Complexity of life sciences in quantum and AI era
Pyrkov et al. (2024): Complexity of life sciences in quantum and AI era
The authors "provide a theoretical framework to orient researchers around key concepts of how quantum computing can be integrated into the study of the hierarchical complexity of living organisms and discuss recent advances in quantum computing for life sciences" .
Like many papers, this paper also contains a remark on different approaches towards biological modelling:
It is worth mentioning that in the scientific community and in particular, in life sciences, there are two distinct paradigms to modeling: first principles theory modeling and data-driven modeling. Until recently, life sciences have been driven mostly by a first principles theory modeling paradigm. This paradigm consists of observing biological phenomena, suggesting hypotheses as to why this phenomenon appears and how it works, constructing experiments to verify the hypotheses, and developing approximate mathematical models from first principles to explain them. Nevertheless, it is often only possible to observe a fraction of the biology, and due to an incomplete understanding of biology on various levels ranging from omics to entire organisms, only a small portion of the underlying phenomena can be modeled accurately. To make the simulation of phenomena computationally tractable, it is necessary to make additional assumptions and approximations, which result in further loss of biological and physical accuracy. In contrast, a new data-driven modeling paradigm has emerged in recent years that relies solely on data and advanced computing capabilities. It manifests that data contains all the biology and physics driving a particular process and hence even without any knowledge of the governing biological and physical laws, it is possible to create models for this process. This paradigm is currently gaining significant attention because of the substantial availability of unprecedented amounts of data, state-of-the-art machine learning and data analytics libraries that are openly accessible, and high-performance computational resources.
There are three aspects that make this endeavour complex:
First, effective modeling tools must also consider the scaling behavior of living systems from individual cells to entire organisms. [...] Second, algorithmic complexity, many traditional techniques in life sciences at each scale have a superpolynomial complexity. [...] The third aspect of complexity in life sciences is data complexity, the problem of big data in some cases and small data in other cases, which limits the effectiveness of modeling.
The paper further contains sections on quantum computing, providing a general introduction, and the algorithmic and data complexity of life sciences with quantum computing.