The resulting statistical model was used to predict the expressio

The resulting statistical model was used to predict the expression patterns driven by 8008 candidate CREs, and a subset of these predictions was then tested with a high degree of success. This study shows that the binding patterns of a small number of TFs to CREs are sufficient to predict their spatio-temporal activity and emphasizes the capacity of different TF binding patterns to yield the same expression output. It also provides a way to predict the functional consequences of changes in TF binding, which is observed even over short evolutionary timescales [ 36]. This approach may also be effective for prediction at finer scales of resolution, by making use of binding

data for more Palbociclib supplier TFs and annotations of CRE activity at cellular resolution. The examples above illustrate that a systems approach to Bleomycin investigating TRNs can address biological problems at multiple scales, from a physical model of gradient formation at the molecular level, to rules for CRE architecture at the binding site level, to a statistical model for predicting the tissue-level expression of new CREs. The three studies contend with an increasing number of components, from a single TF, to a handful of TFs controlling a single CRE, to a handful of TFs controlling many CREs. They also occur at increasingly later developmental

time points, as the embryo itself becomes more complex. The computational frameworks needed to CYTH4 answer the questions that are posed in these studies require data of different breadths and resolutions. Notably, the data sets used in each study decrease in spatial and temporal resolution as they increase in the number of components, from single particle resolution at ∼8 min intervals, to cellular resolution at ∼10 min intervals, to tissue and embryo resolution data at ∼2 h intervals; yet they are all successful in providing a satisfying answer to the questions they pose. These differences in data type emphasize that

only the appropriate amount of detail should be included in an effective computational framework. Though not addressed directly in each study, the results also provide a computational framework that can be used to contextualize morphological or genetic variability within and between species. Comparing insights from studies of different TRNs may shed light on how they are designed to accommodate different timescales, tissue types and output requirements. Many other TRNs have attractive features for systems-level studies, summarized in Table 1. The relevant players for these TRNs are largely known (Parts). Many of them give rise to a discrete number of morphologically distinct cell types, which may facilitate quantitating network output (Cell types). Some TRNs produce structures precisely, while the output of others is more variable (Precision).

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