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1.  Dilution and the theoretical description of growth-rate dependent gene expression 
Expression of a gene is not only tuned by direct regulation, but also affected by the global physiological state of the (host) cell. This global dependence complicates the quantitative understanding of gene regulation and the design of synthetic gene circuits. In bacteria these global effects can often be described as a dependence on the growth rate. Here we discuss how growth-rate dependence can be incorporated in mathematical models of gene expression by comparing data for unregulated genes with the predictions of different theoretical descriptions of growth-rate dependence. We argue that a realistic description of growth effects requires a growth-rate dependent protein synthesis rate in addition to dilution by growth.
doi:10.1186/1754-1611-7-22
PMCID: PMC3847955  PMID: 24041253
Genetic circuits; Modeling; Bacterial growth; Dilution; Growth-rate dependence
2.  Dissecting specific and global transcriptional regulation of bacterial gene expression 
An experimental-computational approach is applied to dissect the contribution of specific transcription factor-mediated versus global growth-dependent regulation to bacterial gene expression, and obtain a quantitative understanding of dynamic adaptations in arginine biosynthesis of E. coli.
We present a model-based approach to quantitatively dissect simultaneous contributions from specific transcription factors and the global growth status to bacterial gene expression, based on parameter inference from GFP-based promoter activity measurements.We show that growth rate can be used to predict the unregulated expression baseline of a gene, since growth rate dependence of global regulation occurs both in steady state and during transient changes in growth rate.We obtain a quantitative understanding of both specific and global regulation in arginine biosynthesis, as demonstrated by accurate model-based predictions of complex transient gene-expression responses to simultaneous perturbation in growth rate and arginine availability.We uncover two principles of joint regulation of the arginine biosynthesis pathway: (i) specific regulation by repression dominates in steady metabolic states and (ii) global regulation sets the maximal expression reachable during transition between steady metabolic states.
Gene expression is regulated by specific transcriptional circuits but also by the global expression machinery as a function of growth. Simultaneous specific and global regulation thus constitutes an additional—but often neglected—layer of complexity in gene expression. Here, we develop an experimental-computational approach to dissect specific and global regulation in the bacterium Escherichia coli. By using fluorescent promoter reporters, we show that global regulation is growth rate dependent not only during steady state but also during dynamic changes in growth rate and can be quantified through two promoter-specific parameters. By applying our approach to arginine biosynthesis, we obtain a quantitative understanding of both specific and global regulation that allows accurate prediction of the temporal response to simultaneous perturbations in arginine availability and growth rate. We thereby uncover two principles of joint regulation: (i) specific regulation by repression dominates the transcriptional response during metabolic steady states, largely repressing the biosynthesis genes even when biosynthesis is required and (ii) global regulation sets the maximum promoter activity that is exploited during the transition between steady states.
doi:10.1038/msb.2013.14
PMCID: PMC3658269  PMID: 23591774
expression machinery; modelling; synthetic biology; transcriptional circuit; transcriptional regulation
3.  Growth-Rate Dependence Reveals Design Principles of Plasmid Copy Number Control 
PLoS ONE  2011;6(5):e20403.
Genetic circuits in bacteria are intimately coupled to the cellular growth rate as many parameters of gene expression are growth-rate dependent. Growth-rate dependence can be particularly pronounced for genes on plasmids; therefore the native regulatory systems of a plasmid such as its replication control system are characterized by growth-rate dependent parameters and regulator concentrations. This natural growth-rate dependent variation of regulator concentrations can be used for a quantitative analysis of the design of such regulatory systems. Here we analyze the growth-rate dependence of parameters of the copy number control system of ColE1-type plasmids in E. coli. This analysis allows us to infer the form of the control function and suggests that the Rom protein increases the sensitivity of control.
doi:10.1371/journal.pone.0020403
PMCID: PMC3103578  PMID: 21647376
4.  Accurate prediction of gene feedback circuit behavior from component properties 
A basic assumption underlying synthetic biology is that analysis of genetic circuit elements, such as regulatory proteins and promoters, can be used to understand and predict the behavior of circuits containing those elements. To test this assumption, we used time-lapse fluorescence microscopy to quantitatively analyze two autoregulatory negative feedback circuits. By measuring the gene regulation functions of the corresponding repressor–promoter interactions, we accurately predicted the expression level of the autoregulatory feedback loops, in molecular units. This demonstration that quantitative characterization of regulatory elements can predict the behavior of genetic circuits supports a fundamental requirement of synthetic biology.
doi:10.1038/msb4100185
PMCID: PMC2132446  PMID: 18004276
auto-regulation; feedback; GRF; quantification; regulatory elements
5.  Emergent bistability by a growth-modulating positive feedback circuit 
Nature chemical biology  2009;5(11):842-848.
