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Allostery, and how to define and measure signal transduction

期刊

BIOPHYSICAL CHEMISTRY
卷 283, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.bpc.2022.106766

关键词

Allosteric; Deep learning; Artificial intelligence; Neurodevelopmental disorders; Signaling; Cellular network

资金

  1. National Cancer Institute, National Institutes of Health [HHSN261201500003I]
  2. Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research

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This article discusses the concept of productive signaling, including its definition, measurement, and determining factors. It highlights the importance of understanding signal propagation in diseases like cancer and neurodevelopmental disorders. The authors propose a framework for investigating signal transduction by defining cellular processes, conducting experimental measurements, and utilizing computational AI algorithms.
Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

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