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This software, written in MatLab, implements a variance-based approach to sensitivity analysis of biochemical reaction systems based on a biophysically derived model for parameter fluctuations. It produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects.
H.-X. Zhang, W. P. Dempsey, Jr., and J. Goutsias
Probabilistic sensitivity analysis of biochemical reaction systems
The Journal of Chemical Physics
131: 094101, 2009

This software, coded in MatLab, implements four techniques, derivative approximation, polynomial approximation, Gauss-Hermite integration, and orthonormal Hermite approximation, for analytically approximating the variance-based sensitivity indices associated with a biochemical reaction system.

H.-X. Zhang and J. Goutsias
A comparison of approximation techniques for variance-based sensitivity analysis of biochemical reaction systems
BMC Bioinformatics
11: 246, 2010

This software, coded in Matlab, implements a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. The method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations.
G. Jenkinson, X. Zhong, and J. Goutsias
Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems
BMC Bioinformatics
11: 547, 2010

This software, coded in Matlab, implements a variance-based approach to sensitivity analysis of biochemical reaction systems that explicitly models experimental variability and effectively reduces the impact of this type of uncertainty on the results. The approach is based on a previously proposed approach to probabilistic sensitivity analysis and leads to a technique that can be effectively used to accomodate appreciable levels of experimental variability. The software uses a computational model of the epidermal growth factor receptor signaling pathway.
H.-X. Zhang and J. Goutsias
Reducing experimental variability in variance-based sensitivity analysis of biochemical reaction systems
The Journal of Chemical Physics
134: 114105, 2011

This software, coded in Matlab, implements a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. TCMC formulates the model calibration problem as a constrained optimization problems that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. The provided software uses the Systems Biology Toolbox 2.1 that can be obtained by clicking here.
G. Jenkinson and J. Goutsias, Thermodynamically consistent model calibration in chemical kinetics
BMC Systems Biology
5: 64, 2011.

This software, coded in Matlab, implements a method for numerically integrating the master equation associated with the stochastic SIR model of epidemiology. The method is based on an informative stochastic process, known as the degree-of-advancement. Exploiting the stucture of the master equation governing this process results in a novel numerical algorithm for calculating the exact solution of the master equation, up to a desired precision, which is referred to as the implicit Euler method. The provided software uses the Expokit software package, whose details can be found by clicking here.
G. Jenkinson and J. Goutsias
Numerical integration of the master equation in some models of stochastic epidemiology
PLoS One
7(5): e36160, 2012.

This software, coded in Matlab, implements three examples of Markovian reaction networks used to illustrate key concepts of the theory of such networks. The examples include: opinion formation in social networks, transcription regulation in biochemical reaction networks, and avalanche formation in neural networks.
G. Jenkinson and J. Goutsias
Markovian dynamics on complex reaction networks
Physics Reports
529(2): 199-264, 2013.

This software, coded in Matlab, implements four examples of an effective statistical methodology that addresses the problem of checking whether a particular analytical or computational scheme to the solution of the chemical master equation is valid. The proposed technique is based on drawing a moderate number of samples from the master equation and on using the well-known Kolmogorov-Smirnov test statistic to reject the validity of a given approximation method or accept it with a certain level of confidence.

G. Jenkinson and J. Goutsias
Statistically testing the validity of analytical and computational approximations to the chemical master equation
The Journal of Chemical Physics
138: 204108, 2013.

This software, coded in Matlab, implements a method proposed for investigating the role intrinsic fluctuations play in creating avalanches in leaky Markovian networks. Using this broad class of models, the method employs a potential energy landscape perspective, coupled with a macroscopic description based on statistical thermodynamics, to study the emergence of avalanching in many biological, physical and man-made interaction networks.
G. Jenkinson and J. Goutsias,
Intrinsic noise induces critical behavior in leaky Markovian networks leading to avalanching
PLoS Computational Biology
10(1): e1003411, 2014.
This software, coded in R, implements IntegraMir, a novel integrative computational method to infer certain types of deregulated miRNA-mediated regulatory circuits at the transcriptional, post-transcriptional and signaling levels. To reliably predict miRNA-target interactions from mRNA/miRNA expression data, the method collectively utilizes sequence-based miRNA-target predictions obtained from several algorithms, known information about mRNA and miRNA targets of TFs available in existing databases, certain molecular structures identified to be statistically over-represented in gene regulatory networks, available molecular subtyping information, and state-of-the-art statistical techniques to appropriately constrain the underlying analysis.
A. S. Afshar, J. Xu, and J. Goutsias,
Integrative identification of deregulated miRNA/TF-mediated gene regulatory loops and networks in prostate cancer
PLoS One
9(6): e100806, 2014.