A synthetic gene circuit is often engineered by considering the host cell as an invariable “chassis”. Circuit activation, however, may modulate host physiology, which in turn can drastically impact circuit behavior. We illustrate this point by a simple circuit consisting of mutant T7 RNA polymerase (T7 RNAP*) that activates its own expression in bacterium Escherichia coli. Although activation by the T7 RNAP* is noncooperative, the circuit caused bistable gene expression. This counterintuitive observation can be explained by growth retardation caused by circuit activation, which resulted in nonlinear dilution of T7 RNAP* in individual bacteria. Predictions made by models accounting for such effects were verified by further experimental measurements. Our results reveal a novel mechanism of generating bistability and underscore the need to account for host physiology modulation when engineering gene circuits.
doi:10.1038/nchembio.218
PMCID: PMC2908482  PMID: 19801994
bistability; host physiology; positive feedback; synthetic biology
6.  A modular positive feedback-based gene amplifier 
Background
Positive feedback is a common mechanism used in the regulation of many gene circuits as it can amplify the response to inducers and also generate binary outputs and hysteresis. In the context of electrical circuit design, positive feedback is often considered in the design of amplifiers. Similar approaches, therefore, may be used for the design of amplifiers in synthetic gene circuits with applications, for example, in cell-based sensors.
Results
We developed a modular positive feedback circuit that can function as a genetic signal amplifier, heightening the sensitivity to inducer signals as well as increasing maximum expression levels without the need for an external cofactor. The design utilizes a constitutively active, autoinducer-independent variant of the quorum-sensing regulator LuxR. We experimentally tested the ability of the positive feedback module to separately amplify the output of a one-component tetracycline sensor and a two-component aspartate sensor. In each case, the positive feedback module amplified the response to the respective inducers, both with regards to the dynamic range and sensitivity.
Conclusions
The advantage of our design is that the actual feedback mechanism depends only on a single gene and does not require any other modulation. Furthermore, this circuit can amplify any transcriptional signal, not just one encoded within the circuit or tuned by an external inducer. As our design is modular, it can potentially be used as a component in the design of more complex synthetic gene circuits.
doi:10.1186/1754-1611-4-4
PMCID: PMC2845093  PMID: 20187959
7.  Molecular crowding shapes gene expression in synthetic cellular nanosystems 
Nature nanotechnology  2013;8(8):602-608.
Summary
The integration of synthetic and cell-free biology has made tremendous strides towards creating artificial cellular nanosystems using concepts from solution-based chemistry: only the concentrations of reacting species modulate gene expression rates. However, it is known that macromolecular crowding, a key feature of natural cells, can dramatically influence biochemical kinetics by volume exclusion effects that reduce diffusion rates and enhance binding rates of macromolecules. Here, we demonstrate that macromolecular crowding can increase the robustness of gene expression through integrating synthetic cellular components of biological circuits and artificial cellular nanosystems. In addition, we reveal how ubiquitous cellular modules, including genetic components, a negative feedback loop, and the size of crowding molecules, can fine tune gene circuit response to molecular crowding. By bridging a key gap between artificial and living cells, our work has implications for efficient and robust control of both synthetic and natural cellular circuits.
doi:10.1038/nnano.2013.132
PMCID: PMC3951305  PMID: 23851358
molecular crowding; synthetic biology; gene regulation; artificial cells; robustness
8.  Synthetic gene circuits for metabolic control: design trade-offs and constraints 
A grand challenge in synthetic biology is to push the design of biomolecular circuits from purely genetic constructs towards systems that interface different levels of the cellular machinery, including signalling networks and metabolic pathways. In this paper, we focus on a genetic circuit for feedback regulation of unbranched metabolic pathways. The objective of this feedback system is to dampen the effect of flux perturbations caused by changes in cellular demands or by engineered pathways consuming metabolic intermediates. We consider a mathematical model for a control circuit with an operon architecture, whereby the expression of all pathway enzymes is transcriptionally repressed by the metabolic product. We address the existence and stability of the steady state, the dynamic response of the network under perturbations, and their dependence on common tuneable knobs such as the promoter characteristic and ribosome binding site (RBS) strengths. Our analysis reveals trade-offs between the steady state of the enzymes and the intermediates, together with a separation principle between promoter and RBS design. We show that enzymatic saturation imposes limits on the parameter design space, which must be satisfied to prevent metabolite accumulation and guarantee the stability of the network. The use of promoters with a broad dynamic range and a small leaky expression enlarges the design space. Simulation results with realistic parameter values also suggest that the control circuit can effectively upregulate enzyme production to compensate flux perturbations.
doi:10.1098/rsif.2012.0671
PMCID: PMC3565798  PMID: 23054953
metabolic control; operon regulation; feedback control design
9.  Growth rate regulation in Escherichia coli 
FEMS microbiology reviews  2011;36(2):269-287.
