Systems biology aims to understand biology at the system level by examining the structure and dynamics of cellular and organismal functions, rather than isolated components. It emphasizes properties like robustness, which are crucial for future medical advancements. This field requires breakthroughs in experimental devices, software, and analytical methods to fully realize its potential. Since Norbert Weiner's time, system-level understanding has been a recurring theme in biology, but recent progress in molecular biology, especially genome sequencing and high-throughput measurements, has enabled comprehensive data collection and molecular-level insights. This has created a golden opportunity for system-level analysis grounded in molecular understanding. System-level understanding in systems biology involves shifting the focus from individual genes and proteins to the system's structure and dynamics. While gene and protein lists provide a catalog of components, they are insufficient without understanding how they interact. Network diagrams represent a first step but are static, whereas dynamic interactions are more important. Understanding how components dynamically interact during operation is essential for system function. Key properties of biological systems include network structures, dynamics, control mechanisms, and design principles. Progress in these areas requires advances in computational sciences, genomics, and measurement technologies. Identifying gene-regulatory logic and biochemical networks remains a major challenge, with various methods being developed to automatically discover regulatory relationships from microarray data. Dynamic analysis of cellular systems requires modeling, with careful consideration of the model's purpose and scope. Steady-state analysis and bifurcation analysis help understand system behavior under various conditions. Understanding regulatory circuits and their evolutionary modifications provides insights into design patterns. Robustness is a critical property of biological systems, with three main aspects: adaptation, parameter insensitivity, and graceful degradation. These are achieved through system control, redundancy, structural stability, and modularity, similar to engineering systems. Comprehensive quantitative data is essential for systems-level analysis, requiring high throughput and accurate measurements. Innovations in experimental devices, single-molecule measurements, and nano-technologies are crucial. Software infrastructure, such as SBML and SBW, supports model exchange and simulation. Systems biology impacts pharmaceutical industries and medical practice by enabling detailed models of cell regulation, aiding in drug discovery and predicting systemic side effects. It may lead to simulation-based screening of therapeutic agents, similar to structural dynamics analysis for buildings. Systems biology is in its infancy but holds significant potential in both scientific and practical terms, promising to revolutionize our understanding of complex biological systems and provide new opportunities for practical applications.Systems biology aims to understand biology at the system level by examining the structure and dynamics of cellular and organismal functions, rather than isolated components. It emphasizes properties like robustness, which are crucial for future medical advancements. This field requires breakthroughs in experimental devices, software, and analytical methods to fully realize its potential. Since Norbert Weiner's time, system-level understanding has been a recurring theme in biology, but recent progress in molecular biology, especially genome sequencing and high-throughput measurements, has enabled comprehensive data collection and molecular-level insights. This has created a golden opportunity for system-level analysis grounded in molecular understanding. System-level understanding in systems biology involves shifting the focus from individual genes and proteins to the system's structure and dynamics. While gene and protein lists provide a catalog of components, they are insufficient without understanding how they interact. Network diagrams represent a first step but are static, whereas dynamic interactions are more important. Understanding how components dynamically interact during operation is essential for system function. Key properties of biological systems include network structures, dynamics, control mechanisms, and design principles. Progress in these areas requires advances in computational sciences, genomics, and measurement technologies. Identifying gene-regulatory logic and biochemical networks remains a major challenge, with various methods being developed to automatically discover regulatory relationships from microarray data. Dynamic analysis of cellular systems requires modeling, with careful consideration of the model's purpose and scope. Steady-state analysis and bifurcation analysis help understand system behavior under various conditions. Understanding regulatory circuits and their evolutionary modifications provides insights into design patterns. Robustness is a critical property of biological systems, with three main aspects: adaptation, parameter insensitivity, and graceful degradation. These are achieved through system control, redundancy, structural stability, and modularity, similar to engineering systems. Comprehensive quantitative data is essential for systems-level analysis, requiring high throughput and accurate measurements. Innovations in experimental devices, single-molecule measurements, and nano-technologies are crucial. Software infrastructure, such as SBML and SBW, supports model exchange and simulation. Systems biology impacts pharmaceutical industries and medical practice by enabling detailed models of cell regulation, aiding in drug discovery and predicting systemic side effects. It may lead to simulation-based screening of therapeutic agents, similar to structural dynamics analysis for buildings. Systems biology is in its infancy but holds significant potential in both scientific and practical terms, promising to revolutionize our understanding of complex biological systems and provide new opportunities for practical applications.