Epigenomics: genome-wide study of methylation phenomena.

Epigenetics is one of the key areas of future research that can elucidate how genomes work. It combines genetics and the environment to address complex biological systems such as the plasticity of our genome. While all nucleated human cells carry the same genome, they express different genes at different times. Much of this is governed by epigenetic changes resulting in differential methylation of our genome--or different epigenomes. Individual studies over the past decades have already established the involvement of DNA methylation in imprinting, gene regulation, chromatin structure, genome stability and disease, especially cancer. Now, in the wake of the Human Genome Project (HGP), epigenetic phenomena can be studied genome-wide and are giving rise to a new field, epigenomics. Here, we review the current and future potential of this field and introduce the pilot study towards the Human Epigenome Project (HEP).

Feature selection for DNA methylation based cancer classification.

Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia.

The effect of intracortical competition on the formation of topographic maps in models of Hebbian learning.

Correlation-based learning (CBL) models and self-organizing maps (SOM) are two classes of Hebbian models that have both been proposed to explain the activity-driven formation of cortical maps. Both models differ significantly in the way lateral cortical interactions are treated, leading to different predictions for the formation of receptive fields. The linear CBL models predict that receptive field profiles are determined by the average values and the spatial correlations of the second order of the afferent activity patterns, whereas SOM models map stimulus features. Here, we investigate a class of models which are characterized by a variable degree of lateral competition and which have the CBL and SOM models as limit cases. We show that there exists a critical value for intracortical competition below which the model exhibits CBL properties and above which feature mapping sets in. The class of models is then analyzed with respect to the formation of topographic maps between two layers of neurons. For Gaussian input stimuli we find that localized receptive fields and topographic maps emerge above the critical value for intracortical competition, and we calculate this value as a function of the size of the input stimuli and the range of the lateral interaction function. Additionally, we show that the learning rule can be derived via the optimization of a global cost function in a framework of probabilistic output neurons which represent a set of input stimuli by a sparse code.

Contrast adaptation and infomax in visual cortical neurons.

In the primary visual cortex (V1) the contrast response function of many neurons saturates at high contrast and adapts depending on the visual stimulus. We propose that both effects - contrast saturation and adaptation - can be explained by a fast and a slow component in the synaptic dynamics. In our model the saturation is an effect of fast synaptic depression with a recovery time constant of about 200 ms. Fast synaptic depression leads to a contrast response function with a high gain for only a limited range of contrast values. Furthermore, we propose that slow adaptation of the transmitter release probability at the geniculocortical synapses is the underlying neural mechanism that accounts for contrast adaptation on a time scale of about 7 sec. For the functional role of contrast adaptation we make the hypothesis that it serves to achieve the best visual cortical representation of the geniculate input. This representation should maximize the mutual information between the cortical activity and the geniculocortical input by increasing the release probability in a low contrast environment. We derive an adaptation rule for the transmitter release probability based on this infomax principle. We show that changes in the transmitter release probability may compensate for changes in the variance of the geniculate inputs - an essential requirement for contrast adaptation. Also, we suggest that increasing the release probability in a low contrast environment is beneficial for signal extraction, because neurons remain sensitive only to an increase in the presynaptic activity if it is synchronous and, therefore, likely to be stimulus related. Our hypotheses are tested in numerical simulations of a network of integrate-and-fire neurons for one column of V1 using fast synaptic depression and slow synaptic adaptation. The simulations show that changing the synaptic release probability of the geniculocortical synapses is a better model for contrast adaptation than the adaptation of the synaptic weights: only in the case of changing the transmitter release probability does our model reproduce the experimental finding that the average membrane potential (DC component) adapts much more strongly than the stimulus modulated component (F1 component). In the case of changing the synaptic weights, however, the average membrane potential (DC) as well as the stimulus modulated component (F1 component) would adapt. Furthermore, changing the release probability at the recurrent cortical synapses cannot account for contrast adaptation, but could be responsible for establishing oscillatory activity often observed in recordings from visual cortical cells.

The joint development of orientation and ocular dominance: role of constraints.

Correlation-based learning (CBL) has been suggested as the mechanism that underlies the development of simple-cell receptive fields in the primary visual cortex of cats, including orientation preference (OR) and ocular dominance (OD) (Linsker, 1986; Miller, Keller, & Stryker, 1989). CBL has been applied successfully to the development of OR and OD individually (Miller, Keller, & Stryker, 1989; Miller, 1994; Miyashita & Tanaka, 1991; Erwin, Obermayer, & Schulten, 1995), but the conditions for their joint development have not been studied (but see Erwin & Miller, 1995, for independent work on the same question) in contrast to competitive Hebbian models (Obermayer, Blasdel, & Schulten, 1992). In this article, we provide insight into why this has been the case: OR and OD decouple in symmetric CBL models, and a joint development of OR and OD is possible only in a parameter regime that depends on nonlinear mechanisms.