Regulatory feedback on receptor and non‐receptor synthesis for robust signaling

Lander, Arthur D.; Nie, Qing; Sanchez-Tapia, Cynthia; Simonyan, Aghavni; Wan, Frederic Y. M.

doi:10.1002/dvdy.160

Cited by 4 publications

(24 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computed morphogen gradients from models in Refs. 6, 8, 29, 34, and 35 and the

v ℓ

‐Model herein (see Figures 3–7) are consistent with this theoretical prediction and available GFP‐DPP expressions for Dpp gradients in the Drosophila wing imaginal disc.…”

Section: The Vℓ‐modelsupporting

confidence: 86%

“…The inequality

B_{e} false(x false) = A_{e} false(x false) / α_{0} ≪ g_{r} / g_{0}

as required by () notwithstanding,

B_{e} false(x false)

may still be

O (1)

or larger because

g_{r} / g_{0}

may be

≫

1 as it is the case for the illustrative examples in Refs. 29 and 35 and in later sections of this paper.…”

Section: Time‐independent Steady State Without Feedbackmentioning

confidence: 77%

“…Unlike the robustness‐induced feedback of Refs. 29 and 35, the robustness index in each case can also be calculated after we have determined the various concentrations (and not to be solved simultaneously through an integro‐differential system as it is necessary for robustness index based feedback of Refs. 29 and 35).…”

Section: Time‐independent Steady State With Feedbackmentioning

confidence: 99%

“…Although the improvements from the new feedback process applied to receptor synthesis rate were found still insufficient for robust signaling, it enabled us to uncover the benefits of a concurrent application of two (or more) appropriate feedback processes. Several combinations of two robustness index based spatially uniform feedback processes, previously found ineffective when acting alone have been shown to be much more effective and successful in promoting robust signaling gradients 29,35 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spatially varying multifeedback for robust signaling

Wan

2021

Stud Appl Math

View full text Add to dashboard Cite

Elaborate regulatory feedback processes are thought to make biological development robust, that is, resistant to changes induced by sustained genetic or environmental perturbations. How this might be accomplished is still not completely understood, especially when models of some known feedback processes have been shown, some analytically and others by numerical simulations, to be ineffective in promoting robust signaling. By working with nonlocal feedback processes, combinations of two of these ineffective feedback processes were found to ensure robustness as measured by a well‐defined robustness index for the Dpp–Tkv (decapentaplegic–thickvein) signaling in the wing imaginal disc of Drosophila fruit flies. In this paper, we show that there are also spatially varying multifeedback combinations of individually ineffective components that are similarly successful. The analysis of these Hill function type multifeedback models are found to be much more challenging mathematically as the relevant boundary‐value problem remains nonlinear even for systems in a steady state of low receptor occupancy.

show abstract

“…Computed morphogen gradients from models in Refs. 6, 8, 29, 34, and 35 and the

v ℓ

‐Model herein (see Figures 3–7) are consistent with this theoretical prediction and available GFP‐DPP expressions for Dpp gradients in the Drosophila wing imaginal disc.…”

Section: The Vℓ‐modelsupporting

confidence: 86%

“…The inequality

B_{e} false(x false) = A_{e} false(x false) / α_{0} ≪ g_{r} / g_{0}

as required by () notwithstanding,

B_{e} false(x false)

may still be

O (1)

or larger because

g_{r} / g_{0}

may be

≫

1 as it is the case for the illustrative examples in Refs. 29 and 35 and in later sections of this paper.…”

Section: Time‐independent Steady State Without Feedbackmentioning

confidence: 77%

Section: Time‐independent Steady State With Feedbackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatially varying multifeedback for robust signaling

Wan

2021

Stud Appl Math

View full text Add to dashboard Cite

show abstract

“…For example, early interpretations of cellular processes as circuits provided insights about basic regulatory motifs that could explain cellular behaviors [6]. Similarly, techniques from chemistry, physics, and various engineering disciplines have been used to model cellular processes [7,8], but due to the spatiotemporal complexity of cellular processes, from femtosecond/nanometer electron transfer reactions to years and meter scales in tumor growth, no established paradigm has emerged to capture the full complexity of cellular processes. Multiple tools have been developed to achieve specific modeling tasks.…”

Section: Introductionmentioning

confidence: 99%

Programmatic modeling for biological systems

Lubbock

López

2021

Preprint

View full text Add to dashboard Cite

Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain- specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.

show abstract

Biodiversity-based development and evolution: the emerging research systems in model and non-model organisms

Zhao

Gao

et al. 2021

Sci. China Life Sci.

View full text Add to dashboard Cite

Evolutionary developmental biology, or Evo-Devo for short, has become an established field that, broadly speaking, seeks to understand how changes in development drive major transitions and innovation in organismal evolution. It does so via integrating the principles and methods of many subdisciplines of biology. Although we have gained unprecedented knowledge from the studies on model organisms in the past decades, many fundamental and crucially essential processes remain a mystery. Considering the tremendous biodiversity of our planet, the current model organisms seem insufficient for us to understand the evolutionary and physiological processes of life and its adaptation to exterior environments. The currently increasing genomic data and the recently available gene-editing tools make it possible to extend our studies to non-model organisms. In this review, we review the recent work on the regulatory signaling of developmental and regeneration processes, environmental adaptation, and evolutionary mechanisms using both the existing model animals such as zebrafish and Drosophila, and the emerging nonstandard model organisms including amphioxus, ascidian, ciliates, single-celled phytoplankton, and marine nematode. In addition, the challenging questions and new directions in these systems are outlined as well.

show abstract

Regulatory feedback on receptor and non‐receptor synthesis for robust signaling

Cited by 4 publications

References 49 publications

Spatially varying multifeedback for robust signaling

Spatially varying multifeedback for robust signaling

Programmatic modeling for biological systems

Biodiversity-based development and evolution: the emerging research systems in model and non-model organisms

Contact Info

Product

Resources

About