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Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law.

ABSTRACT: Synthetic biology seeks to envision living cells as a matter of engineering. However, increasing evidence suggests that the genetic load imposed by the incorporation of synthetic devices in a living organism introduces a sort of unpredictability in the design process. As a result, individual part characterization is not enough to predict the behavior of designed circuits and thus, a costly trial-error process is eventually required. In this work, we provide a new theoretical framework for the predictive treatment of the genetic load. We mathematically and experimentally demonstrate that dependences among genes follow a quantitatively predictable behavior. Our theory predicts the observed reduction of the expression of a given synthetic gene when an extra genetic load is introduced in the circuit. The theory also explains that such dependence qualitatively differs when the extra load is added either by transcriptional or translational modifications. We finally show that the limitation of the cellular resources for gene expression leads to a mathematical formulation that converges to an expression analogous to the Ohm's law for electric circuits. Similitudes and divergences with this law are outlined. Our work provides a suitable framework with predictive character for the design process of complex genetic devices in synthetic biology.

SUBMITTER: Carbonell-Ballestero M

PROVIDER: S-EPMC4705654 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law.

Carbonell-Ballestero M M Garcia-Ramallo E E Montañez R R Rodriguez-Caso C C Macía J J

Nucleic acids research 20151209 1

Synthetic biology seeks to envision living cells as a matter of engineering. However, increasing evidence suggests that the genetic load imposed by the incorporation of synthetic devices in a living organism introduces a sort of unpredictability in the design process. As a result, individual part characterization is not enough to predict the behavior of designed circuits and thus, a costly trial-error process is eventually required. In this work, we provide a new theoretical framework for the pr ...[more]

PMID: 26656950

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Project description:For practical construction of complex synthetic genetic networks able to perform elaborate functions it is important to have a pool of relatively simple modules with different functionality which can be compounded together. To complement engineering of very different existing synthetic genetic devices such as switches, oscillators or logical gates, we propose and develop here a design of synthetic multi-input classifier based on a recently introduced distributed classifier concept. A heterogeneous population of cells acts as a single classifier, whose output is obtained by summarizing the outputs of individual cells. The learning ability is achieved by pruning the population, instead of tuning parameters of an individual cell. The present paper is focused on evaluating two possible schemes of multi-input gene classifier circuits. We demonstrate their suitability for implementing a multi-input distributed classifier capable of separating data which are inseparable for single-input classifiers, and characterize performance of the classifiers by analytical and numerical results. The simpler scheme implements a linear classifier in a single cell and is targeted at separable classification problems with simple class borders. A hard learning strategy is used to train a distributed classifier by removing from the population any cell answering incorrectly to at least one training example. The other scheme implements a circuit with a bell-shaped response in a single cell to allow potentially arbitrary shape of the classification border in the input space of a distributed classifier. Inseparable classification problems are addressed using soft learning strategy, characterized by probabilistic decision to keep or discard a cell at each training iteration. We expect that our classifier design contributes to the development of robust and predictable synthetic biosensors, which have the potential to affect applications in a lot of fields, including that of medicine and industry.

Project description:Intracellular spatial heterogeneity is frequently observed in bacteria, where the chromosome occupies part of the cell's volume and a circuit's DNA often localizes within the cell. How this heterogeneity affects core processes and genetic circuits is still poorly understood. In fact, commonly used ordinary differential equation (ODE) models of genetic circuits assume a well-mixed ensemble of molecules and, as such, do not capture spatial aspects. Reaction-diffusion partial differential equation (PDE) models have been only occasionally used since they are difficult to integrate and do not provide mechanistic understanding of the effects of spatial heterogeneity. In this paper, we derive a reduced ODE model that captures spatial effects, yet has the same dimension as commonly used well-mixed models. In particular, the only difference with respect to a well-mixed ODE model is that the association rate constant of binding reactions is multiplied by a coefficient, which we refer to as the binding correction factor (BCF). The BCF depends on the size of interacting molecules and on their location when fixed in space and it is equal to unity in a well-mixed ODE model. The BCF can be used to investigate how spatial heterogeneity affects the behavior of core processes and genetic circuits. Specifically, our reduced model indicates that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome. The extent of these effects depends on the value of the BCF, which we found to be close to unity. For translation, the value of the BCF is always greater than unity, it increases with mRNA size, and, with biologically relevant parameters, is substantially larger than unity. Our model has broad validity, has the same dimension as a well-mixed model, yet it incorporates spatial heterogeneity. This simple-to-use model can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects.

Project description:Background:Coral reefs are colored by eukaryotic chromoproteins (CPs) that are homologous to green fluorescent protein. CPs differ from fluorescent proteins (FPs) by intensely absorbing visible light to give strong colors in ambient light. This endows CPs with certain advantages over FPs, such as instrument-free detection uncomplicated by ultra-violet light damage or background fluorescence, efficient Förster resonance energy transfer (FRET) quenching, and photoacoustic imaging. Thus, CPs have found utility as genetic markers and in teaching, and are attractive for potential cell biosensor applications in the field. Most near-term applications of CPs require expression in a different domain of life: bacteria. However, it is unclear which of the eukaryotic CP genes might be suitable and how best to assay them. Results:Here, taking advantage of codon optimization programs in 12 cases, we engineered 14 CP sequences (meffRed, eforRed, asPink, spisPink, scOrange, fwYellow, amilGFP, amajLime, cjBlue, meffBlue, aeBlue, amilCP, tsPurple and gfasPurple) into a palette of Escherichia coli BioBrick plasmids. BioBricks comply with synthetic biology's most widely used, simplified, cloning standard. Differences in color intensities, maturation times and fitness costs of expression were compared under the same conditions, and visible readout of gene expression was quantitated. A surprisingly large variation in cellular fitness costs was found, resulting in loss of color in some overnight liquid cultures of certain high-copy-plasmid-borne CPs, and cautioning the use of multiple CPs as markers in competition assays. We solved these two problems by integrating pairs of these genes into the chromosome and by engineering versions of the same CP with very different colors. Conclusion:Availability of 14 engineered CP genes compared in E. coli, together with chromosomal mutants suitable for competition assays, should simplify and expand CP study and applications. There was no single plasmid-borne CP that combined all of the most desirable features of intense color, fast maturation and low fitness cost, so this study should help direct future engineering efforts.

Dataset Information

Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law.

Publications

Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law.

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