Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in Mycobacterium tuberculosis

Ascensao, Joao A; Datta, Pran K.; Hancioglu, Baris; Sontag, Eduardo D.; Gennaro, Maria Laura; Igoshin, Oleg A.

doi:10.1371/journal.pcbi.1004741

Cited by 18 publications

(15 citation statements)

References 58 publications

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“…The argument does not work for x 9 due to a negative feedback loop between x 6 and x 7 (a directed path that goes around this loop will have the opposite parity from the path that does not). These results play a useful role in ruling out putative biological pathways, as illustrated next (see [14] for more details).…”

Section: B Monotonicitymentioning

confidence: 86%

“…The following Theorem and proof are from [14], and heavily rely upon an earlier version of the result given in [11].…”

Section: B Monotonicitymentioning

confidence: 99%

“…Sometimes we only care about conditional monotonicity, depending on monotonic behavior of a particular node, even if the input is not monotonic. The following theorem from [14] is useful in that context.…”

Section: B Monotonicitymentioning

confidence: 99%

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Control-theoretic methods for biological networks

Blanchini

EI-Samad

Giordano

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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Feedback is both a pillar of control theory and a pervasive principle of nature. For this reason, control-theoretic methods are powerful to analyse the dynamic behaviour of biological systems and mathematically explain their properties, as well as to engineer biological systems so that they perform a specific task by design. This paper illustrates the relevance of control-theoretic methods for biological systems. The first part gives an overview of biological control and of the versatile ways in which cells use feedback. By employing control-theoretic methods, the complexity of interlaced feedback loops in the cell can be revealed and explained, and layered feedback loops can be designed in the cell to induce the desired behaviours, such as oscillations, multi-stability and activity regulation. The second part is mainly devoted to modelling uncertainty in biology and understanding the robustness of biological phenomena due to their inherent structure. Important control-theoretic tools used in systems biology are surveyed. The third part is focused on tools for model discrimination in systems biology. A deeper understanding of molecular pathways and feedback loops, as well as qualitative information on biological networks, can be achieved by studying the "dynamic response phenotypes" that appear in temporal responses. Several applications to the analysis of biological systems are showcased.

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Section: B Monotonicitymentioning

confidence: 86%

“…The following Theorem and proof are from [14], and heavily rely upon an earlier version of the result given in [11].…”

Section: B Monotonicitymentioning

confidence: 99%

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Control-theoretic methods for biological networks

Blanchini

EI-Samad

Giordano

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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“…Second, I1-FFLs are proven mechanisms for non-monotonic time-responses in transcription networks that mediate trade-offs in metabolism (Weickert and Adhya, 1993;Kaplan et al, 2008;Ascensao et al, 2016). For example, an I1-FFL was shown to regulate the non-monotonic target gene responses of galactose metabolism so that when cells are severely starved for glucose, galactose breakdown is reduced, and galactose is redirected toward cell wall synthesis (Weickert and Adhya, 1993).…”

Section: Physiological Relevance Of Fflsmentioning

confidence: 99%

Hierarchical and Dynamic Regulation of Defense-Responsive Specialized Metabolism by WRKY and MYB Transcription Factors

Barco

Clay

2020

Front. Plant Sci.

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The plant kingdom produces hundreds of thousands of specialized bioactive metabolites, some with pharmaceutical and biotechnological importance. Their biosynthesis and function have been studied for decades, but comparatively less is known about how transcription factors with overlapping functions and contrasting regulatory activities coordinately control the dynamics and output of plant specialized metabolism. Here, we performed temporal studies on pathogen-infected intact host plants with perturbed transcription factors. We identified WRKY33 as the condition-dependent master regulator and MYB51 as the dual functional regulator in a hierarchical gene network likely responsible for the gene expression dynamics and metabolic fluxes in the camalexin and 4-hydroxy-indole-3-carbonylnitrile (4OH-ICN) pathways. This network may have also facilitated the regulatory capture of the newly evolved 4OH-ICN pathway in Arabidopsis thaliana by the more-conserved transcription factor MYB51. It has long been held that the plasticity of plant specialized metabolism and the canalization of development should be differently regulated; our findings imply a common hierarchical regulatory architecture orchestrated by transcription factors for specialized metabolism and development, making it an attractive target for metabolic engineering.

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“…"dynamic phenotypes") can provide further insight into the structure of biological systems. Recent examples include scale invariance or "fold-change detection" [10,11,12], non-monotonic behavior under monotonic inputs [13], refractory period stabilization [14], and non-entrained solutions or "period skipping" when stimulii are periodic [14].…”

Section: Introductionmentioning

confidence: 99%

Subharmonics and chaos in simple periodically-forced biomolecular models

Nikolaev

Rahi

Sontag

2017

Preprint

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This paper uncovers a remarkable behavior in two biochemical systems that commonly appear as components of signal transduction pathways in systems biology. These systems have globally attracting steady states when unforced, so they might have been considered "uninteresting" from a dynamical standpoint. However, when subject to a periodic excitation, strange attractors arise via a period-doubling cascade. Quantitative analyses of the corresponding discrete chaotic trajectories are conducted numerically by computing largest Lyapunov exponents, power spectra, and autocorrelation functions. To gain insight into the geometry of the strange attractors, the phase portraits of the corresponding iterated maps are interpreted as scatter plots for which marginal distributions are additionally evaluated. The lack of entrainment to external oscillations, in even the simplest biochemical networks, represents a level of additional complexity in molecular biology, which has previously been insufficiently recognized but is plausibly biologically important.

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Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in Mycobacterium tuberculosis

Cited by 18 publications

References 58 publications

Control-theoretic methods for biological networks

Control-theoretic methods for biological networks

Hierarchical and Dynamic Regulation of Defense-Responsive Specialized Metabolism by WRKY and MYB Transcription Factors

Subharmonics and chaos in simple periodically-forced biomolecular models

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