Boris N. Kholodenko reviews the dynamics of cellular signaling in time and space. He emphasizes that the specificity of cellular responses to receptor stimulation is determined by the temporal and spatial dynamics of downstream signaling networks. Computational models help understand the complex relationships between stimuli and responses, revealing mechanisms that allow networks to amplify signals, reduce noise, and generate discontinuous bistable dynamics or oscillations. These temporal dynamics are coupled to spatial gradients of signaling activities, which guide key intracellular processes but also require mechanisms to facilitate signal propagation across a cell. Cells respond to external cues using a limited number of signaling pathways activated by plasma membrane receptors, such as G protein-coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs). These pathways process, encode, and integrate internal and external signals. Distinct spatio-temporal activation profiles of the same signaling proteins result in different gene activation patterns and diverse physiological responses. Pivotal cellular decisions, such as cytoskeletal reorganization, cell cycle checkpoints, and apoptosis, depend on the precise temporal control and spatial distribution of activated signal-transducers. RTK signaling is central to embryogenesis, cell survival, motility, proliferation, differentiation, glucose metabolism, and apoptosis. Malfunction of RTK signaling causes major human diseases. Upon stimulation, RTKs undergo dimerization or allosteric transitions, activating intrinsic tyrosine kinases. Subsequent phosphorylation of multiple tyrosine residues transmits a biochemical message to cytoplasmic proteins, triggering their mobilization to the cell surface. The resulting cellular responses occur through complex biochemical circuits of protein interactions and covalent-modification cascades. An emerging picture of interconnected networks has replaced the earlier view of discrete linear pathways. Both GPCRs and RTKs activate kinase/phosphatase cascades, such as mitogen-activated protein kinase (MAPK) cascades, that turn on nuclear transcription factors. Specificity is determined by the temporal and spatial dynamics of downstream signaling components. The classical example is the distinct biological outcome of PC12 cell stimulation with EGF and nerve growth factor (NGF). EGF-induced transient MAPK activation results in proliferation, whereas sustained MAPK activation by NGF changes the cell fate and induces differentiation. Mechanistic models can reveal crucial regulations. Since the 1990s, modeling has emerged as a novel tool to handle the rapidly growing information on the molecular parts list and the overwhelmingly complex interaction circuitry of signaling networks. These models aim to create in silico replicas of cellular networks to understand the temporal dynamics of signaling responses. The first mechanistic model of the EGFR network was published in 1999 and explained the temporal dynamics of signaling responses in liver cells stimulated with EGF. Challenges in mechanistic modelling include the lack of quantitative kinetic data and the combinatorial increase in the number of emerging distinct species and states of the protein network being simulated. The firstBoris N. Kholodenko reviews the dynamics of cellular signaling in time and space. He emphasizes that the specificity of cellular responses to receptor stimulation is determined by the temporal and spatial dynamics of downstream signaling networks. Computational models help understand the complex relationships between stimuli and responses, revealing mechanisms that allow networks to amplify signals, reduce noise, and generate discontinuous bistable dynamics or oscillations. These temporal dynamics are coupled to spatial gradients of signaling activities, which guide key intracellular processes but also require mechanisms to facilitate signal propagation across a cell. Cells respond to external cues using a limited number of signaling pathways activated by plasma membrane receptors, such as G protein-coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs). These pathways process, encode, and integrate internal and external signals. Distinct spatio-temporal activation profiles of the same signaling proteins result in different gene activation patterns and diverse physiological responses. Pivotal cellular decisions, such as cytoskeletal reorganization, cell cycle checkpoints, and apoptosis, depend on the precise temporal control and spatial distribution of activated signal-transducers. RTK signaling is central to embryogenesis, cell survival, motility, proliferation, differentiation, glucose metabolism, and apoptosis. Malfunction of RTK signaling causes major human diseases. Upon stimulation, RTKs undergo dimerization or allosteric transitions, activating intrinsic tyrosine kinases. Subsequent phosphorylation of multiple tyrosine residues transmits a biochemical message to cytoplasmic proteins, triggering their mobilization to the cell surface. The resulting cellular responses occur through complex biochemical circuits of protein interactions and covalent-modification cascades. An emerging picture of interconnected networks has replaced the earlier view of discrete linear pathways. Both GPCRs and RTKs activate kinase/phosphatase cascades, such as mitogen-activated protein kinase (MAPK) cascades, that turn on nuclear transcription factors. Specificity is determined by the temporal and spatial dynamics of downstream signaling components. The classical example is the distinct biological outcome of PC12 cell stimulation with EGF and nerve growth factor (NGF). EGF-induced transient MAPK activation results in proliferation, whereas sustained MAPK activation by NGF changes the cell fate and induces differentiation. Mechanistic models can reveal crucial regulations. Since the 1990s, modeling has emerged as a novel tool to handle the rapidly growing information on the molecular parts list and the overwhelmingly complex interaction circuitry of signaling networks. These models aim to create in silico replicas of cellular networks to understand the temporal dynamics of signaling responses. The first mechanistic model of the EGFR network was published in 1999 and explained the temporal dynamics of signaling responses in liver cells stimulated with EGF. Challenges in mechanistic modelling include the lack of quantitative kinetic data and the combinatorial increase in the number of emerging distinct species and states of the protein network being simulated. The first