Engineering stochasticity in gene expression

Principles for the design of multicellular engineered living systems

Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell-cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the "black box" of living cells.

Likelihood for transcriptions in a genetic regulatory system under asymmetric stable Lévy noise

This work is devoted to investigating the evolution of concentration in a genetic regulation system, when the synthesis reaction rate is under additive and multiplicative asymmetric stable Lévy fluctuations. By focusing on the impact of skewness (i.e., non-symmetry) in the probability distributions of noise, we find that via examining the mean first exit time (MFET) and the first escape probability (FEP), the asymmetric fluctuations, interacting with nonlinearity in the system, lead to peculiar likelihood for transcription. This includes, in the additive noise case, realizing higher likelihood of transcription for larger positive skewness (i.e., asymmetry) index β, causing a stochastic bifurcation at the non-Gaussianity index value α = 1 (i.e., it is a separating point or line for the likelihood for transcription), and achieving a turning point at the threshold value β≈-0.5 (i.e., beyond which the likelihood for transcription suddenly reversed for α values). The stochastic bifurcation and turning point phenomena do not occur in the symmetric noise case (β = 0). While in the multiplicative noise case, non-Gaussianity index value α = 1 is a separating point or line for both the MFET and the FEP. We also investigate the noise enhanced stability phenomenon. Additionally, we are able to specify the regions in the whole parameter space for the asymmetric noise, in which we attain desired likelihood for transcription. We have conducted a series of numerical experiments in "regulating" the likelihood of gene transcription by tuning asymmetric stable Lévy noise indexes. This work offers insights for possible ways of achieving gene regulation in experimental research.

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuit's behavior. Here, we create a library of genetic activation cascades in E. coli bacteria, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to nonregulatory interactions among genes that reshape the circuit's behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site and DNA copy number to restore the cascade's intended monotonically increasing response. Our results demonstrate how unintended circuit's behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.

A simple comparison of the extrinsic noise in gene expression between native and foreign regulations in Escherichia coli

Living cells reorganize their gene expression through regulatory machineries in response to external perturbations. The contribution of the regulation to the noise in gene expression is of great interest. In this study, we evaluate the contribution of both native and foreign regulations to the extrinsic noise in gene expression. We analyzed the gene expression data of a mini-library containing 70 genetic constructs of 136 clones into which the gfp gene had been chromosomally incorporated under the control of either native or foreign regulation. We found that the substitution of native by foreign regulation, i.e., the insertion of the P_tet promoter, triggered a decrease in the extrinsic noise, which was independent of the protein abundance. The reanalyses of varied genomic data sets verified that the noisy gene expression mediated by native regulations is a common feature, regardless of the diversity in the genetic approaches used. Disturbing native regulations by a synthetic promoter reduced the extrinsic noise in gene expression in Escherichia coli. It indicated that the extrinsic noise in gene expression caused by the native regulation could be further repressed. These results suggest a tendency of released regulation leading to reduced noise and a linkage between noise and plasticity in the regulation of gene expression.

Robust Synthetic Circuits for Two-Dimensional Control of Gene Expression in Yeast

Cellular phenotypes are the result of complex interactions between many components. Understanding and predicting the system level properties of the resulting networks requires the development of perturbation tools that can simultaneously and independently modulate multiple cellular variables. Here, we develop synthetic modules that use different arrangements of two transcriptional regulators to achieve either concurrent and independent control of the expression of two genes, or decoupled control of the mean and variance of a single gene. These modules constitute powerful tools to probe the quantitative attributes of network wiring and function.

How to train your microbe: methods for dynamically characterizing gene networks

Gene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways.

Comparative genomic and phylogenetic analyses of Gammaproteobacterial glg genes traced the origin of the Escherichia coli glycogen glgBXCAP operon to the last common ancestor of the sister orders Enterobacteriales and Pasteurellales

