A new improved and extended version of the multicell bacterial simulatorgro

Abstract

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...

Modeling mechanical interactions in growing populations of rod-shaped bacteria

“…If a cell is sufficiently near, or in contact with, another object (for example another cell or a trap wall) just before this time step, expansion will result in overlap (O). A relaxation algorithm (R) is then asserted that resolves (or prevents) overlaps of all cells and objects using repulsion forces [8], constraint [26], iteration [21], or a related algorithm [16, 22]. EOR methods have thus been used in a wide variety of contexts.…”

“…Current agent-based models of microbial collectives (e.g. [16, 18, 21, 22, 26]) typically do not allow cells to alter their growth rates in direct response to mechanical sensory input. Adding such capability is challenging, due to the complex relationship between cell growth and the extracellular environment.…”

Deriving general conditions and mechanisms for division of labor using the cell-based simulator gro

“…All examples were run with the extended version of the gro simulator 29 . The source code for the simulator may be found at: http://www.lia.upm.es/software/gro/.…”

“…No specific bacterial strain is modelled, as simulations recreate general rules that can be found in most microbial environments. Simulations were performed using the cell-based model gro 28,29 .…”

A framework for implementing metaheuristic algorithms using intercellular communication

“…One approach towards implementing AI using Synthetic Biology is shown in this article in the form of a framework that automates the mapping of MH elements to synthetic constructs. A proof of concept is implemented to show the automation of the process and generation of readily executable simulation files for the cell colony simulator gro 17,18 .…”