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In order to achieve a comprehensive understanding of complex biological systems, researchers must develop new techniques that incorporate key features of the system across all relevant spatial and temporal scales. Recent advances in molecular biology and genetics have generated a wealth of experimental data that provides details with respect to gene-expression patterns and individual gene and protein functions, but integration of this information into meaningful knowledge of the complete system is a challenge borne by a new scientific era dependent on computational tools. In this paper, we review new computational techniques, developed to reconstruct single-cell biochemical networks for generating quantitative descriptions of network properties, and agent-based models designed to study multicell interactions important in tissue patterning. We also discuss the challenges and promises of combining these approaches in a single quantitative framework for advancing medical care for diseases that arise from a multitude of factors.
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