Beyond reductionism – systems biology gets dynamic

Steven Hertzberg on LinkedIn


“Real biological systems – in the wild, as it were – simply don’t behave as they do under controlled lab conditions that isolate component pathways. They behave as systems – complex, dynamic, integrative systems. They are not simple stimulus-response machines. They do not passively process and propagate signals from the environment and react to them. They are autopoietic, homeostatic systems, creating and maintaining themselves, accommodating to incoming information in the context of their own internal states, which in turn reflect their history and experiences, over seconds, minutes, hours, days, years, and which even reflect the histories of their ancestors through the effects of natural selection.”


“Enactivism sees organisms as creating their own reality through dynamic interaction with their environment, assimilating information about the outside world into their own ongoing dynamics, not in a reflexive way, but through active inference, such that the main patterns of activity remain driven by the system itself. This perspective is well described by Varela, Thompson and Rosch, and developed by Evan Thompson in his 2007 book Mind in Life, and by others, including Alicia Juarrero (Dynamics inAction) and Andy Clark (Surfing Uncertainty), for example.”


Beyond reductionism – systems biology gets dynamic
By Kevin Mitchell – September 14, 2019

Is biology just complicated physics? Can we understand living things as complex machines, with different parts dedicated to specific functions? Or can we finally move to investigating them as complex, integrative, and dynamic systems?

For many decades, mechanistic and reductionist approaches have dominated biology, for a number of compelling reasons. First, they seem more legitimately scientific than holistic alternatives – more precise, more rigorous, closer to the pure objectivity of physics. Second, they work, up to a point at least – they have given us powerful insights into the logic of biological systems, yielding new power to predict and manipulate. And third, they were all we had – studying entire systems was just too difficult. All of that is changing, as illustrated by a flurry of recent papers that are using new technology to revive some old theories and neglected philosophies.

The central method of biological reductionism is to use controlled manipulation of individual components to reveal their specific functions within cells or organisms, building up in the process a picture of the workings of the entire system. This approach has been the mainstay of genetics, biochemistry, cell biology, developmental biology, and even neuroscience. When faced with a system of mind-boggling complexity, it makes sense to approach it in this carefully defined, controlled manner. In any case, in most of these fields it was technically only possible to manipulate one or a few components at a time and only possible to measure their effects on one or a few components of the system.

The productivity of reductionist methods, and the lack of viable alternatives, brought with it a widespread but often tacit commitment to theoretical reductionism – the idea that the whole really is not much more than the sum of its parts. Appeals to holism seem to many biologists not just out of reach technically, but somehow vague, fuzzy, and unscientific. We are trained to break a system down to its component parts, to assign each of them a function, and to recompose the systems and subsystems of organisms and cells in an isolated, linear fashion.

We can see this in genetics, with the isolation of a gene for this or a gene for that. Or in signal transduction, with the definition of linear pathways from transmembrane receptors, through multiple cytoplasmic relays, to some internal effectors. Or in neuroscience, with the assignment of specific and isolated functions to various brain regions, based on lesion studies or activation in fMRI experiments.

The trouble is that is not how cells and organisms work. Defining all these isolated functions and linear pathways has been productive, but only from a certain perspective and only up to a point. This enterprise has mostly depended on analysing responses to strong experimental manipulations – a trusted method to perturb the system but one that is inherently artificial (what Francis Bacon, the so-called father of empiricism, called “vexing nature”)*. And it has mostly analysed effects on limited, pre-defined readouts.

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