Abstract
Information theory is gaining popularity as a tool to characterize performance of biological systems. However, information is commonly quantified without reference to whether or how a system could extract and use it; as a result, information-theoretic quantities are easily misinterpreted. Here, we take the example of pattern-forming developmental systems which are commonly structured as cascades of sequential gene expression steps. Such a multi-tiered structure appears to constitute sub-optimal use of the positional information provided by the input morphogen because noise is added at each tier. However, one must distinguish between the total information in a morphogen and information that can be usefully extracted and interpreted by downstream elements. We demonstrate that quantifying the information that is accessible to the system naturally explains the prevalence of multi-tiered network architectures as a consequence of the noise inherent to the control of gene expression. We support our argument with empirical observations from patterning along the major body axis of the fruit fly embryo. We use this example to highlight the limitations of the standard information-theoretic characterization of biological signalling, which are frequently de-emphasized, and illustrate how they can be resolved.
Highlights
As an inspiring example of productive collaboration between computer science, physics and biology, information theory is2015 The Authors
In biological applications of information theory, the information content is usually assessed for signals that constitute intermediate steps, most commonly transcription factors, for example, NF-κB [6,7] or Drosophila patterning cues [8,9]
The Drosophila patterning network has been described as performing a ‘transition from analogue to digital specification’ of cell identity [39]
Summary
As an inspiring example of productive collaboration between computer science, physics and biology, information theory is2015 The Authors. In the direct strategy (2.1), the application of GNeff reduces the noise to σ0/ Neff and so the controlling signal c(0) carries Ir(0a)w = I(cmax/(σ0/ Neff)) bits of raw information.
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