The Effects of Ribosome Autocatalysis and Negative Feedback in Resource Competition

Mapping Intimacies ◽

10.1101/042127 ◽

2016 ◽

Cited By ~ 2

Author(s):

Fiona Chandra ◽

Domitilla Del Vecchio

Keyword(s):

Steady State ◽

Negative Feedback ◽

Resource Competition ◽

Mrna Level ◽

Transient Behavior ◽

Rrna Synthesis ◽

Qualitative Behavior ◽

Genetic Circuits ◽

Synthetic Genes ◽

State Models

Resource competition, and primarily competition for ribosomes, can lead to unexpected behavior of genetic circuits and has recently gained renewed attention with both experimental and theoretical studies. Current models studying the effects of resource competition assume a constant production of ribosomes and these models describe the experimental results well. However, ribosomes are also autocatalytic since they are partially made of protein and autocatalysis has been shown to have detrimental effects on a system's stability and robustness. Additionally, there are known feedback regulations on ribosome synthesis such as inhibition of rRNA synthesis via ppGpp. Here, we develop two-state models of ribosome and protein synthesis incorporating autocatalysis and feedback to investigate conditions under which these regulatory actions have a significant effect in situations of increased ribosome demand. Our modeling results indicate that for sufficiently low demand, defined by the mRNA level of synthetic genes, autocatalysis has little or no effect. However, beyond a certain demand level, the system goes through a transcritical bifurcation at which the only non-negative steady state is at zero ribosome concentration. The presence of negative feedback, in turn, can shift this point to higher demand values, thus restoring the qualitative behavior observed in a model with a constant ribosome production at low demand. However, autocatalysis affects the dynamics of the system and can lead to an overshoot in the temporal response of the synthetic genes to changes in induction level. Our results show that ribosome autocatalysis has a significant effect on the system robustness to increases in ribosome demand, however the existing negative feedback on ribosome production compensates for the effects of the necessary autocatalytic loop and restores the behavior seen in the system with constant ribosome production. These findings explain why previous models with constant ribosome production reproduce the steady state behavior well, however incorporating autocatalysis and feedback is needed to capture the transient behavior.

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Deterministic characterization of stochastic genetic circuits

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0610468104 ◽

2007 ◽

Vol 104 (18) ◽

pp. 7402-7407 ◽

Cited By ~ 68

Author(s):

Matthew Scott ◽

Terence Hwa ◽

Brian Ingalls

Keyword(s):

Negative Feedback ◽

Positive Feedback ◽

Feedback Loop ◽

Transcriptional Control ◽

Stochastic Systems ◽

Model Parameters ◽

Qualitative Behavior ◽

Genetic Circuits ◽

Molecular Noise ◽

Positive Feedback Circuit

For cellular biochemical reaction systems where the numbers of molecules is small, significant noise is associated with chemical reaction events. This molecular noise can give rise to behavior that is very different from the predictions of deterministic rate equation models. Unfortunately, there are few analytic methods for examining the qualitative behavior of stochastic systems. Here we describe such a method that extends deterministic analysis to include leading-order corrections due to the molecular noise. The method allows the steady-state behavior of the stochastic model to be easily computed, facilitates the mapping of stability phase diagrams that include stochastic effects, and reveals how model parameters affect noise susceptibility in a manner not accessible to numerical simulation. By way of illustration we consider two genetic circuits: a bistable positive-feedback loop and a negative-feedback oscillator. We find in the positive feedback circuit that translational activation leads to a far more stable system than transcriptional control. Conversely, in a negative-feedback loop triggered by a positive-feedback switch, the stochasticity of transcriptional control is harnessed to generate reproducible oscillations.

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Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources

Life ◽

10.3390/life11040271 ◽

2021 ◽

Vol 11 (4) ◽

pp. 271

Author(s):

Chentao Yong ◽

Andras Gyorgy

Keyword(s):

Cell Fate ◽

Rational Design ◽

Resource Competition ◽

Dynamic Properties ◽

Mechanistic Model ◽

Robustness Analysis ◽

System Level ◽

Genetic Circuits ◽

Scarce Resources ◽

Genetic Systems

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.