Production of branched α-glucan, glycogen-like polymers is widely spread in the Bacteria domain. The glycogen pathway of synthesis and degradation has been fairly well characterized in the model enterobacterial species Escherichia coli (order Enterobacteriales, class Gammaproteobacteria), in which the cognate genes (branching enzyme glgB, debranching enzyme glgX, ADP-glucose pyrophosphorylase glgC, glycogen synthase glgA, and glycogen phosphorylase glgP) are clustered in a glgBXCAP operon arrangement. However, the evolutionary origin of this particular arrangement and of its constituent genes is unknown. Here, by using 265 complete gammaproteobacterial genomes we have carried out a comparative analysis of the presence, copy number and arrangement of glg genes in all lineages of the Gammaproteobacteria. These analyses revealed large variations in glg gene presence, copy number and arrangements among different gammaproteobacterial lineages. However, the glgBXCAP arrangement was remarkably conserved in all glg-possessing species of the orders Enterobacteriales and Pasteurellales (the E/P group). Subsequent phylogenetic analyses of glg genes present in the Gammaproteobacteria and in other main bacterial groups indicated that glg genes have undergone a complex evolutionary history in which horizontal gene transfer may have played an important role. These analyses also revealed that the E/P glgBXCAP genes (a) share a common evolutionary origin, (b) were vertically transmitted within the E/P group, and (c) are closely related to glg genes of some phylogenetically distant betaproteobacterial species. The overall data allowed tracing the origin of the E. coli glgBXCAP operon to the last common ancestor of the E/P group, and also to uncover a likely glgBXCAP transfer event from the E/P group to particular lineages of the Betaproteobacteria.

Strategy of tuning gene expression ratio in prokaryotic cell from perspective of noise and correlation

Genes are organized into operons in procaryote, and these genes in one operon generally have related functions. However, genes in the same operon are usually not equally expressed, and the ratio needs to be fine-tuned for specific functions. We examine the difference of gene expression noise and correlation when tuning the expression level at the transcriptional or translational level in a bicistronic operon driven by a constitutive or a two-state promoter. We get analytic results for the noise and correlation of gene expression levels, which is confirmed by our stochastic simulations. Both the noise and the correlation of gene expressions in an operon with a two-state promoter are higher than in an operon with a constitutive promoter. Premature termination of mRNA induced by transcription terminator in the intergenic region or changing translation rates can tune the protein ratio at the transcriptional level or at the translational level. We find that gene expression correlation between promoter-proximal and promoter-distal genes at the protein level decreases as termination increases. In contrast, changing translation rates in the normal range almost does not alter the correlation. This explains why the translation rate is a key factor of modulating gene expressions in an operon. Our results can be useful to understand the relationship between the operon structure and the biological function of a gene network, and also may help in synthetic biology design.

Optogenetic characterization methods overcome key challenges in synthetic and systems biology

Systems biologists aim to understand how organism-level processes, such as differentiation and multicellular development, are encoded in DNA. Conversely, synthetic biologists aim to program systems-level biological processes, such as engineered tissue growth, by writing artificial DNA sequences. To achieve their goals, these groups have adapted a hierarchical electrical engineering framework that can be applied in the forward direction to design complex biological systems or in the reverse direction to analyze evolved networks. Despite much progress, this framework has been limited by an inability to directly and dynamically characterize biological components in the varied contexts of living cells. Recently, two optogenetic methods for programming custom gene expression and protein localization signals have been developed and used to reveal fundamentally new information about biological components that respond to those signals. This basic dynamic characterization approach will be a major enabling technology in synthetic and systems biology.

A 'resource allocator' for transcription based on a highly fragmented T7 RNA polymerase

Synthetic genetic systems share resources with the host, including machinery for transcription and translation. Phage RNA polymerases (RNAPs) decouple transcription from the host and generate high expression. However, they can exhibit toxicity and lack accessory proteins (σ factors and activators) that enable switching between different promoters and modulation of activity. Here, we show that T7 RNAP (883 amino acids) can be divided into four fragments that have to be co-expressed to function. The DNA-binding loop is encoded in a C-terminal 285-aa ‘σ fragment’, and fragments with different specificity can direct the remaining 601-aa ‘core fragment’ to different promoters. Using these parts, we have built a resource allocator that sets the core fragment concentration, which is then shared by multiple σ fragments. Adjusting the concentration of the core fragment sets the maximum transcriptional capacity available to a synthetic system. Further, positive and negative regulation is implemented using a 67-aa N-terminal ‘α fragment’ and a null (inactivated) σ fragment, respectively. The α fragment can be fused to recombinant proteins to make promoters responsive to their levels. These parts provide a toolbox to allocate transcriptional resources via different schemes, which we demonstrate by building a system which adjusts promoter activity to compensate for the difference in copy number of two plasmids.

Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.

