A computational biologist is studying a population of 3 distinct bacterial strains labeled A, B, and C. If a sample of 5 bacteria is taken such that at least one bacterium from each strain is included, how many different samples can be formed?

Thu, 07 May 2026 21:34:22 +0000

**Why the Study of Bacterial Strains in Population Samples Matters—And How Many Samples Are Possible** In an era of growing interest in microbial diversity and personalized medicine, a central question keeps emerging in scientific and health-focused conversations: how do we capture a meaningful snapshot of a bacterial population while ensuring representation across all types? Take a case study involving three distinct bacterial strains—labeled A, B, and C—used by researchers to explore genetic variation in microbial communities. When analyzing samples of five bacteria drawn from this population, a key requirement arises: at least one representative from each strain must be included. This constraint ensures a more complete understanding of strain distribution, which has practical implications for everything from environmental monitoring to clinical diagnostics. A classic combinatorics problem emerges: how many unique samples of five bacteria can include at least one of each strain? The answer lies not in guesswork, but in structured counting methods that reflect the complexity of biological systems. **Why This Problem Is Gaining Attention in the US** Interest in microbial genomics continues to rise amid heightened public awareness of gut health, antibiotic resistance, and personalized medicine—trends amplified by recent science reporting and digital health communities across the United States. Researchers and bioinformaticians increasingly rely on precise sampling strategies to track microbial dynamics, drive innovation in diagnostics, and inform treatment development. When presented with a scenario requiring at least one bacterium from each of three strains in a sample of five, understanding how many feasible combinations exist becomes essential. This question isn’t just academic; it reflects the real-world challenge of sampling diversity efficiently and meaningfully. The structured approach to solving such problems resonates with both scientific professionals and curious readers seeking clarity in complex biological data.