Growth rate regulation in bacteria has been an important issue in bacterial physiology for the past 50 years. This review, using Escherichia coli as a paradigm, summarizes the mechanisms for the regulation of rRNA synthesis in the context of systems biology, particularly, in the context of genome-wide competition for limited RNA polymerase (RNAP) in the cell under different growth conditions including nutrient starvation. The specific location of the seven rrn operons in the chromosome and the unique properties of the rrn promoters contribute to growth rate regulation. The length of the rrn transcripts, coupled with gene dosage effects, influence the distribution of RNAP on the chromosome in response to growth rate. Regulation of rRNA synthesis depends on multiple factors that affect the structure of the nucleoid and the allocation of RNAP for global gene expression. The magic spot ppGpp, which acts with DksA synergistically, is a key effector in both the growth rate regulation and the stringent response induced by nutrient starvation, mainly because the ppGpp level changes in response to environmental cues. It regulates rRNA synthesis via a cascade of events including both transcription initiation and elongation, and can be explained by an RNAP redistribution (allocation) model.
doi:10.1111/j.1574-6976.2011.00279.x
PMCID: PMC3478676  PMID: 21569058
growth rate regulation; rRNA synthesis; RNA polymerase distribution; transcription factories; nucleolus-like structure; ppGpp
10.  Coding-sequence determinants of gene expression in Escherichia coli 
Science (New York, N.Y.)  2009;324(5924):255-258.
Synonymous mutations do not alter the encoded protein, but they can influence gene expression. To investigate the mechanisms, we engineered a synthetic library of 154 genes that vary randomly at synonymous sites, but all encode the same green fluorescent protein. When expressed in E. coli, GFP protein levels varied 250-fold across the library. GFP mRNA levels, mRNA degradation patterns, and bacterial growth rates also varied, but codon bias did not correlate with gene expression. Rather, the stability of mRNA folding near the ribosomal binding site explained over half the variation in protein levels. In our analysis, mRNA folding and associated rates of translation initiation play a predominant role in shaping expression levels of individual genes, whereas codon bias influences global translation efficiency and cellular fitness.
doi:10.1126/science.1170160
PMCID: PMC3902468  PMID: 19359587
11.  Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template 
Variable gene dosage is a major source of fluctuations in gene expression in both endogenous and synthetic circuits. Synthetic incoherent feedforward regulatory motifs using RNA interference are shown to robustly adapt to changes in DNA template amounts in mammalian cells.
Variable gene dosage is a major source of fluctuations in gene product levels in both endogenous and synthetic circuits.To mitigate gene expression variability, we designed, simulated, constructed, and tested regulatory circuits, implementing an incoherent feedforward motif.A number of control mechanisms including transcription and post-transcriptional regulation were tested in mammalian cells.Feedforward regulation displayed better adaptation than negative feedback, and circuits based on RNA interference were the most robust to variation in DNA template amounts.
Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Indeed, changes in gene dosage are clearly a major source of variation in cells, and yet those changes are very common in both normal processes (sex determination, ploidy change) and disease (gene amplification in cancer). In synthetic networks, the problem is exacerbated due to commonly used transient delivery methods that result in very large cell-to-cell variability in gene dosage. The basic question on gene dosage compensation in nature (Veitia et al, 2008; Acar et al, 2010) and a practical challenge of overcoming sensitivity to DNA copy number in synthetic circuits prompted us to investigate mechanisms to reduce this variability using sophisticated internal regulatory mechanisms. Indeed, the baseline expression unit in many synthetic circuits is an open-loop promoter-ORF combination. We hypothesized that some sort of internal regulation will result in ‘expression units' whose gene product (i.e. protein) output will depend only mildly on the intracellular concentration of its DNA template. In other words, we searched for architecture that would lead to ‘adaptation' of the gene product to the amount of DNA template.
