Detecting effective connectivity in networks of coupled neuronal oscillators.

The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

Using heterogeneous data sources in a systems biology approach to modeling the Sonic Hedgehog signaling pathway.

Experimental data from biological pathways come in many forms: qualitative or quantitative, static or dynamic. By combining a variety of these heterogeneous sources of data, we construct a mathematical model of a critical regulatory network in vertebrate development, the Sonic Hedgehog signaling pathway. The structure of our model is first constrained by several well-established pathway interactions. On top of this, we develop a hierarchical genetic algorithm that is capable of integrating different types of experimental data collected on the pathway's function, including qualitative as well as static and dynamic quantitative data, in order to estimate model parameters. The result is a dynamical model that fits the observed data and is robust to perturbations in its parameters. Since it is based on a canonical power-law representation of biochemical pathways whose parameters can be directly translated into physical interactions between network components, our model provides insight into the nature and strength of pathway interactions and suggests directions for future research.