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The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response...
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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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A drug’s dosage and pharmacokinetic properties determine how quickly it acts, how intense its effects are, and how long it lasts. Higher doses increase drug concentration at receptor sites, producing a hyperbolic curve when pharmacologic response is plotted against drug dose. Converting this scale to a log-linear format results in a sigmoidal curve, better representing dose–response relationships.For drugs following a one-compartment model, the pharmacologic response is directly...
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The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
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The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration,...
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Competitive inhibition can linearize dose-response and generate a linear rectifier.

Yonatan Savir1, Benjamin P Tu2, Michael Springer1

  • 1Department of Systems Biology, Harvard Medical School, Boston, MA 02115.

Cell Systems
|October 24, 2015
PubMed
Summary

Biological systems can act as linear rectifiers, responding linearly to inputs above a threshold. This enzyme reaction network motif, potentially found in yeast SAGA complexes, offers a new perspective on biological signal processing.

Keywords:
Competitive inhibitionEnzyme kineticsLinear rectifiersSAGATranscriptional regulation

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Area of Science:

  • Biochemistry
  • Systems Biology
  • Molecular Biology

Background:

  • Biological systems often require large dynamic ranges for responses, exceeding standard molecular interaction capabilities.
  • Maintaining an 'off' state at low input levels is crucial for many cellular processes.

Purpose of the Study:

  • To mathematically demonstrate how enzyme reaction systems can function as linear rectifiers.
  • To propose the yeast SAGA histone acetylation complex as a potential biological linear rectifier.

Main Methods:

  • Mathematical modeling of enzyme reaction systems.
  • Analysis of network motifs involving competitive inhibition, substrate-inhibitor conservation, and positive feedback.

Main Results:

  • Enzyme systems with specific configurations can exhibit linear rectifier behavior.
  • The proposed model shows sensitivity to substrate above a threshold and unresponsiveness below it.

Conclusions:

  • Linear rectifiers represent a viable network motif for biological signal processing.
  • The yeast SAGA complex may function as a linear rectifier, suggesting potential experimental validation.
  • Linear responses and rectifiers may be more readily evolved or constructed than previously thought.