So sounds like you don’t need to know anything and there is no prerequisite in order to do life science studies. This is true to some extent. Otherwise you would not see there are so many middle school or high school students spending their summer doing life science research in some labs. The only one thing I think is useful for life science or you can learn from life science is the design of experiments. This is probably the only thing in life science that shares something in common with other disciplines. What makes you stand out in life science is not how well you are doing for the course work, but how well you know about using different kind of experiment instruments, and your experiences of different kind of life science experiments. These knowledge and skills are highly domain specific, and they do not apply to other disciplines.
Wait, how about data analysis? Can we learn data analysis from life science? In life science, the experiments could be categorized based on the size of data you generated. For experiments generating small amount of data, usually it is too simple to analyze, and you would learn nothing. Compute the mean and standard deviation of the samples, analyze whether there is any statistical difference between the control group and experiment group. Because usually the students and even the professors do not know too much about statistics, they often made mistakes in choosing the right statistical methods for analysis, thus resulting in error-prone conclusions. This is called “You don’t know what you were doing”. For experiments generating large amount of data, such as genome sequencing experiments, it is usually handled and processed by professional software. Essentially you got results magically from a black-box software without knowing what the underlying analytical algorithms are. This is called “You don’t know what it was doing”.