By examining large body of published work, we found frequent reference to a so-called ‘incoherent feedforward' network motif (Mangan and Alon, 2003). The canonical three-node incoherent loop contains input, auxiliary regulator, and output nodes. The output is controlled directly by the input and the auxiliary regulator. The latter is also controlled by the input, introducing an additional indirect effect of the input on the output. In incoherent loops, the input controls the auxiliary regulator node in such a way that input's overall indirect action on the output via this node counteracts its direct effect. In a motif named ‘type I incoherent feedforward loop' (I1-FFL), the input's direct effect is activating, as is its control of the auxiliary node, while the auxiliary node controls the output through repression. One of the most studied properties of such motifs is their transient response to persistent stimulus, that is visually characterized as a ‘bump' or ‘pulse' (hence the name ‘pulse generator') that then goes back to the original steady state of the system (Basu et al, 2004). We hypothesized that changing DNA amount could serve as an input to an incoherent circuit if the auxiliary regulator and the output nodes are coexpressed from this DNA; in other words DNA can be viewed as an ‘activator' of both the regulator and the output. We conjectured that this might lead to adaptation to changes in DNA template.
We designed and simulated in silico a number of network architectures that all exhibit incoherent feedforward connectivity. We also compared them with the well-studied feedback loop circuit that in theory weakens but does not eliminate gene product dependency on the DNA template amount. The schematics of the circuits are shown in Figure 1.
Experimental measurement of input–output response of these circuits, or their transfer function, indeed uncovered adaptation of the output to DNA template abundance. Such adaptation has not been observed with feedback loop, as expected. Among various architectures, the post-transcriptional circuits showed faster adaptation, higher absolute expression levels and lower ‘noise' (Figure 4).
We also simulated and measured stochastic variability in the circuits by collecting all the cells with similar input values and statistically analyzing output values in those cells. We found that substantial noise component could not be accounted for by known noise sources, and concluded that the very step of negative regulation, both by a repressor LacI and by a microRNA, significantly increases cell-to-cell variability. This needs to be addressed in further studies. We also found that the negative feedback loop did not result in reduced noise as we expected, yet it did not result in noise increase as in the incoherent motif. This means that there may be effective noise reduction but it is not sufficient to produce narrow distributions of outputs for a given input.
We conclude that expression units that incorporate incoherent feedforward control of the gene product provide adaptation to the amount of DNA template and can be superior to simple combinations of constitutive promoters with ORFs. We also emphasize the relevance of our findings to the long-standing question of gene dosage compensation in cells, and note that similar incoherent architectures with microRNA negative regulators have been found in cells, suggesting that their physiological role is to curb variability in gene dosage and/or promoter strength.
Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Gene product levels could potentially be decoupled from these changes via built-in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post-transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post-transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability.
doi:10.1038/msb.2011.49
PMCID: PMC3202791  PMID: 21811230
feedforward motifs; gene dosage and noise; mammalian cells; microRNAs; negative autoregulation
12.  Transient Growth Arrest in Escherichia coli Induced by Chromosome Condensation 
PLoS ONE  2013;8(12):e84027.
MukB is a bacterial SMC (structural maintenance of chromosome) protein that regulates the global folding of the Escherichia coli chromosome by bringing distant DNA segments together. We report that moderate overproduction of MukB may lead, depending on strain and growth conditions, to transient growth arrest. In DH5α cells, overproduction of MukB or MukBEF using pBAD expression system triggered growth arrest 2.5 h after induction. The exit from growth arrest was accompanied by the loss of the overproducing plasmid and a decline in the abundance of MukBEF. The arrested cells showed a compound gene expression profile which can be characterized by the following features: (i) a broad and deep downregulation of ribosomal proteins (up to 80-fold); (ii) downregulation of groups of genes encoding enzymes involved in nucleotide metabolism, respiration, and central metabolism; (iii) upregulation of some of the genes responsive to general stress; and (iv) degradation of the patterns of spatial correlations in the transcriptional activity of the chromosome. The transcriptional state of the MukB induced arrest is most similar to stationary cells and cells recovered from stationary phase into a nutrient deprived medium, to amino acid starved cells and to the cells shifting from glucose to acetate. The mukB++ state is dissimilar from all examined transcriptional states generated by protein overexpression with the possible exception of RpoE and RpoH overexpression. Thus, the transcription profile of MukB-arrested cells can be described as a combination of responses typical for other growth-arrested cells and those for overproducers of DNA binding proteins with a particularly deep down-regulation of ribosomal genes.
doi:10.1371/journal.pone.0084027
PMCID: PMC3871593  PMID: 24376785
13.  Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study 
PLoS Computational Biology  2011;7(3):e1002010.