Promoter element arising from the fusion of standard BioBrick parts

We characterize the appearance of a constitutive promoter element in the commonly used cI repressor-encoding BioBrick BBa_C0051. We have termed this promoter element pKAT. Full pKAT activity is created by the ordered assembly of sequences in BBa_C0051 downstream of the cI gene encoding the 11 amino acid LVA proteolytic degradation tag, a BioBrick standard double-TAA stop codon, a genetic barcode, and part of the RFC10 SpeI-XbaI BioBrick scar. Placing BBa_C0051 or other pKAT containing parts upstream of other functional RNA coding elements in a polycistronic context may therefore lead to the unintended transcription of the downstream elements. The frequent reuse of pKAT or pKAT-like containing basic parts in the Registry of Biological Parts has resulted in approximately 5% of registry parts encoding at least one instance of a predicted pKAT promoter located directly upstream of a ribosome binding site and ATG start codon. This example highlights that even seemingly simple modifications of a part's sequence (in this case addition of degradation tags and barcodes) may be sufficient to unexpectedly change the contextual behavior of a part and reaffirms the inherent challenge in carefully characterizing the behavior of standardized biological parts across a broad range of reasonable use scenarios.

Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant

A new experimental approach reveals a bet-hedging strategy in unstressed, clonal yeast cells, whereby they adopt a range of growth states that correlate with expression of a trehalose-synthesis regulator and predict resistance to future stress.

Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits. A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system and how much serves a biological function. In bacteria, stochastic gene expression results in cell-to-cell heterogeneity that might serve as a bet-hedging mechanism, allowing a few cells to survive through an antimicrobial treatment while others perish. Despite its clinical importance, the molecular mechanisms underlying bet hedging remain unclear. Here, we investigate the mechanisms of bet hedging in Saccharomyces cerevisiae using a new high-throughput microscopy assay that monitors variable protein expression, morphology, growth rate, and survival outcomes of tens of thousands of yeast microcolonies simultaneously. We find that clonal populations display broad distributions of growth rates and that slow growth predicts resistance to heat killing in a probabalistic manner. We identify several gene products that are likely to play a role in bet hedging and confirm that Tsl1, a trehalose-synthesis regulator, is an important component of this resistance. Tsl1 abundance correlates with growth rate and replicative age and predicts survival. Our results suggest that yeast bet hedging results from multiple epigenetic growth states determined by a combination of stochastic and deterministic factors.

Genetically identical cells grown in the same environment can display heterogeneity in their morphology, behavior, and composition of their cellular components. In some microorganisms, such cellular heterogeneity can underlie a phenomenon known as bet hedging because it enables some cells to survive in harsh environments, hence increasing the overall population fitness when environmental shifts are unpredictable. Bet hedging is likely to be an important strategy by which microbes infect humans and evade antimicrobial treatments, yet little is known of how cellular heterogeneity contributes to microbial survival. Here, we study the mechanisms underlying bet hedging in yeast. We find that populations of genetically identical yeast contain a broad distribution of growth rates and that slow growth predicts resistance to heat killing in a graded fashion. We identify several gene products that are likely to play a role in this bet-hedging strategy and confirm that Tsl1, a regulator of the production of the disaccharide trehalose, is an important component of acute stress resistance. Finally, we find that old age in cells correlates with a Tsl1-abundant, stress-resistant cell state. Our results suggest that trehalose synthesis is part of a complex and multifactorial mechanism that underlies bet hedging in yeast.

How mathematical modelling elucidates signalling in Bacillus subtilis

Appropriate stimulus perception, signal processing and transduction ensure optimal adaptation of bacteria to environmental challenges. In the Gram-positive model bacterium Bacillus subtilis signalling networks and molecular interactions therein are well-studied, making this species a suitable candidate for the application of mathematical modelling. Here, we review systems biology approaches, focusing on chemotaxis, sporulation, σ(B) -dependent general stress response and competence. Processes like chemotaxis and Z-ring assembly depend critically on the subcellular localization of proteins. Environmental response strategies, including sporulation and competence, are characterized by phenotypic heterogeneity in isogenic cultures. The examples of mathematical modelling also include investigations that have demonstrated how operon structure and signalling dynamics are intricately interwoven to establish optimal responses. Our review illustrates that these interdisciplinary approaches offer new insights into the response of B. subtilis to environmental challenges. These case studies reveal modelling as a tool to increase the understanding of complex systems, to help formulating hypotheses and to guide the design of more directed experiments that test predictions.

Using synthetic biology to understand the evolution of gene expression

The evolution of phenotype is often based on changes in gene expression rather than changes in protein-coding sequence. Gene expression is controlled by complex networks of interacting regulators that act through a variety of biochemical mechanisms. Perturbation of these networks can have profound effects on the fitness of organisms. This highlights an important challenge: the investigation of whether the mechanisms and network architectures we observe in Nature evolved in response to selective pressure--and, if so, what that pressure might have been--or whether the architectures are a result of non-adaptive forces. Synthetic biologists aim to construct artificial genetic and biological systems to increase our understanding of Nature as well as for a number of biotechnological applications. In this review, I will highlight how engineering 'synthetic' control of gene expression provides a way to test evolutionary hypotheses. Synthetic biology might allow us to investigate experimentally the evolutionary paths not taken by extant organisms.

