Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Signal processing during developmental multicellular patterning.

Claudiu A Giurumescu1, Anand R Asthagiri

  • 1Division of Chemistry and Chemical Engineering, The Jacobs Institute for Molecular Engineering for Medicine, California Institute of Technology, Pasadena, California 91125, USA.

Biotechnology Progress
|September 27, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition.

Journal of proteome research·2024
Same author

Dynamics of single-cell protein covariation during epithelial-mesenchymal transition.

bioRxiv : the preprint server for biology·2024
Same author

Multivariate relationships among nucleus and Golgi properties during fibrillar migration are robust to and unchanged by epithelial-to-mesenchymal transition.

PloS one·2020
Same author

Label-Free Automated Cell Tracking: Analysis of the Role of E-cadherin Expression in Collective Electrotaxis.

Cellular and molecular bioengineering·2019
Same author

Golgi Stabilization, Not Its Front-Rear Bias, Is Associated with EMT-Enhanced Fibrillar Migration.

Biophysical journal·2018
Same author

Positive Quantitative Relationship between EMT and Contact-Initiated Sliding on Fiber-like Tracks.

Biophysical journal·2016
Same journal

Purification and concentration of model viruses using single-pass tangential flow filtration.

Biotechnology progress·2026
Same journal

Advanced glucose control strategies leveraging Raman spectroscopy for optimized mammalian cell culture manufacturing.

Biotechnology progress·2026
Same journal

Mechanistic deconvolution of BSA size variants by constrained Raman pseudo-Voigt hard modeling during anion-exchange chromatography.

Biotechnology progress·2026
Same journal

Status and future of recombinant adeno-associated virus vector manufacturing.

Biotechnology progress·2026
Same journal

Multifaceted algae as an ingredient in alternative meat formulations.

Biotechnology progress·2026
Same journal

In-line Raman spectroscopy real-time glucose prediction method for commercial pneumococcal vaccine drug substance fermentation manufacturing process control.

Biotechnology progress·2026
See all related articles

Understanding how cells self-organize in tissue engineering requires analyzing biomolecular signals. This review details three signal processing layers crucial for multicellular patterning and phenotype development.

Area of Science:

  • Biomedical Engineering
  • Developmental Biology
  • Cellular Signaling

Background:

  • Tissue engineering and regenerative medicine design strategies are hindered by limited understanding of cell self-organization on synthetic scaffolds.
  • Embryonic and adult development offer mechanistic insights into multicellular patterning driven by biomolecular signals.

Purpose of the Study:

  • To review the critical layers of signal processing governing multicellular patterning.
  • To highlight the impact of quantitative signal attributes on cell self-organization and phenotype.

Main Methods:

  • Analysis of spatiotemporal presentation of extracellular cues.
  • Examination of intracellular signaling networks and crosstalk.
  • Investigation of intranuclear signal integration and transcriptional regulation.

Related Experiment Videos

Main Results:

  • Multicellular patterning is governed by three hierarchical signal processing layers: extracellular cue presentation, intracellular signaling, and intranuclear integration.
  • Quantitative signal attributes significantly influence patterning outcomes at each processing level.
  • Experiments and mathematical models are key to uncovering these quantitative features.

Conclusions:

  • A deeper understanding of quantitative signal processing is essential for advancing tissue engineering and regenerative medicine.
  • Elucidating these mechanisms can guide the design of synthetic scaffolds that promote desired multicellular phenotypes.
  • Integrating experimental and computational approaches is vital for deciphering complex cell self-organization processes.