Peter J. Steinbach
Center for Molecular Modeling
Center for Information Technology
National Institutes of Health
The program MemExp uses the maximum entropy method (MEM) and either nonlinear least squares (NLS) or maximum likelihood (ML) fitting to analyze a general time-dependent signal in terms of distributed and discrete lifetimes.
One or two distributions of effective log-lifetimes, 
and 
, plus an optional polynomial baseline
(up to a cubic) can be extracted from the data. The h distribution is used to account for signals opposite
in sign to those described by the g distribution when analyzing data that rise and fall. Both
distributions are obtained numerically from the data and are not restricted to
any functional form. Simulataneously, MemExp performs a series of fits by discrete exponentials in
which exponentials are added one at a time and are initialized based on the emerging structure in the MEM
distribution. The amplitude and log-lifetime of each exponential, plus any optional baseline
parameters utilized, are varied using either NLS (for Gaussian noise) or ML (for Poisson noise) fitting.
MemExp automatically recommends one distributed and one discrete description of the kinetics as optimal. The graphical
summary plotted by MemExp permits a thorough evalutaion of the results. Multiple MEM `prior models' are supported,
facilitating a comprehensive analysis of the kinetics data.
New to version 3.0: Rigorous treatment of Poisson noise supported for both the distributed and discrete fits. Deconvolution of instrument response function supported, as well as a correction for scattered excitation light. Dimensions of arrays increased. Input change: IGNORE is now an integer (assigned 0, 1, or 2); formerly a logical variable.
MemExp was written in FORTRAN77 and has been built under several operating systems. Graphical output is in PostScript format.
References: Please refer to the following papers when publishing results obtained using MemExp.
P.J. Steinbach, R. Ionescu, and C.R. Matthews. Analysis of Kinetics using a Hybrid Maximum-Entropy / Nonlinear-Least-Squares Method: Application to Protein Folding. (2002) Biophys. J. 82: 2244-2255.
P.J. Steinbach. Inferring Lifetime Distributions from Kinetics by Maximizing Entropy Using a Bootstrapped Model. (2002) J. Chem. Inf. Comput. Sci. 42: 1476-1478.