![]() The difference is called information gain 6, 7, 8. We have recently been developing a method for quantifying signal transduction involving the Kullback–Leibler divergence (KLD), a relative entropy that expresses the average difference in information entropy before and after a signal event. Using this framework allows researchers to identify the information transmission strategies used by cells 3, 4, 5. Many pioneering studies recently reported that information science theory is a powerful framework for quantitatively understanding signal transduction 2. A well-known example is the epidermal growth factor (EGF)-driven signal cascade in cancer cells 1. Signal transduction systems are unique chain reactions involving signaling molecules in biological systems. In conclusion, ST and STR are promising properties for quantitative analysis of signal transduction. Furthermore, signal molecules with similar STRs may form a signal cascade. Thus, signaling transduction systems may adopt a strategy that prioritizes the maximization of ST. The results were consistent with those from the theoretical analysis. MATHEMATICA VECTORS SKINTo experimentally verify this theoretical conclusion, we measured the STR of the epidermal growth factor (EGF)-related cascade in A431 skin cancer cells following stimulation with EGF using antibody microarrays against phosphorylated signal molecules. We previously reported that if the total ST value in a given signal cascade is maximized, the ST rate (STR) of each signaling molecule per signal duration (min) approaches a constant value. The average information gain can be regarded as the signal transduction quantity (ST), which is identical to the Kullback–Leibler divergence (KLD), a relative entropy. The information gain during a signal event is given by the logarithm of the phosphorylation molecule ratio. Cell signaling molecules are phosphorylated in response to extracellular stimuli, with the phosphorylation sequence forming a signal cascade. Many studies have been performed to quantify cell signaling. ![]()
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