Predicting the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes

For many applications in microbial synthetic biology, optimizing a desired function requires careful tuning of the degree to which various genes are expressed. One challenge for predicting such effects or interpreting typical characterization experiments is that in bacteria such as E. coli, genome copy number varies widely across different phases and rates of growth, which also impacts how and when genes are expressed from different loci. While such phenomena are relatively well-understood at a mechanistic level, our quantitative understanding of such processes is essentially limited to ideal exponential growth. In contrast, common experimental phenomena such as growth on heterogeneous media, metabolic adaptation, and oxygen restriction all cause substantial deviations from ideal exponential growth, particularly as cultures approach the higher densities at which industrial biomanufacturing and even routine screening experiments are conducted. To meet the need for predicting and explaining how gene dosage impacts cellular functions outside of exponential growth, we here report a novel modeling strategy that leverages agent-based simulation and high performance computing to robustly predict the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. We show that by feeding routine experimental data, such as optical density time series, into our heterogeneous multiphasic growth simulator, we can predict genomic DNA distributions over a range of nonexponential growth conditions. This modeling strategy provides an important advance in the ability of synthetic biologists to evaluate the role of genomic DNA content and heterogeneity in affecting the performance of existing or engineered microbial functions

Modelling and simulation of heterogeneous growth dynamics in bacterial populations using a novel multiphasic growth method

The cell cycle is an inevitable source of population heterogeneity, that creates predictable discontinuities. By summarising the canonical understanding of the major steps within the bacterial cell cycle into a mechanistic model, the Cooper-Helmstetter model is able to formally describe a number of population properties such as age, DNA and volume distributions. Although this model successfully describes many different attributes of a bacterial population, it is limited to exponential growth conditions. Outside of rigorous growth environments, bacterial populations contain innate temporal features that make them di cult to formalise theoretically using traditional mechanistic or equation based mathematical models. To model bacterial population cell cycle outside of exponential growth, the single cell cycle mechanistic model was inspected and expanded. A new individual based model was developed and a novel method to track the growth of a population using measured optical density data alone was developed. Together these new features made for the Heterogeneous Multiphasic Growth simulator, and were used to explore the chromosomal DNA dynamics of bacterial populations in disparate growth regimes. The effects of the recA1 mutation on the dynamics of the cell cycle was examined through optimisation to measured data. Furthermore, predictive modelling of theoretical effects of gene copy number and partition noise on synthetic genetic constructs expressed as ordinary differential equations were explored theoretically. By explicitly simulating each member of a population using such a method, a wide range of different aspects of bacterial population may be approached theoretically with more ease, and throughout more diverse growth dynamic

Retrorules: a database of reaction rules for engineering biology

RetroRules is a database of reaction rules for metabolic engineering (https://retrorules.org). Reaction rules are generic descriptions of chemical reactions that can be used in retrosynthesis workflows in order to enumerate all possible biosynthetic routes connecting a target molecule to its precursors. The use of such rules is becoming increasingly important in the context of synthetic biology applied to de novo pathway discovery and in systems biology to discover underground metabolism due to enzyme promiscuity. Here, we provide for the first time a complete set containing >400 000 stereochemistry-aware reaction rules extracted from public databases and expressed in the community-standard SMARTS (SMIRKS) format, augmented by a rule representation at different levels of specificity (the atomic environment around the reaction center). Such numerous representations of reactions expand natural chemical diversity by predicting de novo reactions of promiscuous enzymes

Predicting the Dynamics and Heterogeneity of Genomic DNA Content within Bacterial Populations across Variable Growth Regimes

For many applications in microbial synthetic biology, optimizing a desired function requires careful tuning of the degree to which various genes are expressed. One challenge for predicting such effects or interpreting typical characterization experiments is that in bacteria such as <i>E. coli</i>, genome copy number varies widely across different phases and rates of growth, which also impacts how and when genes are expressed from different loci. While such phenomena are relatively well-understood at a mechanistic level, our quantitative understanding of such processes is essentially limited to ideal exponential growth. In contrast, common experimental phenomena such as growth on heterogeneous media, metabolic adaptation, and oxygen restriction all cause substantial deviations from ideal exponential growth, particularly as cultures approach the higher densities at which industrial biomanufacturing and even routine screening experiments are conducted. To meet the need for predicting and explaining how gene dosage impacts cellular functions outside of exponential growth, we here report a novel modeling strategy that leverages agent-based simulation and high performance computing to robustly predict the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. We show that by feeding routine experimental data, such as optical density time series, into our heterogeneous multiphasic growth simulator, we can predict genomic DNA distributions over a range of nonexponential growth conditions. This modeling strategy provides an important advance in the ability of synthetic biologists to evaluate the role of genomic DNA content and heterogeneity in affecting the performance of existing or engineered microbial functions

Galaxy-SynBioCAD: tools and automated pipelines for Synthetic Biology Design and Metabolic Engineering

International audienc

Galaxy-SynBioCAD: tools and automated pipelines for Synthetic Biology Design and Metabolic Engineering

International audienceThere’s a substantial number of tools released around Synthetic Biology and Metabolic Engineering- related questions and needs. This population of tools is difficult to comprehend and use together, main reasons being complexity and interoperability issues. Indeed, a high level of expertise could be required for installing codes, and execution for real life use cases could be computationally resource demanding. Plus some tools, although complementary, use different inputs and outputs which prevent easy chaining. The SynBioCAD-Galaxy portal [1] is a growing toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enable one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process: from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Tools are made available on GitHub, anaconda.org and the Galaxy Tool Shed, opening to the greatest number access and utilization throughout the SynBio community, and significant effort has been granted for adopting FAIR principles. As a community effort helped by funded projects, the scope covered by tools is expected to expand over time.The poster will give an overview of the SynBioCAD-Galaxy portal in the context of prediction and construction of E. coli lycopene-producing pathways. The poster will open the discussion around good practices guiding releases of tools through continuous integration. A – lightweight – testing instance of SynBioCAD-Galaxy is available at https://galaxysynbiocad.org

Galaxy-SynBioCAD: tools and automated pipelines for Synthetic Biology Design and Metabolic Engineering

International audienc

The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering

International audienceHere we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows

Galaxy-SynBioCAD: tools and automated pipelines for Synthetic Biology Design and Metabolic Engineering

There’s a substantial number of tools released around Synthetic Biology and Metabolic Engineering- related questions and needs. This population of tools is difficult to comprehend and use together, main reasons being complexity and interoperability issues. Indeed, a high level of expertise could be required for installing codes, and execution for real life use cases could be computationally resource demanding. Plus some tools, although complementary, use different inputs and outputs which prevent easy chaining. The SynBioCAD-Galaxy portal [1] is a growing toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enable one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process: from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Tools are made available on GitHub, anaconda.org and the Galaxy Tool Shed, opening to the greatest number access and utilization throughout the SynBio community, and significant effort has been granted for adopting FAIR principles. As a community effort helped by funded projects, the scope covered by tools is expected to expand over time.The poster will give an overview of the SynBioCAD-Galaxy portal in the context of prediction and construction of E. coli lycopene-producing pathways. The poster will open the discussion around good practices guiding releases of tools through continuous integration. A – lightweight – testing instance of SynBioCAD-Galaxy is available at https://galaxysynbiocad.org