gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 10 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.
gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 10 5 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs. KeywordsIndividual based Model, synthetic biology, cell-cell communication, cell shoving algorithm, ecology, digital proteins Synthetic biologists need new computational bioCAD tools to assist them during the design and engineering of new biocircuits. Genetic circuit engineering is moving from single cell devices towards multicellular biocircuits (1 ). Ordinary Di↵erential Equations and Gillespie algorithms are useful tools but mainly for intracellular calculations (2 , 3 ). Computational tools are needed to rapidly predict the dynamical behavior of programmed multicellular microbial communities.Partial di↵erential equations and individual-based models (IbMs) (4 , 5 ) are the most commonly used models to study cell colonies behavior. Partial di↵erential equations aim to reveal the general dynamics of a population as a whole following global equations(6 ). Alternatively, IbMs derive the global dynamics of the population from local interactions rules described for every single individual (i.e., cell). The behavior of the population then emerges as a result of the interaction between these individuals. IbMs are better suited for simulating multicellular systems such as cell colonies or tissues (4 ) as more complex behaviors can be achieved. Because of that, several IbM simulators for cell colonies have been developed. These simulators range from general-purpose and computer-consuming framewo...
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