Towards the rational design of synthetic cells with prescribed population dynamics

Abstract

The rational design of synthetic cell populations with prescribed behaviours is a long-standing goal of synthetic biology, with the potential to greatly accelerate the development of biotechnological applications in areas ranging from medical research to energy production. Achieving this goal requires well-characterized components, modular implementation strategies, simulation across temporal and spatial scales and automatic compilation of high-level designs to low-level genetic parts that function reliably inside cells. Many of these steps are incomplete or only partially understood, and methods for integrating them within a common design framework have yet to be developed. Here, we address these challenges by developing a prototype framework for designing synthetic cells with prescribed population dynamics. We extend the genetic engineering of cells (GEC) language, originally developed for programming intracellular dynamics, with cell population factors such as cell growth, division and dormancy, together with spatio-temporal simulation methods. As a case study, we use our framework to design synthetic cells with predator–prey interactions that, when simulated, produce complex spatio-temporal behaviours such as travelling waves and spatio-temporal chaos. An analysis of our design reveals that environmental factors such as density-dependent dormancy and reduced extracellular space destabilize the population dynamics and increase the range of genetic variants for which complex spatio-temporal behaviours are possible. Our findings highlight the importance of considering such factors during the design process. We then use our analysis of population dynamics to inform the selection of genetic parts, which could be used to obtain the desired spatio-temporal behaviours. By identifying, integrating and automating key stages of the design process, we provide a computational framework for designing synthetic systems, which could be tested in future laboratory studies.

Co-culture systems and technologies: taking synthetic biology to the next level

“…As population interactions suffer from context dependency and can change in different environments, characterization should include stress limits. For example, an engineered interaction was reported to be functional only within certain pH ranges [39]. Taking context dependency into account does not imply a departure from the engineering and computing analogy on which synthetic biology is based, but rather an improved appreciation of the challenges that come with the analogy.…”

“…Schmidt et al [99] report a workflow for finding unknown interspecies effects in a mixed culture by modelling the co-culture based on data from monocultures, and then using experimental deviations from model predictions to identify unexpected species interactions. Dalchau et al [39] report a model of the experimental system by Balagaddé et al [62] using the genetic engineering of cells language in which important environmental factors for the system are derived [39]. The authors simulated the effects of parameters that can be easily tested in the laboratory, at least at a low-throughput level: effect of pH buffer (can change population behaviour if cell communication molecules are pH-sensitive), effects of different diffusion rates and population starting patterns, making this a useful tool for system design.…”

“…For co-culture studies, this means that the environment not only interacts with the (natural or synthetic) genotype of each cell population to affect behaviours, but the structure of the population environment can fundamentally change the way in which populations interact and whether the interactions are stable [38]. For example, populations may form cooperative relationships under some environmental conditions and not others [3,23] and the behaviour of cell populations may change if cultured in different vessels [39]. This long-established principle in ecology has also recently been experimentally demonstrated in a number of synthetic ecology experiments, both in the wet laboratory [10,22,40] and in silico [41,42].…”

“…The aforementioned desirable characteristics can only be achieved through extensive experimental and theoretical testing and characterization of population behaviours. Such characterization is challenging, as synthetic multi-population systems are some of the most complex systems described to date and currently the best characterization data in synthetic biology is at the level of devices [39].…”

“…As co-culture experiments carried out in different kinds of vessel may not be comparable [10,39], it may be necessary to test systems across several platforms. This highlights the importance of documenting extensive metadata of co-cultures in publications, which is currently not always given, as this is invaluable for process replication and optimization.…”