Science – Overview

Science for tomorrow's reality

The founding concept of FIAS called for the creation of a platform bringing together scientists addressing the theory of complex and self-organizing systems. Such systems characteristically are composed of a large number of mutually interacting components, frequently active on their own, which can give rise to new emergent properties.

The growth of knowledge in the natural sciences proceeds at an unprecedented pace. Increasingly sophisticated experimental techniques have produced a wealth of information about the organization of inanimate and living systems. In the life sciences, until quite recently quantitative experimental data were too scarce to allow for a well-founded theoretical description of the complex interactions that are at work in living systems. Mostly these inquiries have proceeded in a deductive way, subdividing systems into ever smaller components and studying these components in isolation in order to facilitate their characterization. Research was focused on identifying the components and studying their properties. Now, however, in some areas enough and sufficiently reliable data have become available, allowing the study of the interplay between the components, which is essential for understanding the dynamics and function of the systems as a whole.

It is believed that most of the properties of inanimate systems will ultimately be understood in terms of the dynamic interactions among elementary constituents. The same holds for the relation between the functional properties of living systems and their constituting components. Such complex systems are governed by the rules of nonlinear dynamics and can develop qualitatively new properties which are not simply derivable from the properties of the components alone. Many examples for this can be found in inanimate and even more so in animate nature.

​Examples from inanimate nature are the various many-body systems encountered in nuclear, solid-state, and astrophysics, as well as the nanostructures and macromolecules found in chemistry and biochemistry.

  • In subatomic physics the fundamental constituents of elementary matter, quarks and gluons, aggregate into composite objects, the hadrons. Although the laws governing the interaction of the constituents are believed to be known since more than 30 years, deducing the properties of hadrons still is a major challenge. Similarly, the aggregation of hadrons into nuclei is a nontrivial process. It even can be argued that empty space itself, the vacuum, is a highly complex object when viewed from the perspective of quantum fluctuations.
  • On the atomic length scale the physics and chemistry of macromolecules and clusters typically deals with complex systems. Many examples of the emergence of qualitatively new features can be quoted, e.g., the development of new collective properties when going from small molecules to large clusters or the cluster aggregation on surfaces leading to intricate, fractally shaped morphologies. The steric properties of macromolecules, exemplified by the highly relevant problem of protein folding, are at the limit of present-day computability and form a bridge to the life sciences.
  • Even on the largest possible scales, in astrophysics and cosmology, problems related to complexity and self-organization arise. Several phase transitions must have occurred in the earliest stages of the universe which led to the formation of the matter that is now observed. The processes which have produced the largest structures in the cosmos, galaxies and clusters of galaxies, show intriguing similarities with self-organization acting in the micro world.
  • The immune system can be seen as a complex interaction network that consists of a variety of different organs and cell types, which cooperate in an intricate way to protect an organism from hostile intruders of internal (e.g., tumors) and external (e.g., infectious agents) origin. Understanding the dynamics of this network is crucial for the design of new drugs and therapeutic strategies.
  • Possibly the most challenging example for a complex system is the human brain. It constitutes a dynamical system containing about 1011 neurons that interact through more than 1014 synaptic connections. Although much progress has been made, it has been possible only for very simple neuronal networks to deduce their functions from the properties of the constituting neurons. The neuronal processes underlying higher cognitive and executive functions such as perception, attention, decision making, value assignment, emotional responses, and action planning are still by and large unknown. Even if the full connectivity graph were known, we would still not be able to understand the emergent functions. Additional knowledge is required about the specific dynamics of these interactions and the codes in which the relevant information is contained.

    Research Areas

    Theoretical Physics

    Theoretical physics is the discipline that aims at describing how the world works in terms of fundamental equations. The goal is to abstract explicit phenomena by reducing them to underlying principles that are responsible for many different manifestations in nature.


    The brain is considered the most complex structure on earth. It is composed of a network of billions of nerve cells. Our goal is to understand how cognitive phenomena can arise from the collective interactions of these many neural elements.

    Computer Science

    Within the last decade high performance computers have become an integral part in todays science and society. Most simulations and experimental analysations of complex systems require a high amount of computing power.

    Life Sciences

    Theoretical biology aims at a quantitative understanding of biological systems, their dynamics and interaction across scales. To this end new analysis approaches are developed to extract relevant information from either tera and petabyte sized data sets, or from very scarce data.

    Systemic Risk

    The latest financial crisis has painfully revealed the importance of a working financial system for the real economy. Our research takes an interdisciplinary approach drawing on expertise from machine learning, information theory and complex systems.