Physics, chemistry and biology study the organization of matter at various scales, from subatomic to cosmic. The behavior of each scale is determined by the laws that govern behavior of the underlying scale, but the nature of relations between scales is full of surprises. For example let’s consider a simple idea: matter consists of tiny, constantly moving particles, which attract each other at close distances, but repel when they are too close. Lennard-Jones potential is one of possible mathematical formalizations of the above idea. In spite of the apparent simplicity of microscopic dynamics, at large scales these rules give rise to a plethora of complex phenomena, such as aggregate states of matter, thermodynamics, acoustics, hydrodynamics and turbulence. In scientific and engineering practice we can often abstract microscopic details behind macroscopic models, such as diffusion or Navier-Stokes equations, that operate at the scale of interest. But in some domains, like material science or biochemistry, we have to focus on the relationship between many orders of magnitude in scale. How do changes in molecular structure reflect in macroscopic material properties? How do genomic sequences translate into morphological appearance? In the field of biology these ties between scales are especially important and obscure at the same time. That’s why development of practical methods of capturing and analyzing such relations are of a great need. In silico multi-agent models provide a computational playground for creation and improvement of such methods. We may use such models for efficient design of local microscopic rules that lead to a desired global behavior. Thus, creation of simulated Artificial Life serves as a stepping stone in deep understanding of life in the real world.

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