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Axially Symmetric Steady-State Models of the Basic State for Instability and Climate Studies. Part II. Nonlinear Calculations

Journal of the Atmospheric Sciences ◽

10.1175/1520-0469(1977)034<0280:asssmo>2.0.co;2 ◽

1977 ◽

Vol 34 (2) ◽

pp. 280-296 ◽

Cited By ~ 157

Author(s):

Edwin K. Schneider

Keyword(s):

Steady State ◽

Axially Symmetric ◽

State Models ◽

Climate Studies

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Fluctuations and noise in kinetic systems III. Cycling steady-state models

Journal of Theoretical Biology ◽

10.1016/s0022-5193(75)80117-2 ◽

1975 ◽

Vol 55 (1) ◽

pp. 229-243 ◽

Cited By ~ 24

Author(s):

Yi-der Chen

Keyword(s):

Steady State ◽

State Models

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Cyclic AMP analogs and retinoic acid influence the expression of retinoic acid receptor alpha, beta, and gamma mRNAs in F9 teratocarcinoma cells

Molecular and Cellular Biology ◽

10.1128/mcb.10.1.391-396.1990 ◽

1990 ◽

Vol 10 (1) ◽

pp. 391-396

Author(s):

L Hu ◽

L J Gudas

Keyword(s):

Retinoic Acid ◽

Steady State ◽

Cyclic Amp ◽

Cellular Response ◽

Retinoic Acid Receptor ◽

Mrna Level ◽

Mrna Levels ◽

Protein Synthesis Inhibitors ◽

Receptor Alpha ◽

F9 Teratocarcinoma

Retinoic acid (RA) receptor alpha (RAR alpha) and RAR gamma steady-state mRNA levels remained relatively constant over time after the addition of RA to F9 teratocarcinoma stem cells. In contrast, the steady-state RAR beta mRNA level started to increase within 12 h after the addition of RA and reached a 20-fold-higher level by 48 h. This RA-associated RAR beta mRNA increase was not prevented by protein synthesis inhibitors but was prevented by the addition of cyclic AMP analogs. In the presence of RA, cyclic AMP analogs also greatly reduced the RAR alpha and RAR gamma mRNA levels, even though cyclic AMP analogs alone did not alter these mRNA levels. The addition of either RA or RA plus cyclic AMP analogs did not result in changes in the three RAR mRNA half-lives. These results suggest that agents which elevate the internal cyclic AMP concentration may also affect the cellular response to RA by altering the expression of the RARs.

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Stress mRNA metabolism in canavanine-treated chicken embryo cells

Molecular and Cellular Biology ◽

10.1128/mcb.4.8.1534-1541.1984 ◽

1984 ◽

Vol 4 (8) ◽

pp. 1534-1541

Author(s):

C N White ◽

L E Hightower

Keyword(s):

