The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium ( Escherichia coli ) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers’ continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

This study was designed to investigate the toxic effects of benzo (a) pyrene (BaP) in the lungs. Mice were repeatedly treated orally with BaP (50 mg/kg body weight, twice a week for four weeks) to induce a tumour. After 4 months of BaP administration, tumours were visible beneath the skin. The histopathological section of the lungs shows congestion of pulmonary blood vessels, alveolar hyperplasia, and concurrent epithelial hyperplasia with infiltrates of inflammatory cells also seen. Thereafter, a single-cell suspension of lung tissues was stained with fluorescently conjugated antibodies for the demarcation of alveolar epithelial (anti-mouse CD74 and podoplanin) and macrophage (F4/80 and CD11b) cells and measured by flow cytometry. The expression of antioxidant genes was assessed by qRT–PCR. The number of alveolar epithelial cells 1 (AEC1) increased, but the number of alveolar epithelial cells 2 (AEC2) and transitional alveolar epithelial cells (TAEC) was significantly decreased in tumour-bearing mice. The proportion of CD11b⁺ alveolar macrophages (AM) and interstitial macrophages (IM) was increased, but the proportion of F4/80⁺ AM cells was reduced. The BaP administration significantly increased the ROS production in alveolar cells. The relative expression levels of antioxidant genes (SOD1, catalase, GPX1, and HIF-1α) were increased, but NRF2 expression was decreased in BaP-treated alveolar cells. The expression of anti-inflammatory (NF-κB) was also significantly increased. In conclusion, BaP exposure induced an inflammatory response, altered alveolar epithelial cell and macrophage diversity, and increased antioxidant responses in the lungs.