dio十六夜:用“复杂性”的眼光看待生命科学问题--Redondo日记本

Complexity in biology.

Exceeding the limits of reductionism and determinism using complexity theory 

The ultimate aim of scientific research is to understand the natural world. In order to achieve this goal, Western science has relied on different cognitive strategies, including simplification, both in terms of analysis and explanation. As the British natural philosopher Sir Isaac Newton (1643–1727) put it, "Truth is ever to be found in the simplicity, and not in the multiplicity and confusion of things." In a way, examples of simplification include using idealized models, such as a 'perfect sphere rolling down a smooth plane in a vacuum'; conducting experiments in a strictly controlled environment such as the laboratory; analysing complex systems by reducing them to their individual parts; and generally by using a linear and deterministic concept of how the world, including life, works.

The French philosopher and mathematician René Descartes (1596–1650) was the first to introduce reductionism to Western thinking and philosophy. According to his view, the world can be regarded as a clockwork mechanism; to understand it, one need only investigate the parts and then reassemble each component to recreate the whole. Descartes' work was expanded by Newton (1643–1727) and ultimately culminated in the Principia Mathematica in 1687—one of the most influential science books ever written—in which Newton further advanced the idea of a 'clockwork universe'.

Since the time of Newton, classical mechanics has been regarded as the foundation of scientific research. Scientists, including biologists, have adopted the Newtonian approach both at the ontological level—in terms of their conception of the world and the things of which it is made—and the epistemological level—in terms of their approach to understanding those things. The Newtonian epistemology, in fact, states that scientific knowledge has to provide an objective representation of the external world. The world's apparent complexity can be resolved by analysis and reducing phenomena to their simplest components. "Once you have done that, [the evolution of phenomena] will turn out to be perfectly regular, reversible and predictable, while the knowledge you gained will merely be a reflection of that pre-existing order" (Heylighen et al, 2007).

Ever since Newton formulated the first laws of gravity, the conceptual model of the physical world had successfully described the shape, movements and actions of the objects within it. But as physicists began to explore especially the atomic and subatomic realms in the early twentieth century, their observations became partially meaningless. The new discoveries required a paradigm shift and a new intellectual framework to understand events at the subatomic level, which eventually resulted in quantum physics.

Since the time of Newton, classical mechanics has been regarded as the foundation of scientific research

As many of the molecular biologists in the 1950s came from physics, it is not surprising that they extended its classical approach to the study of living organisms. Molecular biology, with some exceptions (Westerhoff & Palsson, 2004), has largely adopted a reductionistic view to explain biological systems according to the physical and chemical properties of their individual components. As Francis Crick (1916–2004) put it, "The ultimate aim of the modern movement in biology is to explain all biology in terms of physics and chemistry" (Crick, 1966). In due course, reductionism proved to be an extremely powerful analytical methodology and it enabled scientists to analyse many basic molecular and cellular processes.

Complex systems exist at different levels of organization that range from the subatomic realm to individual organisms to whole populations and beyond

Nonetheless, biologists might be reaching the limits of this approach. Despite their best efforts, scientists are far from winning the war on cancer, owing largely to the complex nature of both the disease and the human organism. The human brain is a complex, nonlinear system that defies all reductionistic and deterministic attempts to understand it (Singer, 2007). On a macro level, ecosystems and human societies present the same challenge. What is needed is a new approach to study these systems. Complexity theory can provide new conceptual tools that will inevitably question many of the assumptions of Newtonian science.

Complex systems exist at different levels of organization that range from the subatomic realm to individual organisms to whole populations and beyond. They include, for example, molecules, cells, organisms, ecosystems and human societies. Despite their differences, these all share common features, such as emergent properties. In addition, randomness and order are both relevant for the behaviour of the overall system. They are, in fact, neither typified by complete determinism, such as the phenomena that are investigated by Newtonian mechanics, nor by total randomness, such as the subjects of statistical mechanics (Heylighen et al, 2007). Complex systems exist on the 'edge of chaos'. They might show regular and predictable behaviour, but they can undergo sudden massive and stochastic changes in response to what seem like minor modifications. The metaphor of the 'butterfly effect'—whereby a single butterfly beating its wings can cause a storm—describes, for example, the dependence of a complex system on its initial conditions.