» Articles » PMID: 20127700

Improved Network Performance Via Antagonism: From Synthetic Rescues to Multi-drug Combinations

Overview
Journal Bioessays
Publisher Wiley
Date 2010 Feb 4
PMID 20127700
Citations 17
Authors
Affiliations
Soon will be listed here.
Abstract

Recent research shows that a faulty or sub-optimally operating metabolic network can often be rescued by the targeted removal of enzyme-coding genes - the exact opposite of what traditional gene therapy would suggest. Predictions go as far as to assert that certain gene knockouts can restore the growth of otherwise nonviable gene-deficient cells. Many questions follow from this discovery: What are the underlying mechanisms? How generalizable is this effect? What are the potential applications? Here, I approach these questions from the perspective of compensatory perturbations on networks. Relations are drawn between such synthetic rescues and naturally occurring cascades of reaction inactivation, as well as their analogs in physical and other biological networks. I specially discuss how rescue interactions can lead to the rational design of antagonistic drug combinations that select against resistance and how they can illuminate medical research on cancer, antibiotics, and metabolic diseases.

Citing Articles

Understanding Braess' Paradox in power grids.

Schafer B, Pesch T, Manik D, Gollenstede J, Lin G, Beck H Nat Commun. 2022; 13(1):5396.

PMID: 36104335 PMC: 9474455. DOI: 10.1038/s41467-022-32917-6.


Extreme Antagonism Arising from Gene-Environment Interactions.

Wytock T, Zhang M, Jinich A, Fiebig A, Crosson S, Motter A Biophys J. 2020; 119(10):2074-2086.

PMID: 33068537 PMC: 7732749. DOI: 10.1016/j.bpj.2020.09.038.


Network Pharmacology-Guided Development of a Novel Integrative Regimen to Prevent Acute Graft-vs.-Host Disease.

Lyu M, Zhou Z, Wang X, Lv H, Wang M, Pan G Front Pharmacol. 2019; 9:1440.

PMID: 30618740 PMC: 6300759. DOI: 10.3389/fphar.2018.01440.


Antagonistic Phenomena in Network Dynamics.

Motter A, Timme M Annu Rev Condens Matter Phys. 2018; 9:463-484.

PMID: 30116502 PMC: 6089548. DOI: 10.1146/annurev-conmatphys-033117-054054.


Experimental evolution of diverse Escherichia coli metabolic mutants identifies genetic loci for convergent adaptation of growth rate.

Wytock T, Fiebig A, Willett J, Herrou J, Fergin A, Motter A PLoS Genet. 2018; 14(3):e1007284.

PMID: 29584733 PMC: 5892946. DOI: 10.1371/journal.pgen.1007284.


References
1.
Duarte N, Becker S, Jamshidi N, Thiele I, Mo M, Vo T . Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A. 2007; 104(6):1777-82. PMC: 1794290. DOI: 10.1073/pnas.0610772104. View

2.
Feist A, Henry C, Reed J, Krummenacker M, Joyce A, Karp P . A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol. 2007; 3:121. PMC: 1911197. DOI: 10.1038/msb4100155. View

3.
Hashimoto M, Ichimura T, Mizoguchi H, Tanaka K, Fujimitsu K, Keyamura K . Cell size and nucleoid organization of engineered Escherichia coli cells with a reduced genome. Mol Microbiol. 2004; 55(1):137-49. DOI: 10.1111/j.1365-2958.2004.04386.x. View

4.
Dancey J, Chen H . Strategies for optimizing combinations of molecularly targeted anticancer agents. Nat Rev Drug Discov. 2006; 5(8):649-59. DOI: 10.1038/nrd2089. View

5.
Isalan M, Lemerle C, Michalodimitrakis K, Horn C, Beltrao P, Raineri E . Evolvability and hierarchy in rewired bacterial gene networks. Nature. 2008; 452(7189):840-5. PMC: 2666274. DOI: 10.1038/nature06847. View