A natural framework for arbitrary multi-scale computer science and systems biology efficient computational modeling

The aim of the present paper is to provide the first concise overview of a natural framework for arbitrary multi-scale computer science and systems biology computational modeling. To grasp a more reliable representation of reality and to get more effective modeling techniques, researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergically articulated by natural coupling. After a brief introduction about traditional modeling vs. fresh QFT approach, we go to the root of the problem directly. We present key points solution to arbitrary multi-scale modeling problems. The first attempt to identify basic principles to get stronger modeling solution for scientific application has been developing at Politecnico di Milano University since the 1990s. The fundamental principles on computational information conservation theory (CICT), for arbitrary multi-scale system modeling from basic generator and relation through discrete paths denser and denser to one another, towards a never ending 'blending quantum continuum,' are recalled. A computational example is presented and discussed. This paper is a relevant contribute towards arbitrary multi-scale computer science and systems biology modeling, to show how computational information conservation approach can offer stronger and more effective system modeling algorithms for more reliable simulation.