Experimental and Modeling Approaches for Understanding the Effect of Gene Expression Noise in Biological Development

Holloway, David M.

doi:10.3389/fphy.2018.00036

Cited by 2 publications

(2 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[51] This may provide the necessary tools to study high-affinity biomolecular interactions and access slowly fluctuating processes, e.g., "noise" in gene expression. [180,181] These processes are now difficult to access because fluorescence-based approaches are limited in their observation time to much less than an hour. On the other hand, mechanical measurements are stable in longterm, but require an external force, large handles, and linkers, and suffer from reduced statistics.…”

Section: Future Potential and Integrationmentioning

confidence: 99%

Plasmonic Assemblies for Real‐Time Single‐Molecule Biosensing

Armstrong

Horáček

Zijlstra

2020

Small

View full text Add to dashboard Cite

and bound states in the continuum, [27,28] and have been used in applications like biosensing [29] and color printing. [30] Plasmonic assemblies have proven particularly powerful in the field of biosensing. The presence of a biomarker results in, e.g., the (dis-)assembly of solution-phase plasmonic particles, or to changes in the particle-spacing in an assembly. Both result in strong changes in the far-field optical response of the plasmonic assemblies that is most often monitored using spectroscopy in a UV-vis spectrometer. In solution-based sensors, clustering of metal nanoparticles can be induced by target molecules, shifting the optical extinction spectrum of the nanoparticle solution (Figure 1d). [31] Such clustering assays based on particle (dis-) assembly have resulted in colorimetric assays for a range of analytes including proteins, DNA/RNA sequences, heavy ions, and toxic compounds. [31,32] Single-molecule biosensors, on the other hand, detect analytes by probing the optical response of a single nanoparticle. An important advantage of single-molecule sensing is the ability to construct histograms of sensor properties at the single-molecule level, e.g., the induced plasmon shift and statistical parameters like the waiting-time between events. Single-molecule biosensing using plasmonic sensors has been demonstrated in 2012 by Figure 1. Reported applications of nanoparticle assemblies. a) Left: Calculated SERS enhancement factors for a sphere dimer with a 2 nm gap spacing. Right: A Raman spectra of methylene blue dispersed on a monolayer (black) and bilayer (red) of gold nanoparticles. Adapted with permission. [11] Copyright 2019, American Chemical Society. (https://pubs.acs.org/doi/10.1021/acsnano.9b04224) Further permissions related to this material should be directed to the ACS. b) Non-linear emission spectrum of noncentrosymmetric patterned gold nanorods. The third-harmonic generation (THG) peak is in green and the second-harmonic generation (SHG) is in orange. Top inset-scheme of the assembly's THG (green) and SHG (orange) stemming from the red excitation wavelength (ω). Bottom inset-electron microscopy image of the nanoassembly (scale bar 200 nm). Adapted with permission. [16] Copyright 2019, American Chemical Society. c) Examples of metamaterials comprised of plasmonic assemblies (scale bars are 500 nm). Reproduced with permission. [25] Copyright 2014, Springer Nature. d) Left: A cluster assay biosensor in which nanoparticle assembly is induced by a target molecule. Right: An example UV-vis spectrum of the gold nanoparticle monomers (red) and subsequent assemblies upon addition of target molecules (blue).

show abstract

Section: Future Potential and Integrationmentioning

confidence: 99%

Plasmonic Assemblies for Real‐Time Single‐Molecule Biosensing

Armstrong

Horáček

Zijlstra

2020

Small

View full text Add to dashboard Cite

show abstract

“…Random fluctuations in molecular levels causes noise in various biological processes [1][2][3]. Cells must make crucial decisions in such noisy molecular environment thus their decisionmaking system should be able to control stochastic fluctuations.…”

Section: Introductionmentioning

confidence: 99%

Systematic analysis of noise reduction properties of coupled and isolated feed-forward loops

Chakravarty

Csikász‐Nagy

2021

PLoS Comput Biol

View full text Add to dashboard Cite

Cells can maintain their homeostasis in a noisy environment since their signaling pathways can filter out noise somehow. Several network motifs have been proposed for biological noise filtering and, among these, feed-forward loops have received special attention. Specific feed-forward loops show noise reducing capabilities, but we notice that this feature comes together with a reduced signal transducing performance. In posttranslational signaling pathways feed-forward loops do not function in isolation, rather they are coupled with other motifs to serve a more complex function. Feed-forward loops are often coupled to other feed-forward loops, which could affect their noise-reducing capabilities. Here we systematically study all feed-forward loop motifs and all their pairwise coupled systems with activation-inactivation kinetics to identify which networks are capable of good noise reduction, while keeping their signal transducing performance. Our analysis shows that coupled feed-forward loops can provide better noise reduction and, at the same time, can increase the signal transduction of the system. The coupling of two coherent 1 or one coherent 1 and one incoherent 4 feed-forward loops can give the best performance in both of these measures.

show abstract

Experimental and Modeling Approaches for Understanding the Effect of Gene Expression Noise in Biological Development

Cited by 2 publications

References 81 publications

Plasmonic Assemblies for Real‐Time Single‐Molecule Biosensing

Plasmonic Assemblies for Real‐Time Single‐Molecule Biosensing

Systematic analysis of noise reduction properties of coupled and isolated feed-forward loops

Contact Info

Product

Resources

About