Stochastic expression of genes produces heterogeneity in clonal populations of bacteria under identical conditions. We analyze and compare the behavior of the inducible lac genetic switch using well-stirred and spatially resolved simulations for Escherichia coli cells modeled under fast and slow-growth conditions. Our new kinetic model describing the switching of the lac operon from one phenotype to the other incorporates parameters obtained from recently published in vivo single-molecule fluorescence experiments along with in vitro rate constants. For the well-stirred system, investigation of the intrinsic noise in the circuit as a function of the inducer concentration and in the presence/absence of the feedback mechanism reveals that the noise peaks near the switching threshold. Applying maximum likelihood estimation, we show that the analytic two-state model of gene expression can be used to extract stochastic rates from the simulation data. The simulations also provide mRNA–protein probability landscapes, which demonstrate that switching is the result of crossing both mRNA and protein thresholds. Using cryoelectron tomography of an E. coli cell and data from proteomics studies, we construct spatial in vivo models of cells and quantify the noise contributions and effects on repressor rebinding due to cell structure and crowding in the cytoplasm. Compared to systems without spatial heterogeneity, the model for the fast-growth cells predicts a slight decrease in the overall noise and an increase in the repressors rebinding rate due to anomalous subdiffusion. The tomograms for E. coli grown under slow-growth conditions identify the positions of the ribosomes and the condensed nucleoid. The smaller slow-growth cells have increased mRNA localization and a larger internal inducer concentration, leading to a significant decrease in the lifetime of the repressor–operator complex and an increase in the frequency of transcriptional bursts.
Author Summary
Expressing genes in a bacterial cell is noisy and random. A colony of bacteria grown from a single cell can show remarkable differences in the copy number per cell of a given protein after only a few generations. In this work we use computer simulations to study the variation in how individual cells in a population express a set of genes in response to an environmental signal. The modeled system is the lac genetic switch that Escherichia coli uses to find, collect, and process lactose sugar from the environment. The noise inherent in the genetic circuit controlling the cell's response determines how similar the cells are to each other and we study how the different components of the circuit affect this noise. Furthermore, an estimated 30–50% of the cell volume is taken up by a wide variety of large biomolecules. To study the response of the circuit caused by crowding, we simulate the circuit inside a three-dimensional model of an E. coli cell built using data from cryoelectron tomography reconstructions of a single cell and proteomics data. Correctly including random effects of molecular crowding will be critical to developing fully dynamic models of living cells.
doi:10.1371/journal.pcbi.1002010
PMCID: PMC3053318  PMID: 21423716
14.  Hidden hysteresis – population dynamics can obscure gene network dynamics 
Background
Positive feedback is a common motif in gene regulatory networks. It can be used in synthetic networks as an amplifier to increase the level of gene expression, as well as a nonlinear module to create bistable gene networks that display hysteresis in response to a given stimulus. Using a synthetic positive feedback-based tetracycline sensor in E. coli, we show that the population dynamics of a cell culture has a profound effect on the observed hysteretic response of a population of cells with this synthetic gene circuit.
Results
The amount of observable hysteresis in a cell culture harboring the gene circuit depended on the initial concentration of cells within the culture. The magnitude of the hysteresis observed was inversely related to the dilution procedure used to inoculate the subcultures; the higher the dilution of the cell culture, lower was the observed hysteresis of that culture at steady state. Although the behavior of the gene circuit in individual cells did not change significantly in the different subcultures, the proportion of cells exhibiting high levels of steady-state gene expression did change.
Although the interrelated kinetics of gene expression and cell growth are unpredictable at first sight, we were able to resolve the surprising dilution-dependent hysteresis as a result of two interrelated phenomena - the stochastic switching between the ON and OFF phenotypes that led to the cumulative failure of the gene circuit over time, and the nonlinear, logistic growth of the cell in the batch culture.
Conclusions
These findings reinforce the fact that population dynamics cannot be ignored in analyzing the dynamics of gene networks. Indeed population dynamics may play a significant role in the manifestation of bistability and hysteresis, and is an important consideration when designing synthetic gene circuits intended for long-term application.
doi:10.1186/1754-1611-7-16
PMCID: PMC3700772  PMID: 23800122
15.  Nonlinear Fitness Landscape of a Molecular Pathway 
PLoS Genetics  2011;7(7):e1002160.
Genes are regulated because their expression involves a fitness cost to the organism. The production of proteins by transcription and translation is a well-known cost factor, but the enzymatic activity of the proteins produced can also reduce fitness, depending on the internal state and the environment of the cell. Here, we map the fitness costs of a key metabolic network, the lactose utilization pathway in Escherichia coli. We measure the growth of several regulatory lac operon mutants in different environments inducing expression of the lac genes. We find a strikingly nonlinear fitness landscape, which depends on the production rate and on the activity rate of the lac proteins. A simple fitness model of the lac pathway, based on elementary biophysical processes, predicts the growth rate of all observed strains. The nonlinearity of fitness is explained by a feedback loop: production and activity of the lac proteins reduce growth, but growth also affects the density of these molecules. This nonlinearity has important consequences for molecular function and evolution. It generates a cliff in the fitness landscape, beyond which populations cannot maintain growth. In viable populations, there is an expression barrier of the lac genes, which cannot be exceeded in any stationary growth process. Furthermore, the nonlinearity determines how the fitness of operon mutants depends on the inducer environment. We argue that fitness nonlinearities, expression barriers, and gene–environment interactions are generic features of fitness landscapes for metabolic pathways, and we discuss their implications for the evolution of regulation.
