Information Transduction Capacity of Noisy Biochemical Signaling Networks

Abstract

Molecular noise restricts the ability of an individual cell to resolve input signals of different strengths and gather information about the external environment. Transmitting information through complex signaling networks with redundancies can overcome this limitation. We developed an integrative theoretical and experimental framework, based on the formalism of information theory, to quantitatively predict and measure the amount of information transduced by molecular and cellular networks. Analyzing tumor necrosis factor (TNF) signaling revealed that individual TNF signaling pathways transduce information sufficient for accurate binary decisions, and an upstream bottleneck limits the information gained via multiple pathways together. Negative feedback to this bottleneck could both alleviate and enhance its limiting effect, despite decreasing noise. Bottlenecks likewise constrain information attained by networks signaling through multiple genes or cells.

Dynamics of TGF-β signaling reveal adaptive and pulsatile behaviors reflected in the nuclear localization of transcription factor Smad4

“…The single-cell data (3) following nuclear localization of NFκB in response to a step in TNF-α show a largely digital and adaptive response (but see ref. 37). The intensity of the step defines the fraction of cells that respond, and the nuclear-localized NFκB returns to near base line in 90 min.…”

Extending the dynamic range of transcription factor action by translational regulation

“…To exploit this concept, as a first step it is necessary to estimate the various information theoretic quantities from data on real systems, and for genetic regulatory networks that has been achieved only very recently. There are estimates of the mutual information between the concentration of a TF and its target gene expression [8,9], the information that expression levels of multiple genes carry about the position of cells in the developing fruit fly embryo [10,11], and the information that gene expression levels provide about external signals in mammalian cells [12,13]. As a second step, we need to understand theoretically how the various features of the systemsthe architecture of signal transmission, the noise levels, the distribution of input signalscontribute to determining information transmission.…”

Information transfer by leaky, heterogeneous, protein kinase signaling systems

“…Understanding the process of information transfer from the biological environment to the nucleus (1) and studying quantitatively how information about the signal is lost along the way are essential in understanding cellular decision-making (2,3). The signal transduction networks used by cells are subject to various sources of noise, and we are only beginning to explore how these affect the process of information transfer (4,5). Here we focus, in the context of protein kinase (PK) signaling, on the little-studied effects of two important types of biological noise: cell-to-cell variability in the componentry of the network and basal network activity.…”

“…Mutual information and information capacity are becoming increasingly important for quantifying information transfer by biochemical systems (4,(24)(25)(26). Mutual information allows the comparison of systems in terms of how well a given signal can be inferred using the Bayesian posterior distribution (27) based on each system's intracellular output.…”