About Systems Biology

Systems Biology is quickly replacing Molecular Biology as the most encompassing field of life science. It has become a fashion to begin a scientific lecture by "nobody knows what is systems biology but this is what I will talk about in the next x minutes anyway". I positively hate that. And I advise anybody hearing that sentence to leave the conference room. Why would-you waste time attending a conference from somebody who admit being totaly incompetent up-front?

After having witnessed the birth (some would say the revival) of Systems Biology, allow-me to tentatively give my (simple) definition of it:

Systems Biology is the study of the emerging properties of a biological system, taking into account all the necessary constituents, their relationships and their dynamics.

That's it. Nothing more. It seems trivial, but it is actualy a new paradigm (in the Kuhnian sense), or framework of thought, in explanatory biology. Physiology was the first paradigm in a Biology trying to explain the world rather than merely describe it. Born at the end of the XVIIIth century and flourishing from 1850s (Claude Bernard) up to 1970s (neurophysiology), physiology tries to understand a living system by studying it in its entirety, as it is working. Physiology was replaced, as a paradigm, by Molecular Biology, born out of Genetics meeting Biochemistry in mid-XXth century (you will have understood that by Molecular Biology I do not mean only the use of DNA recombinant technology, as it is sometimes used for). Molecular Biology tries to disassemble biological systems to observe, and understand, the components separately. While Molecular Biology was very successful in the 50s and 60s (Watson and Crick, Monod and Jacob ...), it really became the paradigm only at the end of the 70s, with the use of recombinant DNA. Systems Biology is actually only one facet of a general paradigm shift in all aspects of science, appearing around the second world war and encompassing cybernetics, information theory, multi-agent systems etc, triggered by the study of an ant nest, a boeing 747, the internet or facebook. Systems Biology was kept dormant by the lack of quantitative data and computational power. These roadblocks disappeared in the year 1990s. However, Systems Biology is not restricted to computational modelling, as we often hear, although modelling is a very important, if not mandatory, tool to achieve its goals. Moreover, Systems Biology does not apply only to large-scale studies with many components. The study of a pathway made up of two enzymes is Systems Biology. One will often find that the steady-state of the systems is far from the equilibrium of any of the enzymes, and thus unpredictable only by observing the enzymes separately.

Systems Biology approaches are different from reductionist approaches, although they are based on them. Indeed, the elements of a system being considered together rather than in isolation, they can influence each other and function off the optimal range they would display is observed separately. Systems Biology approaches are also different from phenomenology, because they try to reveal mechanistic explanations, and allow for emergent properties. A typical phenomenological approach will take a vector of input, a vector of output and try to describe the system function as a mathematical transformation between those vectors. That is based on a very heavy hypothesis: That our set of input vectors samples the whole universe of possible inputs... Let's take another example extracted from neuroscience: the formal neurons such as the famous McCulloch-Pitts neurons or more complex such as integrate and fire neurons. Those representations do not try to represent the biophysical reality of the biological neuron, but rather aim to reproduce their behaviour. They are based on the assumption that the signal travels unidirectionally. However, we now know that in the dendrites (the receptive part of th neuron), action potentials are going both ways, and this is very important in plasticity phenomena. As a consequence, while those formal neurons remain tremendous computational tools, and very useful to study the function of networks, they do not teach us anything about the way biological neurons function, and can hardly be used to build realistic models.

A very important side-effect of Systems Biology is that to study a process of level N, we need to consider the function of elements of level N-1. This is very different from reductionism and phenomenology, that both rely on elements of level N to explain processes of the same level N. For instance, to understand a cellular process, we need to rely on molecular function; to understand the function of an organ, we need to take into accound the characteristic of the cells forming the tissues of the organ. Of course that also means the knowledge needed by a systems biologist necessary covers several fields ...