In our approach we generalize the equiangular condition in a generalized linear model. We propose an explicit method of monotonically decreasing sparsity for outcomes that can be modelled by an exponential family. In certain cases it is reasonable to assume that the underlying process generating the data is itself sparse, in the sense that only a few of the measured variables are involved in the process. Remote sensing, various forms of automated screening and other high throughput measurement devices collect a large amount of information, typically about few independent statistical subjects or units. Sparsity is an essential feature of many contemporary data problems. From the analysis it is clear that the sampler mixes well and partially is able to identify the dynamics of the MAPK/ERK pathway. To test the inference method we use the simulated data generated by the Gillespie algorithm. Additionally in inference of such a realistic and complex system we consider all possible kinds of dependencies coming from distinct stages of updates. In the estimation we apply the Euler approximation, which is the discretized version of the diffusion technique. Then we estimate the model parameters of the network in a Bayesian setting via MCMC and data augmentation schemes. Our reaction set takes into account the localization and different binding sites of the molecules in the cell by implementing the multiple parametrization. ![]() ![]() In this study we describe the MAPK/ERK pathway as an explicit set of reactions by combining different sources. Because of its importance in cellular lifecycle, it has been studied intensively, resulting in a number of qualitative descriptions of this regulatory mechanism. The MAPK/ERK pathway is one of the major signal transduction systems which regulates the cellular growth control of all eukaryotes like the cell proliferation and the apoptosis.
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