Steady State ◽

Stress Proteins ◽

Animal Cell ◽

Stress Protein ◽

High Rate ◽

Rrna Synthesis ◽

28S Rrna ◽

Mrna Levels ◽

Molecular Weights

Four major chicken stress mRNAs with apparent molecular weights of 1.2 X 10(6), 0.88 X 10(6), 0.59 X 10(6), and 0.25 X 10(6) to 0.28 X 10(6) were separated on acidic agarose-urea gels. Using cell-free translation, the coding assignments of these mRNAs were determined to be stress proteins with apparent molecular weights of 88,000, 71,000, 35,000, and 23,000. Despite high levels of translational activity in vivo and in vitro, no newly synthesized mRNA for the 23-kilodalton stress protein was detected on gels under conditions which readily allowed detection of other stress mRNAs, suggesting activation of a stored or incompletely processed mRNA. Cloned Drosophila heat shock genes were used to identify and measure changes in cellular levels of the two largest stress mRNAs. Synthesis of these mRNAs increased rapidly during the first hour of canavanine treatment and continued at a high rate for at least 7 h, with the mRNAs attaining new steady-state levels by ca. 3 h. Both of these inducible stress mRNAs had very short half-lives compared with other animal cell mRNAs. Using an approach-to-steady-state analysis, the half-lives were calculated to be 89 min for the mRNA encoding the 88-kilodalton stress protein and 46 min for the mRNA encoding the 71-kilodalton stress protein. Chicken 18S and 28S rRNA synthesis was inhibited, and actin mRNA levels measured with cloned cDNA encoding chicken beta-actin slowly declined in canavanine-treated cells.

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Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters

Energies ◽

10.3390/en11102544 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2544 ◽

Cited By ~ 5

Author(s):

En-Chih Chang

Keyword(s):

Steady State ◽

Sliding Mode Control ◽

Digital Signal Processor ◽

Sliding Mode ◽

Transient Behavior ◽

Voltage Source ◽

Accurate Estimation ◽

Inference System ◽

Mode Control ◽

State Error

In this paper, an intelligent sliding mode controlled voltage source inverter (VSI) is developed to achieve not only quick transient behavior, but satisfactory steady-state response. The presented approach combines the respective merits of a nonsingular fast terminal attractor (NFTA) as well as an adaptive neuro-fuzzy inference system (ANFIS). The NFTA allows no singularity and error states to be converged to the equilibrium within a finite time, while conventional sliding mode control (SMC) leads to long-term (infinite) convergent behavior. However, there is the likelihood of chattering or steady-state error occurring in NFTA due to the overestimation or underestimation of system uncertainty bound. The ANFIS with accurate estimation and the ease of implementation is employed in NFTA for suppressing the chatter or steady-state error so as to improve the system’s robustness against uncertain disturbances. Simulation results display that this described approach yields low distorted output wave shapes and quick transience in the presence of capacitor input rectifier loading as well as abrupt connection of linear loads. Experimental results conducted on a 1 kW VSI prototype with control algorithm implementation in Texas Instruments DSP (digital signal processor) support the theoretic analysis and reaffirm the robust performance of the developed VSI. Because the proposed VSI yields remarkable benefits over conventional terminal attractor VSIs on the basis of computational quickness and unsophisticated realization, the presented approach is a noteworthy referral to the designers of correlated VSI applications in future, such as DC (direct current) microgrids and AC (alternating current) microgrids, or even hybrid AC/DC microgrids.

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Flame Failures and Recovery in Industrial Furnaces: A Neural Network Steady-State Model for the Firing Rate Setpoint Rearrangement

International Journal of Chemical Engineering ◽

10.1155/2018/3790849 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15

Author(s):

Tahmineh Adili ◽

Zohreh Rostamnezhad ◽

Ali Chaibakhsh ◽

Ali Jamali

Keyword(s):

Neural Network ◽

Steady State ◽

Optimization Problems ◽

Network Models ◽

Economic Loss ◽

Neural Network Models ◽

Firing Rates ◽

State Models ◽

Major Equipment ◽

Artificial Neural Network Models

Burner failures are common abnormal conditions associated with industrial fired heaters. Preventing from economic loss and major equipment damages can be attained by compensating the lost heat due to burners’ failures, which can be possible by defining appropriate setpoints to rearrange the firing rates for healthy burners. In this study, artificial neural network models were developed for estimating the appropriate setpoints for the combustion control system to recover an industrial fired-heater furnace from abnormal conditions. For this purpose, based on an accurate high-order mathematical model, constrained nonlinear optimization problems were solved using the genetic algorithm. For different failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal conditions were obtained and data were recorded for training and testing stages. The performances of the developed neural steady-state models were evaluated through simulation experiments. The obtained results indicated the feasibility of the proposed technique to deal with the failures in the combustion system.

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