This is a truly remarkable time in the biological sciences. Biology now has the opportunity to effectively attack some of the most fundamental problems of society, including healthcare, agriculture, bio-energy, a sustainable environment, and nutrition.
These opportunities are a result of system strategies for probing biological complexity, emerging technologies that are allowing us to explore new dimensions of data space, and the provision of novel analytical tools for analyzing, integrating, and modeling large datasets.
Over the past 40 years, I have done a lot of thinking about biological complexity and these thoughts have directly and/or indirectly led to me to participate in a series of paradigm changes that have transformed how we think about and decipher the complexities of biology and medicine.
There is an active, fascinating, and rapidly growing field that focuses on complex adaptive systems in living organisms, including their emergent properties, robustness, criticality, stochastic, and determinism.
My thinking about complexity initially was centered not on these conceptualizations, but rather appreciating that biological complexity emerges from the principles of Darwinian evolution and the need for enormous amounts of bottom-up data to decipher this complexity.
In this article I will discuss biological complexity and the five paradigm changes in which I have had the good fortune to participate. These changes have led to new strategies and technologies for attacking the complex biological and medical challenges of the 21st century. Successful implementation of these new strategies and technologies will require a balanced national portfolio of big and small science.
Early Thoughts about Complexity
Biological complexity is inherent in Darwinian evolution. The ongoing selection of infrequent genetic mutations continually reshapes the phenotypes of all organisms to respond to new environmental imperatives. These mutations are random and not directed.
Darwinian evolution also proceeds in a modular fashion, e.g., employing highly conserved basic subnetwork patterns to build complex developmental networks. Further, while evolution builds on past successful phenotypes, the criteria for selection are constantly changing with the evolving environment (and genomes for that matter).
For these reasons, the evolutionary path is often extremely convoluted. Stick insects, for example, have evolved and lost wings three different times in the course of evolution—and this capricious history is, in part, recorded in its genome. Accordingly, in biology a search for the simplest solution is rarely the most productive approach as it does not aid in the process of understanding life’s complexity.
This simple observation has enormous implications for how biology is practiced. In fact, it forces biology to focus on the development of new tools for generating and analyzing huge masses of biological data.
The only way to deconvolute this biological complexity is to perturb the systems of the organism (genetically and/or environmentally) and record detailed molecular, cellular, and phenotypic responses—and then separate the signal from significant noise to construct explanations or predictable models. It is this data that reflects the reality of the organism’s complexity and from which complexity can be deciphered.
In short, complexity requires biology to become an information science because it gives us some of the most fundamental concepts for thinking about biological complexity.