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Conceptional Foundation

The challenges of an ever-evolving world of science, characterized by the convergence of previously separated disciplines, by the need to foster broad cooperation, and by fierce international competition call for the introduction of innovative structures and modes of organization. This challenge was perceived at Goethe-University in Frankfurt am Main and in the year 2004 led to the establishment of the Frankfurt Institute for Advanced Studies (FIAS).

The scientific motivation for the foundation of a new Institute for Advanced Studies and its structural characteristics were laid down in a concept paper which subsequently underwent a favorable independent review by the Volkswagen Foundation. The principles laid down there have proven very fruitful and still serve as the corner stone of the Institute. Here we provide a brief synopsis of the FIAS founding charter, while subsequent sections will provide more details on research goals pursued and on the development the Institute has taken so far.

The challenge of complex systems

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.

In animate nature composite systems of high complexity abound. Many examples can be quoted.

  • The genome, which provides the information for the development and maintenance of living systems, forms an exceedingly complex network that allows for highly dynamic interactions among different genes. Much of the information contained in the genome is actually encoded in these dynamic network interactions, very much in the same way as the information conveyed by a sentence to a large degree is encoded in the respective syntactic relations.
  • The proteome of a living organism typically consist of tens of thousands or hundreds of thousands of different types of proteins which are coupled to form a closely interwoven, extremely complex interaction network. Only glimpses of an understanding of the resulting intracellular dynamics have been obtained so far.
  • Similarly, 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.
  • Yet another example of self-organizing complexity is the development of organisms. Here, too, a large number of components (genes, molecules within the differentiating cells, molecular signals from the environment and from within the growing organism) interact with each other to promote the specialization of initially pluripotent cells, the formation of organs and eventually mature organisms.
  • 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.

Social, economic, and climate systems share many of these features and therefore their dynamics can be studied with the same mathematical tools that are applied for the analysis of the above-mentioned networks.

Because of the organizing principles that rule in complex systems and create states of global coherence, the information contained in these systems always exceeds that contained in the components, and therefore systems properties can only be understood if both the component properties and the organizational principles are known. Sufficiently simple systems can be understood once their components are known because their dynamics are essentially linear and can therefore be deduced from the properties of the components by applying the known laws of physics. However, with growing complexity, new collective properties and organizing principles emerge and the properties apparent at the systems level cannot be inferred from the properties of the individual components alone. This requires insights into the possible modes of component interactions, the dynamics of the resulting collective behavior and the self-organizing principles required for the stabilization of systems.

An interesting question refers to the difference between organizational principles in inanimate and animate systems. In the former case the occurrence of structure-forming phase transitions, the self-assembly of molecules or atomic clusters, etc., can in principle be directly understood from first principles. Biological organisms, however, are the product of an evolutionary process and as such are goal-oriented. Due to selection pressure acting on reproductive fitness, living systems have been forced to optimize their organization so as to preserve structural and functional integrity and reproductive efficiency. Whether such evolutionary paths are unique, or just a special possibility out of many, is largely unknown. This indicates that the investigation and comparison of the organizational principles of animate and inanimate complex systems may be mutually benefitting. Although their organizational principles have numerous features in common, it is likely that there are interesting and yet to be identified differences.

Research at FIAS is dedicated to the study of the formation, self-organizing stabilization and emergent dynamics of complex systems in a variety of contexts, both in animate and inanimate nature, and from a variety of perspectives. Firmly grounded in their native disciplines, physicists, chemists, biologists, neuroscientists and information scientists study the properties of complex systems in their own domains of specialization in depth. Beyond that, however, they are also given the opportunity – and are encouraged – to bridge the gap between fields and to enter into interdisciplinary collaborations.

Common to much of the research at the Institute is the use of similar mathematical tools, taken from statistical physics, field theory, many-body theory, etc. and of similar numerical tools and computational methods. Therefore computer science is an integral part of this approach, providing common knowledge representations, tools, and computational algorithms to simulate complex systems and to support interdisciplinary research.

A think tank for theoretical research

In addition to the challenging scientific problems it is set to address, there has been a second motivation for the establishment of FIAS, which has its roots in the landscape of existing research institutions in the Frankfurt region. The Frankfurt/Rhein-Main area harbors a number of laboratories of international reputation which are dedicated to experimental research at the cutting edge of natural science, in particular in the areas of neurobiology and brain research, membrane proteomics and structural biology, atomic and heavy ion physics and investigations of the structure of elementary matter. While these branches of science have many potential connections and areas of overlap, there is barely any contact between researchers working in the different disciplines. Furthermore, while experimental work in these facilities receives substantial funding, this is not generally the case for theoretical research, which runs the risk of falling behind in the attempt to understand the rapidly accumulating experimental facts. This is particularly deplorable at a time when some of the mentioned disciplines, in particular those in the life sciences, are reaching a stage of maturity which calls for a higher degree of formalization of concepts and mathematical penetration.

This situation has inspired the founding of the Frankfurt Institute for Advanced Studies. The Institute serves as a research superstructure and think tank, bringing together theorists from the disciplines of biology, chemistry, computer science, neuroscience, and physics in a common organizational and intellectual framework and embedding them in a stimulating academic environment. A wide range of questions related to the dynamical properties, structure formation and self-organization of complex systems are investigated in various contexts, for animate and inanimate systems, with ensuing synergies for the disciplines of physics, chemistry and biology alike.

While FIAS is dedicated to theoretical research, it maintains close contacts to groups having an experimental focus, both in the vicinity of Frankfurt and internationally. Fruitful collaborations have been established with the science faculties at Goethe University and with various extramural research institutions. Providing guidance and theoretical underpinnings for experimental research is a widely perceived need, in particular in the life sciences where theoretical analysis plays an ever increasing role, and FIAS is positioned to meet this challenge.


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