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Traditional enzyme modeling approaches use the Michaelis-Menten kinetic equation for one substrate/one product reactions while the King-Altman method is used to derive equations for more complex multiple reactant reactions.
Both timed and hybrid modeling approaches use time intervals in their transitions system, but failed at investigating large networks since the achieved models are quickly too complex for a standard analysis.
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Researchers have proposed various modeling approaches using ordinary and delayed differential equations [2 5].
The complex structure of active ERK2 (PDB ID: 2ERK) bound to Ets-1 (residues 29 138) (PDB ID: 2JV3) [21] was predicted using the molecular mechanics modeling approaches using a semi-rigid body potential smoothing energy minimization as described previously [13].
Chiu et al. [29] proposed a pixel-wise color background modeling approach using probability theory and clustering.
We present results of our activity modeling approach using videos of both high-resolution single individual activities and ground plane surveillance activities.
The models were built following a statistical approach using a continuous Hidden Markov Model, a black-box state space modeling approach using Recursive Multi-Layer Perceptrons, and a histrogram-based approach.
A physiologically based pharmacokinetic (PBPK) modeling approach using the Simcyp program was implemented.
A hierarchical linear modeling approach, using the Poisson model with equal exposure and a logarithmic link function, was used to model these events.
The reduced order modelling approach uses prediction data for the thermo-mechanical behaviour.
TGS modelling approach uses a simple algorithm resulting from the best accumulated combination of candidate gene polymorphisms.
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