An S-System Parameter Estimation Method (SPEM) for Biological Networks

An S-System Parameter Estimation Method (SPEM) for Biological Networks

Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on th...

SPEM (S-system Parameter Estimation Method) Vignette

1 Goal SPEM (S-system Parameter Estimation Method) package allows for the computation of parameters in the n-gene S-system from time series data. 2 Introduction Biological systems are composed of interacting components [1]. The process of the expression and the interactions of these components are nonlinear. S-systems have a power law formalism which is general enough to capture the base of the...

Neural-network-based parameter estimation in S-system models of biological networks.

The genomic and post-genomic eras have been blessing us with overwhelming amounts of data that are of increasing quality. The challenge is that most of these data alone are mere snapshots of the functioning organism and do not reveal the organizational structure of which the particular genes and metabolites are contributors. To gain an appreciation of their roles and functions within cells and ...

Parameter estimation for Boolean models of biological networks

Boolean networks have long been used as models of molecular networks and play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With onl...

Proper orthogonal decomposition for parameter estimation in oscillating biological networks