Author Summary
The levels of protein produced by an organism are likely to change its fitness, potentially driving the evolution of genetic regulation. Importantly, protein expression generates costs as well as benefits. Here, we use a model genetic system, the lac operon of Escherichia coli, to investigate different sources of fitness costs. We find that fitness depends not only on the production rate of proteins but also on their enzymatic activity. A simple quantitative model, which is based on the biophysics of protein production and activity, accurately reproduces the experimental results and provides testable predictions. The model describes a feedback cycle between a molecular pathway and the growth rate of cells: pathway activity impedes growth, but growth itself affects the pathway. This feedback can generate dramatic effects, such as gene expression barriers, fitness cliffs, and population extinctions, which can be triggered by small environmental or genetic changes. Our results disentangle the complex interplay of protein production and activity, and they show how these processes shape the evolution of simple organisms.
doi:10.1371/journal.pgen.1002160
PMCID: PMC3140986  PMID: 21814515
16.  A Programmable Escherichia coli Consortium via Tunable Symbiosis 
PLoS ONE  2012;7(3):e34032.
Synthetic microbial consortia that can mimic natural systems have the potential to become a powerful biotechnology for various applications. One highly desirable feature of these consortia is that they can be precisely regulated. In this work we designed a programmable, symbiotic circuit that enables continuous tuning of the growth rate and composition of a synthetic consortium. We implemented our general design through the cross-feeding of tryptophan and tyrosine by two E. coli auxotrophs. By regulating the expression of genes related to the export or production of these amino acids, we were able to tune the metabolite exchanges and achieve a wide range of growth rates and strain ratios. In addition, by inverting the relationship of growth/ratio vs. inducer concentrations, we were able to “program” the co-culture for pre-specified attributes with the proper addition of inducing chemicals. This programmable proof-of-concept circuit or its variants can be applied to more complex systems where precise tuning of the consortium would facilitate the optimization of specific objectives, such as increasing the overall efficiency of microbial production of biofuels or pharmaceuticals.
doi:10.1371/journal.pone.0034032
PMCID: PMC3316586  PMID: 22479509
17.  Non-optimal microbial response to antibiotics underlies suppressive drug interactions 
Cell  2009;139(4):707-718.
SUMMARY
Antibiotics inhibiting translation can increase bacterial growth rate in the presence of DNA synthesis inhibitors. Here, we show that this extreme type of drug antagonism, termed suppression, results from non-optimal regulation of ribosomal genes, leading to sub-maximal growth in the presence of DNA stress. Using GFP-tagged transcription reporters in Escherichia coli, we find that ribosomal genes are not directly regulated by DNA stress, leading to an imbalance between cellular DNA and protein content. Sequential deletion of up to 6 of the 7 ribosomal RNA operons corrects this imbalance and leads to improved survival and growth under DNA synthesis inhibition. Further, this genetic manipulation completely removes the suppressive drug interaction. Mathematical modeling shows that non-optimal regulation of ribosome synthesis under DNA stress can be explained as a side-effect of optimal growth-rate-dependent regulation in different nutrient environments. Together, these results reveal the genetic mechanism underlying an important class of suppressive drug interactions.
doi:10.1016/j.cell.2009.10.025
PMCID: PMC2838386  PMID: 19914165
18.  In Vivo Gene Expression Dynamics of Tumor-Targeted Bacteria 
ACS Synthetic Biology  2012;1(10):465-470.
The engineering of bacteria to controllably deliver therapeutics is an attractive application for synthetic biology. While most synthetic gene networks have been explored within microbes, there is a need for further characterization of in vivo circuit behavior in the context of applications where the host microbes are actively being investigated for efficacy and safety, such as tumor drug delivery. One major hurdle is that culture-based selective pressures are absent in vivo, leading to strain-dependent instability of plasmid-based networks over time. Here, we experimentally characterize the dynamics of in vivo plasmid instability using attenuated strains of S. typhimurium and real-time monitoring of luminescent reporters. Computational modeling described the effects of growth rate and dosage on live-imaging signals generated by internal bacterial populations. This understanding will allow us to harness the transient nature of plasmid-based networks to create tunable temporal release profiles that reduce dosage requirements and increase the safety of bacterial therapies.
doi:10.1021/sb3000639
PMCID: PMC3477096  PMID: 23097750
synthetic biology; S. typhimurium; bacterial cancer therapy; plasmid-loss dynamics
19.  On Ribosome Load, Codon Bias and Protein Abundance 
PLoS ONE  2012;7(11):e48542.
