Synthetic Biology has the ultimate objective of design cells with predictable responses. Our ability to develop modified and synthetic organisms tailored to chemical production is fostered by our ability to recombine DNA with error-free protocols. However, our current capacities for modeling how cells work is way behind our synthesis and analysis tools that difficult the prediction of desired cell responses. Interestingly, computational modeling has impacted prominently Synthetic Biology, where the manipulation of biological systems is cost-intensive, and computational resources could leverage experimental procedures. Traditionally, Ordinary Differential Equations (ODEs) have been employed to model biological systems, but their assumptions are simply not realistic. Particularly, it has been known for a long time that biological processes are stochastic, discrete and structurally complex, hampering differential equations systems to fit these properties. Even if noise is considered, modelers would be making assumptions on how cell components traveling between compartments could affect physically separated processes, how they bind each other, and how they perform behaviors that resemble cooperativity and competition.
To further resolve a connection between modeling and designing organisms, we present a Rule-based model simulated using Gillespie’s Stochastic Simulation Algorithm. Under this approach, rules are macroscopic chemical reactions between entities that recapitulate one or several patterns necessary for a transformation. The rate associated with each rule represents how often a reaction fires in a given time. We modeled two gene regulatory networks of E. coli. These two models resemble the core network that regulates transcription and the replication of the ColEI plasmid. Average and variance of selected variables were analyzed in these examples simulated employing arbitrary rates, yet surprisingly, their properties are in close agreement with experimental data. Specifically, when the core transcription network reached pseudo-equilibrium, it predicts free RNA Polymerase Holoenzyme close to 20%, relatively near the 30% reported during exponentially growing E. coli. Similarly, the plasmid replication controlled with a negative feedback simulated a saturation dynamic, producing tens or hundreds of copies, depending strongly on the rate of interaction between its non-coding RNAs.
We are aware of limitations in our example models. We considered cells in a pseudo-stationary state, therefore disregarding the necessity to model metabolism, translation and protein degradation or dilution. Although, the processes mentioned above could be easily incorporated in successive refinements. Importantly, modeling metabolism and linking it to transcription and translation could facilitate a more reliable prediction of phenotype emergency. To this end, a Gene Regulatory Network (GRN), a Genome-Scale Metabolic Model (GSMM) and (optionally) a protein-protein and an RNA-protein interaction networks will serve as inputs to write draft models. We sought to automatically write a genome-scale model of replication, transcription, translation, RNA and protein degradation joint to metabolism. For instance, we wrote a combined metabolism and gene expression model that resemble the published central metabolism of E. coli (MODEL1505110000) employing the RegulonDB GRNs and the iJO1366 GSMM, resulting in comparable dynamics as the published ODE model.
