Predicting experimental designs leading to rewiring of transcription program and evolution of anticipatory regulation in E. coli (original) (raw)
Related papers
Adaptive prediction of environmental changes by microorganisms
Nature, 2009
Natural habitats of some microorganisms may fluctuate erratically, whereas others, which are more predictable, offer the opportunity to prepare in advance for the next environmental change. In analogy to classical Pavlovian conditioning, microorganisms may have evolved to anticipate environmental stimuli by adapting to their temporal order of appearance.
The physics of bacterial decision making
Frontiers in Cellular and Infection Microbiology, 2014
The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.
Positive feedback circuits and adaptive regulations in bacteria
Acta biotheoretica, 2001
The mechanisms by which bacteria adapt to changes in their environment involve transcriptional regulation in which a transcriptional regulator responds to signal(s) from the environment and regulates (positively or negatively) the expression of several genes or operons. Some of these regulators exert a positive feedback on their own expression. This is a necessary (although not sufficient) condition for the occurrence of multistationarity. One biological consequence of multistationarity may be epigenetic modifications, a hypothesis unusual to microbiologists, in spite of some well-known epigenetic modifications in bacteria. We propose here that the occurrence of mucoidy in the opportunistic pathogen Pseudomonas aeruginosa, which is currently attributed to mutations only, may also be an epigenetic modification. A theoretical approach using a generalised logical analysis lends credit to this hypothesis and suggests experiments to ascertain it.
Regulatory dynamics of standard two-component systems in bacteria
Journal of Theoretical Biology, 2010
Complex cellular networks regulate metabolism, environmental adaptation, and phenotypic changes in biological systems. Among the elements forming regulatory networks in bacteria are regulatory proteins such as transcription factors, which respond to exogenous and endogenous conditions. To perceive their surroundings, bacteria have evolved sensory regulatory systems of two-components. The archetype of these systems is made up of two proteins-a signal sensor and a response regulator-whose genes are usually located together in a single transcription unit. These units switch transcriptional programs in response to environmental conditions. Here, we study 14 two-component systems in Escherichia coli, which have been experimentally characterized with respect to their transcriptional regulation and their perceived signal. Given that the activity of these sensory units is connected to the rest of the transcriptional network, we first classify them as autonomous, semiautonomous or dependent, according to whether or not they use additional regulators to be transcribed. Next, we use discrete-time models to simulate their qualitative regulatory dynamics in response to their transcriptional regulation and to the activation of these systems by their cognate signals. Compared to more traditional ordinary differential equations method, ours has the advantage of being computationally simple and mathematically tractable, while keeping the ability to reproduce the phenomenology described by non-linear models. The aim of the present work is not the study of all possible behaviors of these two-component systems, but to exemplify those behaviors reported in the literature. On the other hand, most of these systems are auto-activating switches, a property that distinguishes them from the other transcription factors in the regulatory network, which are mostly auto-repressing. Based on the data, our models show dynamic behaviors that explain how most of these sensory systems convey abilities for multistationarity, and these dynamic properties could explain the phenotypic heterogeneity observed in bacterial populations. Our results are likely to have an impact in the design of synthetic signaling modules.
Biotechnology and Bioengineering, 2003
Bacteria develop a number of devices for sensing, responding, and adapting to different environmental conditions. Understanding within a genomic perspective how the transcriptional machinery of bacteria is modulated, as a response to changing conditions, is a major challenge for biologists. Knowledge of which genes are turned on or turned off under specific conditions is essential for our understanding of cell behavior. In this study we describe how the information pertaining to gene expression and associated growth conditions (even with very little knowledge of the associated regulatory mechanisms) is gathered from the literature and incorporated into Regulon-DB, a database on transcriptional regulation and operon organization in E. coli. The link between growth conditions, signal transduction, and transcriptional regulation is modeled in the database in a simple format that highlights biological relevant information. As far as we know, there is no other database that explicitly clarifies the effect of environmental conditions on gene transcription. We discuss how this knowledge constitutes a benchmark that will impact future research aimed at integration of regulatory responses in the cell; for instance, analysis of microarrays, predicting culture behavior in biotechnological processes, and comprehension of dynamics of regulatory networks. This integrated knowledge will contribute to the future goal of modeling the behavior of E. coli as an entire cell. The RegulonDB database can be accessed on the web at the
Frontiers in Microbiology, 2017
Rapidly changing concentrations of substrates frequently occur during large-scale microbial cultivations. These changing conditions, caused by large mixing times, result in a heterogeneous population distribution. Here, we present a powerful and efficient modeling approach to predict the influence of varying substrate levels on the transcriptional and translational response of the cell. This approach consists of two parts, a single-cell model to describe transcription and translation for an exemplary operon (trp operon) and a second part to characterize cell distribution during the experimental setup. Combination of both models enables prediction of transcriptional patterns for the whole population. In summary, the resulting model is not only able to anticipate the experimentally observed short-term and long-term transcriptional response, it further allows envision of altered protein levels. Our model shows that locally induced stress responses propagate throughout the bioreactor, resulting in temporal, and spatial population heterogeneity. Stress induced transcriptional response leads to a new population steady-state shortly after imposing fluctuating substrate conditions. In contrast, the protein levels take more than 10 h to achieve steady-state conditions.
Programming a Pavlovian-like conditioning circuit in Escherichia coli
Nature Communications, 2014
Synthetic genetic circuits are programmed in living cells to perform predetermined cellular functions. However, designing higher-order genetic circuits for sophisticated cellular activities remains a substantial challenge. Here we program a genetic circuit that executes Pavlovianlike conditioning, an archetypical sequential-logic function, in Escherichia coli. The circuit design is first specified by the subfunctions that are necessary for the single simultaneous conditioning, and is further genetically implemented using four function modules. During this process, quantitative analysis is applied to the optimization of the modules and fine-tuning of the interconnections. Analogous to classical Pavlovian conditioning, the resultant circuit enables the cells to respond to a certain stimulus only after a conditioning process. We show that, although the conditioning is digital in single cells, a dynamically progressive conditioning process emerges at the population level. This circuit, together with its rational design strategy, is a key step towards the implementation of more sophisticated cellular computing.
Adaptable Functionality of Transcriptional Feedback in Bacterial Two-Component Systems
A widespread mechanism of bacterial signaling occurs through two-component systems, comprised of a sensor histidine kinase (SHK) and a transcriptional response regulator (RR). The SHK activates RR by phosphorylation. The most common two-component system structure involves expression from a single operon, the transcription of which is activated by its own phosphorylated RR. The role of this feedback is poorly understood, but it has been associated with an overshooting kinetic response and with fast recovery of previous interrupted signaling events in different systems. Mathematical models show that overshoot is only attainable with negative feedback that also improves response time. Our models also predict that fast recovery of previous interrupted signaling depends on high accumulation of SHK and RR, which is more likely in a positive feedback regime. We use Monte Carlo sampling of the parameter space to explore the range of attainable model behaviors. The model predicts that the effective feedback sign can change from negative to positive depending on the signal level. Variations in two-component system architectures and parameters may therefore have evolved to optimize responses in different bacterial lifestyles. We propose a conceptual model where low signal conditions result in a responsive system with effectively negative feedback while high signal conditions with positive feedback favor persistence of system output.
Cybernetic modeling of adaptive prediction of environmental changes by microorganisms
Mathematical Biosciences, 2014
Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy.