A Hill type equation can predict target gene expression driven by p53 pulsing

Many factors determine target gene expression dynamics under p53 pulsing. In this study, I sought to determine the mechanism by which duration, frequency, binding affinity and maximal transcription rate affect the expression dynamics of target genes. Using an analytical method to solve a simple model, I found that the fold change of target gene expression increases relative to the number of p53 pulses, and the optimal frequency, 0.18 h⁻¹, from two real p53 pulses drives the maximal fold change with a decay rate of 0.18 h⁻¹. Moreover, p53 pulses may also lead to a higher fold change than sustained p53. Finally, I discovered that a Hill‐type equation, including these effect factors, can characterise target gene expression. The average error between the theoretical predictions and experiments was 23%. Collectively, this equation advances the understanding of transcription factor dynamics, where duration and frequency play a significant role in the fine regulation of target gene expression with higher binding affinity.

CoViD-19 epidemic follows the "kinetics" of enzymes with cooperative substrate binding

The Hill equation, which models the cooperative ligand-receptor binding equilibrium, turns out to be useful in modeling the progression of infectious disease outbreaks such as CoViD-19. The equation fits well the data for total and daily case numbers, allows tentative predictions for the half-point and end point of the epidemic, and presents a mathematical characterization of how social interventions "flatten the curve" of the disease progression.

A Statistical Thermodynamic Model for Ligands Interacting With Ion Channels: Theoretical Model and Experimental Validation of the KCNQ2 Channel

Ion channels are important therapeutic targets, and their pharmacology is becoming increasingly important. However, knowledge of the mechanism of interaction of the activators and ion channels is still limited due to the complexity of the mechanisms. A statistical thermodynamic model has been developed in this study to characterize the cooperative binding of activators to ion channels. By fitting experimental concentration-response data, the model gives eight parameters for revealing the mechanism of an activator potentiating an ion channel, i.e., the binding affinity (K_A), the binding cooperative coefficients for two to four activator molecules interacting with one channel (γ, μ, and ν), and the channel conductance coefficients for four activator binding configurations of the channel (a, b, c, and d). Values for the model parameters and the mechanism underlying the interaction of ztz240, a proven KCNQ2 activator, with the wild-type channel have been obtained and revealed by fitting the concentration-response data of this activator potentiating the outward current amplitudes of KCNQ2. With these parameters, our model predicted an unexpected bi-sigmoid concentration-response curve of ztz240 activation of the WT-F137A mutant heteromeric channel that was in good agreement with the experimental data determined in parallel in this study, lending credence to the assumptions on which the model is based and to the model itself. Our model can provide a better fit to the measured data than the Hill equation and estimates the binding affinity, as well as the cooperative coefficients for the binding of activators and conductance coefficients for binding states, which validates its use in studying ligand-channel interaction mechanisms.

A three-stage algorithm to make toxicologically relevant activity calls from quantitative high throughput screening data

BACKGROUND

The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations than at a single concentration. In vitro quantitative high throughput screening (qHTS) assays are multiple-concentration experiments with an important role in the National Toxicology Program's (NTP) efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a more predictive science based on broad inclusion of biological observations.

OBJECTIVE

We developed a systematic approach to classify substances from large-scale concentration-​response data into statistically supported, toxicologically relevant activity categories.

METHODS

The first stage of the approach finds active substances with robust concentration-response profiles within the tested concentration range. The second stage finds substances with activity at the lowest tested concentration not captured in the first stage. The third and final stage separates statistically significant (but not robustly statistically significant) profiles from responses that lack statistically compelling support (i.e., "inactives"). The performance of the proposed algorithm was evaluated with simulated qHTS data sets.

RESULTS

The proposed approach performed well for 14-point-concentration-response curves with typical levels of residual error (σ ≤ 25%) or when maximal response (|RMAX|) was > 25% of the positive control response. The approach also worked well in most cases for smaller sample sizes when |RMAX| ≥ 50%, even with as few as four data points.

CONCLUSIONS

The three-stage classification algorithm performed better than one-stage classification approaches based on overall F-tests, t-tests, or linear regression.

Peptidyl-puromycin synthesis on polyribosomes from Escherichia coli

Peptide bond synthesis was studied with native polyribosomes of E. coli. With the use of this system for transpeptidation, it was possible to show that a single K(+) activates the ribosome monomers of polyribosomes; that protonation of a single group (probably imidazole or an N-terminal amino group) with a pK(a) equal to about 7.2 inactivates the transpeptidase complex; that Mn(++) can substitute for Mg(++), but that Ca(++), spermidine, and putrescine do so only very poorly; and that the K(m) for puromycin in this system is about 2.4 x 10(-6) M.