Chemical kinetics of induced gene expression: activation of transcription by noncooperative binding of multiple regulatory molecules

Abstract

A chemical kinetics model is described for the regulation of gene expression by the progressive binding of regulatory molecules to specific binding sites on DNA. Chemical rate equations are formulated and solved for the accumulation of regulatory molecules on DNA, the change in the level of induced mRNA, and the change in the level of the encoded protein in the activated tissue. Some special cases are examined, including that of an activation threshold created by a requirement for the binding of a minimum number of regulatory molecules prior to gene activation. Experimental data for several hormone-activated genetic systems are analyzed in the frame of the proposed model, and kinetic parameters are predicted. The model accounts for a number of experimental characteristics of hormone-inducible genetic systems, including the existence of a lag in the time course of mRNA accumulation, the sigmoidal curve of induced mRNA kinetics, the effect of hormone on mRNA stabilization, and the induction parameters observed when hormone analogues are used. The model also provides an explanation for the phenotypes of genetic variants with altered inducibility as changes in the molecular kinetic parameters of gene activity.

Development of lead hammerhead ribozyme candidates against human rod opsin mRNA for retinal degeneration therapy

“…The intrinsic decay rate of RHO protein $(k_{d,P}=\frac{0.693}{{\tau }_{d,P}})$ is then 1.30 × 10 −4 /min. The steady state numbers of RHO protein ( N P,ss ) can then be estimated (Almagor and Paigen, 1988): $$N_{P,ss}=\frac{k_{s,P}}{k_{d,P}}\ast \frac{k_{s,R}}{(k_{d,R}+k_{d,hhRz})}$$ where k d,hhRz is the rate of RHO mRNA degradation contributed by the hhRz. Assuming the rates calculated above and initially that k d,hhRz is zero, then N p,ss is found to be 1.08 × 10 8 RHO protein molecules per photoreceptor outer segment, which is clearly on the correct order of known biology.…”

Importance of Input Perturbations and Stochastic Gene Expression in the Reverse Engineering of Genetic Regulatory Networks: Insights From an Identifiability Analysis of an In Silico Network

“…With few exceptions (Davidson et al 2002;Lee et al 2002;Shen-Orr et al 2002), most known examples of transcriptional regulation consist of only a few interacting genes (Almagor and Paigen 1988;Reagan et al 1993;Alberts et al 1994;Reinitz and Sharp 1995;Meyer and Schmidt 1997;Ouali et al 1997;Herdegen and Leah 1998;Cherry and Adler 2000;Gardner et al 2000;Ramakrishnan et al 2002). There are few alternatives for constructing biologically relevant networks that contain large numbers of genes other than by assembling several known regulatory motifs.…”

Transient, cyclic changes in mouse visual cell gene products during the light‐dark cycle

“…However, the time course of approach from one steady state to a new steady state is determined solely by the rate of degradation. The validity, derivation, and application of the equations to the analysis of changes in protein and mRNA levels are presented by Berlin and Schimke (1965) and Almagor and Paigen (1988), respectively. Therefore, from the data for S-Ag shown in Figure 3, the fold change in mRNA may be calculated and the log of this value plotted for each time (t) against time t. These results are presented in Figure 4 and demonstrate that the half-life of S-Ag mRNA is about 3.5 h, irrespective of whether the amount of mRNA is increasing or decreasing during the light-dark cycle.…”