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By fitting this data (in addition to the transient data presented below) to our finite element reaction-diffusion model, the effective diffusion rates were calculated.
Rate constants for EGFR phosphorylation/dephosphorylation and adaptor docking are estimated by fitting this data to simulations in our agent-based stochastic model, Signaling Pathways Simulator (SPS) [ 33].
By fitting this data to a two-step kinetic model, the obtained data from these precipitation experiments agreed with a well-established model for protein aggregation, which includes a nucleation and an autocatalytic growth phase.
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We modify an analytic expression for the induced voltage, previously derived for Newtonian fluids, to include a complex viscosity, and determine the viscous and elastic modulus of complex fluids by fitting this expression to our data.
We achieved this by constructing a stochastic model to describe the transmission dynamics within each ward and by fitting this model to patient data.
The general form of the equation is as follows: Juvenile and adult cancer potencies and the ratio of the two were calculated by fitting this model to the data for each age group.
In Equation 1, n and K are empirical constants which are obtained by fitting this equation to the experimental data shown in Figure 3.
By fitting the data from this plot into the one site binding model, an apparent Kd of 35±3 µM was revealed, which is higher than that of the aptamer alone (3 µM) [47].
Differential and algebraic equations describing this kinetic pathway were derived, and model parameters were determined by fitting the data.
Circulating half-life was determined by fitting the data to a single exponential decay.
The kinetic experimental data were analyzed by fitting the data to the pseudo-first-order and pseudo-second-order models.
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