In life sciences, the description of complex systems as adjusted from life sciences entails the incorporation of many components such as interactions between elements happening specifically (taking place on networks with interactions often being discrete and systems operation algorithmically), being out-of-equilibrium which renders them difficult to be handled analytically, following evolutionary dynamics while progressing changing their context, boundary conditions or their own habitat or environment, being adaptive and robust simultaneously operating at the edge of chaos and last but not least having memory and being path-dependent, demonstrating their non-ergodic and non-Markovian features. For instance, in biology and virology, since the virus shape, the evolution of cells, ecosystems, organisms, tissues, organs and even the biosphere, it is important to address this impactful influence spanning across all scales of biological organization, ranging from genomes to the planets. Their dynamics include non-linear phenomena and some tipping points along with self-organizing and emergent properties that share commonalities with other non-biological and biological complex systems. The subtle fact that such commonalities and similarities may mask universal properties prevailing complexity which concern the areas including but not limited to natural science, physical science, biological physics, biological anthropology, cell biology, genetics, microbiology, biochemistry, physiology and pharmacology. The concepts and tools as important keys to understand interaction, evolution; and hence, the success and robustness of mathematical and computational models along with their applications account for the processes in which some agents are able to propagate through networks.
Given the structural, ecological and computational features linked with any biological element, complexity is important to understand the flow and co-evolution of related processes. The advancement of life sciences in modern science has been achieved based on the paradigm of knowledge which states that it is possible to understand the organisms’ functions when they are looked into all through the way deep down to the molecular level. This vision-enhancing paradigm is necessary to understand complex and dynamic characteristics and computational life science, in this regard, benefits from proactive use of computers and machines for the efficient analyses of big data obtained from innovative quantification technologies which integrate dynamic multilayer systems to understand life systems, achieve predictability and controllability.
Concerning life and natural phenomena, natural science and neuroscience, nonlinear dynamics in complex systems, the aspect of interdisciplinary nature of current and future science, as ongoing and unfolding, will be at the foreground with the provision and exemplification of quantitative models based on linear dynamics and complex systems, which go beyond the scope of traditional statistical analyses. To elucidate, concepts related to phase space, fixed points, stability transitions, besides models of dynamical properties of neurons, synergetics, self- organization and pattern formation need profoundly addressed with detail as well as to-the-point precision.
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