I'm taking a Bioinformatics class and have been reading "System Modeling in Cellular Biology" by Szallasi et. al. Here are the thoughts that I have had while reading the first few chapters.
Data-driven versus hypothesis-driven research
The world is very complex. Science has been used to understand how things work. Science has often been driven by a hypothesis followed by experimentation which then increases our understanding of the problem --- these questions were based on what we observe or maybe a few researchers have observed. Recently, we continue to gather more and more data which also can be used to drive research --- these questions are based not only on what we might observe in life, but additionally on what the data suggests, in some instances of millions of people. Both ways of attacking the problem can lead us to the same truth, however, it seems that the later has more potential of getting us there quicker.
Modeling is constantly used in biological research. Szallasi mentions a couple reasons why models might be useful (1) testing whether a model is accurate and relect known facts, and (2) models can help us to understand which parts of the system contribute most to some desired properties of interest.
I love how robust and resilient biological processes are. I would love to be able to create a computer program that is a fraction as robust as, say the body is at healing itself.
Many biological processes are modular, much like how good programmers would make a function or class. For example, the human kidney can be substituted into another person and it can work successfully in them. Likewise, in programming, code that connects to a database can be used interchangeably withing multiple programs.
Bottom-up versus Top-down approaches
Bottom-up approaches typically build on existing biological knowledge, whereas, top-down approaches leverage the enormous amount of biological data to find something important to then delve into.