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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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The cell cycle is an organized set of events that leads the cell to divide into two daughter cells, each containing chromosomes identical to the parent cell. It is the cell cycle that leads to the formation of an entire organism from a single-cell zygote. Besides, cell division also functions in the renewal or repair of tissues in adult multicellular eukaryotes. For example, in the bone marrow, the stem cells divide to form new blood cells. Although essential for several functions, cell...
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Rapid Development of Cell State Identification Circuits with Poly-Transfection
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Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm.

Pak Kin Wong1, Fuqu Yu, Arash Shahangian

  • 1Department of Aerospace and Mechanical Engineering and Bio5 Institute, University of Arizona, N718, 1130 North Mountain Avenue, Tucson, AZ 85721, USA. pak@email.arizona.edu

Proceedings of the National Academy of Sciences of the United States of America
|March 22, 2008
PubMed
Summary

Identifying effective drug combinations for cellular control is complex. This study demonstrates a closed-loop optimization method that efficiently finds potent drug mixtures, significantly reducing trial-and-error experiments and required dosages.

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Area of Science:

  • Systems biology
  • Pharmacology
  • Computational biology

Background:

  • Drug combinations often show enhanced efficacy over single agents.
  • Identifying optimal drug combinations is challenging due to vast combinatorial possibilities and biological network complexity.

Purpose of the Study:

  • To develop and demonstrate an efficient closed-loop optimization modality for discovering potent drug combinations.
  • To control cellular functions by navigating large parametric spaces.

Main Methods:

  • Experimental closed-loop optimization approach.
  • Iterative searching within a large parametric space of drug combinations.
  • Application to viral infection inhibition and cytokine-mediated signaling pathways.

Main Results:

  • Identified a potent drug combination for inhibiting vesicular stomatitis virus infection in NIH 3T3 fibroblasts within tens of iterations.
  • Achieved a 10-fold reduction in drug dosage compared to individual drugs.
  • Discovered a potent cytokine mixture regulating nuclear factor kappa B activity in 293T cells within thirty iterations.

Conclusions:

  • The closed-loop optimization approach is highly effective for identifying potent drug combinations.
  • This method significantly accelerates the discovery process compared to traditional trial-and-error.
  • The approach has broad potential for manipulating diverse biological systems.