Different codons encoding the same amino acid are not used equally in protein-coding sequences. In bacteria, there is a bias towards codons with high translation rates. This bias is most pronounced in highly expressed proteins, but a recent study of synthetic GFP-coding sequences did not find a correlation between codon usage and GFP expression, suggesting that such correlation in natural sequences is not a simple property of translational mechanisms. Here, we investigate the effect of evolutionary forces on codon usage. The relation between codon bias and protein abundance is quantitatively analyzed based on the hypothesis that codon bias evolved to ensure the efficient usage of ribosomes, a precious commodity for fast growing cells. An explicit fitness landscape is formulated based on bacterial growth laws to relate protein abundance and ribosomal load. The model leads to a quantitative relation between codon bias and protein abundance, which accounts for a substantial part of the observed bias for E. coli. Moreover, by providing an evolutionary link, the ribosome load model resolves the apparent conflict between the observed relation of protein abundance and codon bias in natural sequences and the lack of such dependence in a synthetic gfp library. Finally, we show that the relation between codon usage and protein abundance can be used to predict protein abundance from genomic sequence data alone without adjustable parameters.
doi:10.1371/journal.pone.0048542
PMCID: PMC3492488  PMID: 23144899
20.  Stochastic signalling rewires the interaction map of a multiple feedback network during yeast evolution 
Nature Communications  2012;3:682-.
During evolution, genetic networks are rewired through strengthening or weakening their interactions to develop new regulatory schemes. In the galactose network, the GAL1/GAL3 paralogues and the GAL2 gene enhance their own expression mediated by the Gal4p transcriptional activator. The wiring strength in these feedback loops is set by the number of Gal4p binding sites. Here we show using synthetic circuits that multiplying the binding sites increases the expression of a gene under the direct control of an activator, but this enhancement is not fed back in the circuit. The feedback loops are rather activated by genes that have frequent stochastic bursts and fast RNA decay rates. In this way, rapid adaptation to galactose can be triggered even by weakly expressed genes. Our results indicate that nonlinear stochastic transcriptional responses enable feedback loops to function autonomously, or contrary to what is dictated by the strength of interactions enclosing the circuit.
GAL genes enhance their own transcription via the transcription factor Gal4p, and the number of Galp4 sites in a promoter is expected to strengthen the feedback. In this study, Hsu et al. show that instead the feedback loops are activated by genes that have frequent bursts of expression and fast RNA decay kinetics.
doi:10.1038/ncomms1687
PMCID: PMC3293423  PMID: 22353713
21.  Dynamic, Sex-Differential STAT5 and BCL6 Binding to Sex-Biased, Growth Hormone-Regulated Genes in Adult Mouse Liver 
Molecular and Cellular Biology  2012;32(4):880-896.
Sex-dependent pituitary growth hormone (GH) secretory patterns determine the sex-biased expression of >1,000 genes in mouse and rat liver, affecting lipid and drug metabolism, inflammation, and disease. A fundamental biological question is how robust differential expression can be achieved for hundreds of sex-biased genes simply based on the GH input signal pattern: pulsatile GH stimulation in males versus near-continuous GH exposure in females. STAT5 is an essential transcriptional mediator of the sex-dependent effects of GH in the liver, but the mechanisms that underlie its sex-dependent actions are obscure. Here we elucidate the dynamic, sex-dependent binding of STAT5 and the GH/STAT5-regulated repressor BCL6 to mouse liver chromatin genome wide, revealing a counteractive interplay between these two regulators of sex differences in liver gene expression. Our findings establish a close correlation between sex-dependent STAT5 binding and sex-biased target gene expression. Moreover, sex-dependent STAT5 binding correlated positively with sex-biased DNase hypersensitivity and H3-K4me1 and H3-K4me3 (activating) marks, correlated negatively with sex-biased H3-K27me3 (repressive) marks, and was associated with sex-differentially enriched motifs for HNF6/CDP factors. Importantly, BCL6 binding was preferentially associated with repression of female-biased STAT5 targets in male liver. Furthermore, BCL6 and STAT5 common targets but not BCL6 unique targets showed strong enrichment for lipid and drug metabolism. These findings provide a comprehensive, genome-wide view of the mechanisms whereby these two GH-regulated transcription factors establish and maintain sex differences affecting liver physiology and disease. The approaches used here to characterize sex-dependent STAT5 and BCL6 binding can be applied to other condition-specific regulatory factors and binding sites and their interplay with cooperative chromatin binding factors.
doi:10.1128/MCB.06312-11
PMCID: PMC3272977  PMID: 22158971
22.  mTOR Signaling Feedback Modulates Mammary Epithelial Differentiation and Restrains Invasion Downstream of PTEN Loss 
Cancer research  2013;73(16):5218-5231.
