Final answer:
To track lines of bacterial descendants, scientists use RNA, DNA, and protein analysis, constructing phylogenetic trees or cladograms that may be complicated by horizontal gene transfer. They employ computer-based algorithms and combine both genetic and morphological data to accurately determine evolutionary relationships.
Step-by-step explanation:
To visualize or track one line of descendants from one bacterium versus another, scientists utilize a variety of techniques including RNA, DNA, and protein analysis. Advances in computational technology have been instrumental in helping us understand phylogenetic relationships and construct comprehensive phylogenetic trees. With the discovery of horizontal gene transfer complicating evolutionary pathways, some scientists have shifted from a simplistic "tree of life" concept to a more intricate "web of life" approach. This is because gene transfer among different species can obscure direct ancestral lines, creating a network of genetic relationships rather than a straightforward tree.
When determining phylogenetic relationships, researchers often create cladograms or phylogenetic trees that illustrate these relationships. These diagrams can be rooted, representing a common ancestor, or unrooted, showing relative relationships without a starting point. In the case of bacteria, scientists may use Hfr (high-frequency recombination) strain mapping to follow the gene transfer during bacterial mating, allowing them to chart the lineage and evolution of particular gene segments.
However, the occurrence of random similarities in DNA sequences between distantly related organisms can misrepresent ancestral relations. To mitigate this, scientists use computer-based statistical algorithms that, alongside morphological data, provide a more accurate representation of evolutionary relationships. While considering genetic commonalities, the actual relationship between organisms can only be reliably ascertained with these advanced technological tools and a dual approach combining both genetic and morphological data.