Multichromatic control of gene expression in Escherichia coli

Light is a powerful tool for manipulating living cells because it can be applied with high resolution across space and over time. We previously constructed a red light-sensitive Escherichia coli transcription system based on a chimera between the red/far-red switchable cyanobacterial phytochrome Cph1 and the E. coli EnvZ/OmpR two-component signaling pathway. Here, we report the development of a green light-inducible transcription system in E. coli based on a recently discovered green/red photoswitchable two-component system from cyanobacteria. We demonstrate that the transcriptional output is proportional to the intensity of green light applied and that the green sensor is orthogonal to the red sensor at intensities of 532-nm light less than 0.01 W/m(2). Expression of both sensors in a single cell allows two-color optical control of transcription both in batch culture and in patterns across a lawn of engineered cells. Because each sensor functions as a photoreversible switch, this system should allow the spatial and temporal control of the expression of multiple genes through different combinations of light wavelengths. This feature aids precision single-cell and population-level studies in systems and synthetic biology.

Economy of operon formation: cotranscription minimizes shortfall in protein complexes

Genes of prokaryotes and Archaea are often organized in cotranscribed groups, or operons. In contrast, eukaryotic genes are generally transcribed independently. Here we show that there is a substantial economic gain for the cell to cotranscribe genes encoding protein complexes because it synchronizes the fluctuations, or noise, in the levels of the different components. This correlation substantially reduces the shortfall in production of the complex. This benefit is relatively large in small cells such as bacterial cells, in which there are few mRNAs and proteins per cell, and is diminished in larger cells such as eukaryotic cells.

Tuning and controlling gene expression noise in synthetic gene networks

Synthetic gene networks can be used to control gene expression and cellular phenotypes in a variety of applications. In many instances, however, such networks can behave unreliably due to gene expression noise. Accordingly, there is a need to develop systematic means to tune gene expression noise, so that it can be suppressed in some cases and harnessed in others, e.g. in cellular differentiation to create population-wide heterogeneity. Here, we present a method for controlling noise in synthetic eukaryotic gene expression systems, utilizing reduction of noise levels by TATA box mutations and noise propagation in transcriptional cascades. Specifically, we introduce TATA box mutations into promoters driving TetR expression and show that these mutations can be used to effectively tune the noise of a target gene while decoupling it from the mean, with negligible effects on the dynamic range and basal expression. We apply mathematical and computational modeling to explain the experimentally observed effects of TATA box mutations. This work, which highlights some important aspects of noise propagation in gene regulatory cascades, has practical implications for implementing gene expression control in synthetic gene networks.

A synthetic genetic edge detection program

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

Synthetic control of a fitness tradeoff in yeast nitrogen metabolism

BACKGROUND

Microbial communities are involved in many processes relevant to industrial and medical biotechnology, such as the formation of biofilms, lignocellulosic degradation, and hydrogen production. The manipulation of synthetic and natural microbial communities and their underlying ecological parameters, such as fitness, evolvability, and variation, is an increasingly important area of research for synthetic biology.

RESULTS

Here, we explored how synthetic control of an endogenous circuit can be used to regulate a tradeoff between fitness in resource abundant and resource limited environments in a population of Saccharomyces cerevisiae. We found that noise in the expression of a key enzyme in ammonia assimilation, Gdh1p, mediated a tradeoff between growth in low nitrogen environments and stress resistance in high ammonia environments. We implemented synthetic control of an endogenous Gdh1p regulatory network to construct an engineered strain in which the fitness of the population was tunable in response to an exogenously-added small molecule across a range of ammonia environments.

CONCLUSION

The ability to tune fitness and biological tradeoffs will be important components of future efforts to engineer microbial communities.

Toward scalable parts families for predictable design of biological circuits

Our current ability to engineer biological circuits is hindered by design cycles that are costly in terms of time and money, with constructs failing to operate as desired, or evolving away from the desired function once deployed. Synthetic biologists seek to understand biological design principles and use them to create technologies that increase the efficiency of the genetic engineering design cycle. Central to the approach is the creation of biological parts--encapsulated functions that can be composited together to create new pathways with predictable behaviors. We define five desirable characteristics of biological parts--independence, reliability, tunability, orthogonality and composability, and review studies of small natural and synthetic biological circuits that provide insights into each of these characteristics. We propose that the creation of appropriate sets of families of parts with these properties is a prerequisite for efficient, predictable engineering of new function in cells and will enable a large increase in the sophistication of genetic engineering applications.