Oncogenic signaling pathways are tightly regulated by negative feedback circuits and relief of these circuits represents a common mechanism of tumor drug resistance. Although the significance of these feedback pathways for signal transduction is evident, their relevance for cellular differentiation and morphogenesis in a genetically-defined context is unclear. In this study, we used isogenic benign mammary organotypic cultures to interrogate the role of mTOR-mediated negative feedback in the specific setting of PTEN inactivation. We found that mTOR signaling promoted basal-like differentiation and repressed nuclear hormone receptor expression after short-term PTEN loss in murine cell cultures analyzed ex vivo. Unexpectedly, we found that PTEN inactivation inhibited growth factor-induced epithelial invasion, and that downstream mTOR-mediated signaling feedback was both necessary and sufficient for this effect. Mechanistically, using isogenic MCF10A cells with and without somaticPTEN deletion, we showed that mTOR inhibition promoted EGF-mediated epithelial invasion by de-repressing upstream EGFR, SRC and PI3K signaling. In addition to offering new signal transduction insights, these results bring to light a number of important and potentially clinically relevant cellular consequences of mTOR inhibition in the specific context of PTEN loss, including modulation of hormone and growth factor responsiveness and promotion of epithelial invasion. Our findings prompt future investigations of the possibility that mTOR inhibitor therapy may not only be ineffective but even deleterious in tumors with PTEN loss.
doi:10.1158/0008-5472.CAN-13-0429
PMCID: PMC3767295  PMID: 23774212
PTEN; mTORC1; feedback; invasion; breast
23.  Towards a unified theory for morphomechanics 
Mechanical forces are closely involved in the construction of an embryo. Experiments have suggested that mechanical feedback plays a role in regulating these forces, but the nature of this feedback is poorly understood. Here, we propose a general principle for the mechanics of morphogenesis, as governed by a pair of evolution equations based on feedback from tissue stress. In one equation, the rate of growth (or contraction) depends on the difference between the current tissue stress and a target (homeostatic) stress. In the other equation, the target stress changes at a rate that depends on the same stress difference. The parameters in these morphomechanical laws are assumed to depend on stress rate. Computational models are used to illustrate how these equations can capture a relatively wide range of behaviours observed in developing embryos, as well as show the limitations of this theory. Specific applications include growth of pressure vessels (e.g. the heart, arteries and brain), wound healing and sea urchin gastrulation. Understanding the fundamental principles of tissue construction can help engineers design new strategies for creating replacement tissues and organs in vitro.
doi:10.1098/rsta.2009.0100
PMCID: PMC2865877  PMID: 19657011
morphogenesis; development; biomechanics; mechanobiology; computational models; growth
24.  A framework for scalable parameter estimation of gene circuit models using structural information 
Bioinformatics  2013;29(13):i98-i107.
Motivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation.
Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems.
Availability: http://sfb.kaust.edu.sa/Pages/Software.aspx
Contact: xin.gao@kaust.edu.sa
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt232
PMCID: PMC3694671  PMID: 23813015
25.  Nonlinear Dynamic Trans/Cis Regulatory Circuit for Gene Transcription via Microarray Data 
The trans-regulatory circuit is considered as the regulatory interactions between upstream regulatory genes and transcription factor binding site motifs or cis elements. And the cis-regulatory circuit is viewed as a dynamic interactive circuit among binding site motifs with their effective action on the expression scheme of target gene. In brief, gene transcription depends on the trans/cis regulatory circuits. In this study, nonlinear trans/cis regulatory circuits for gene transcription in yeast are constructed using microarray data, translation time delay, and information of transcription factors (TFs) binding sites. We provide a useful nonlinear dynamic modeling and develop a parameter estimating method for the construction of trans/cis regulatory circuits, which is powerful for understanding gene transcription. We apply our method to construct trans/cis regulatory circuits of yeast cell cycle-related genes and successfully quantify their regulatory abilities and find possible cis-element interactions. Not only could the data of yeast be applied by our method, but those of other species also could. The proposed method can provide a quantitative basis for system analysis of gene circuits, which is potential for gene regulatory circuit design with a desired gene expression.
PMCID: PMC2759131  PMID: 19936085
transcription factor; nonlinear dynamic model; trans/cis regulatory circuit; cell cycle

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