Network analysis to elucidate natural system dynamics, diversity and performance
- David Leitner (University of Nevada, Reno, USA)
- Claire Lesieur (CNRS, Univ. Lyon, France)
- Luisa Di Paola (University Campus Biomedico Rome, Italy)
- Alessandro Giuliani (''Sapienza’ University of Rome, Italy)
- Elena Papaleo (Danish Cancer Society Research Center - Denmark, Denmark)
Omics-Data has brought into light the fact that nothing is unique in living systems (1). Whatever the scales, the building blocks of life are based on diversity and whatever collective acts they orchestrate, they use many alternative approaches (2).
The diversity lies at the level of individual elements and at the level of their connectivity:
• Chemical information: gene sequences, protein sequences (sequence variants) (3)
• Shape information: cellular morphologies, protein structures, RNAs, DNAs (4)
• Functional dynamics: Molecular dynamics, interactions and thermodynamics
• Collective acts: signaling pathways, metabolisms, protein folding, allostery
Diversity underpins natural system performances bearing robustness and adaptability to perturbations, and supporting rescue and compensatory mechanisms (5). The diversity encompasses notions like combinatorial solutions, alternative solutions and backups and embeds spatial optimization, non-linearity and dynamics. Natural systems also fail and pathologies result from chemical errors (e.g. sequence variants, environmental changes), structural errors, and impaired functions. Thus, just as robustness and adaptability, system failure is born out of diversity and collective responses whose output is negative, at least at some level.
Our challenge is to separate the wheat from the chaff to understand how nature corrects mistakes at the scale of molecular systems, organisms and species. This is the key to natural system resiliency and the rules needed to both design systems inspired by nature and decipher the impact of genetic background on individual disease development and drug responses (6–9).
Essentially the problem is based on individual elements and the layout of their connectivity, which makes network-based models well suited to investigate properties of natural systems (3,10).
1. Gartner, Z. J., Prescher, J. A. & Lavis, L. D. Unraveling cell-to-cell signaling networks with chemical biology. Nat. Chem. Biol. 13, 564 (2017).
2. Smith, J. L., Skiniotis, G. & Sherman, D. H. Architecture of the polyketide synthase module: surprises from electron cryo-microscopy. Curr Opin Struct Biol 31, 9–19 (2015).
3. Laddach, A., Ng, J. C.-F., Chung, S. S. & Fraternali, F. Genetic variants and protein–protein interactions: a multidimensional network-centric view. Curr. Opin. Struct. Biol. 50, 82–90 (2018).
4. Degiacomi, M. T. et al. Molecular assembly of the aerolysin pore reveals a swirling membrane-insertion mechanism. Nat. Chem. Biol. 9, 623–629 (2013).
5. Demir, O. et al. Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants. PLoS Comput Biol 7, e1002238 (2011).
6. Rackham, O. J. et al. The evolution and structure prediction of coiled coils across all genomes. J Mol Biol 403, 480–93 (2010).
7. Toyama, B. H. & Hetzer, M. W. Protein homeostasis: live long, won’t prosper. Nat. Rev. Mol. Cell Biol. 14, 55 (2013).
8. Wanieck, K., Fayemi, P.-E., Maranzana, N. & JACOBS, S. Biomimetics and its tools. (2017).
9. Ponzoni, L. & Bahar, I. Structural dynamics is a determinant of the functional significance of missense variants. Proc. Natl. Acad. Sci. 201715896 (2018).
10. Vuillon, L. & Lesieur, C. From local to global changes in proteins: a network view. Curr. Opin. Struct. Biol. 31, 1–8 (2015).