Abstract
These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied “the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer”. It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems–biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).
Highlights
Received: 16 November 2021Accepted: 15 December 2021Published: 23 December 2021Publisher’s Note: MDPI stays neutralLet me first describe the status of the quantitative description of biology.I will review tools from systems biology (SB)
The systems view dictates that “one should target network state resulting from gene/protein networks and their emergent behavior, rather than individual genes or proteins as a new strategy for drug discovery, suggesting that we explore multi-target drugs or non-additive combination therapies” [20]
flux balance analysis (FBA) compares in silico system simulations to experimental results [27], and as such it is an indispensable tool in cancer biology
Summary
Let me first describe the status of the quantitative description of biology (and cancer). The central dogma of SB is that it is the dynamic interactions of molecules and cells that give rise to biological function (emergent property) via computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, cell–cell interactions, etc.) Both arms of exploration are needed, absolutely. Many phenomena at subcellular/cellular levels that are typically described in biochemistry and cell biology textbooks can be considered as phenomena resulting from the interaction between different compartments and hierarchies (e.g., cell growth, division/proliferation, apoptosis, cell activation, homeostasis, cell death, differentiation, system robustness, redundancy, multiplicity of steady-states, hysteresis, oscillations, structural hierarchy, consciousness, self-organization, evolution, self-awareness, etc.) The latter hierarchies probably fall into the category of first principles in biology.
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