Signalmolekülnetz

Signalmolekülnetz is the network of signaling molecules and their interactions that govern cellular communication in biological systems. It encompasses ligands (signaling molecules), receptors, intracellular signaling proteins, second messengers, and transcription factors. These networks operate at multiple scales, from intracellular cascades to intercellular communication via autocrine, paracrine, and endocrine signaling, coordinating processes such as development, metabolism, immunity, and neural activity. Information flows through the network when ligands bind receptors and trigger cascades, leading to outcomes such as kinase activation, production of second messengers like cAMP or Ca2+, and changes in gene expression.

Graphs are commonly used to represent the signalmolekülnetz, with nodes as molecules or complexes and edges as activating or inhibiting interactions. Dynamic modeling uses differential equations or logical/Boolean approaches to capture temporal evolution and dose-response behavior. Experimental data from omics, imaging, and single-cell analysis feed into network reconstruction and inference.

Key properties include topology, motifs (such as feedback and feedforward loops), redundancy and crosstalk between pathways, and context dependence that yields different outcomes in different cell types or conditions. Spatial organization and temporal dynamics add further complexity.

Applications include understanding development and homeostasis, elucidating disease mechanisms (for example cancer or autoimmune disorders), and guiding drug discovery and combination therapies. Challenges include incomplete knowledge, measurement noise, and the difficulty of integrating multi-scale data.

Examples of signalmolekülnetz components are receptor tyrosine kinases linking to MAPK and PI3K pathways, cytokine signaling networks in the immune system, and neurotransmitter signaling networks in the nervous system. See also: signal transduction, systems biology, network biology, pathway analysis.