotechnology with focus on biotechnology, iot-smart-systems, performance. Abstract Engineering microbial consortia is an important new frontier for synthetic biol...
Generated by MESSAI extraction pipeline · review against source PDF
Reported parameters
No values extracted from this paper yet.
No 3D model is mapped to this paper yet. Parameter ranges above still place reported values on the literature distribution.
Abstract
Abstract Engineering microbial consortia is an important new frontier for synthetic biology given its efficiency in performing complex tasks and endurance to environmental uncertainty. Most synthetic circuits regulate populational behaviors via cell-to-cell communications, which are affected by spatially heterogenous environments. Therefore, it is important to understand the limits on controlling system dynamics that are determined by interconnections among cell agents and provide a control strategy for engineering consortia. Here, we build a network model for a fractional population control circuit in two-strain consortia, and characterize the cell-to-cell communication network by topological properties, such as symmetry, locality and connectivity. Using linear network control theory, we relate the network topology to system output’s tracking performance. We analytically and numerically demonstrate that the minimum network control energy for accurate tracking depends on locality difference between two cell population’s spatial distributions and how strongly the controller node contributes to communication strength. To realize a robust consortia, we can manipulate the communication network topology and construct strongly connected consortia by altering chemicals in environments. Our results ground the expected cell population dynamics in its spatially organized communication network, and inspire directions in cooperative control in microbial consortia.
Keywords
Identifiers
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Year
- 2018