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The reference center for complex systems and a model of interdisciplinary research. The Institute produces a series of working papers involving research done by members and collaborators. These Working Papers Series provide a good picture of the advances done at SFI and in associated Labs.
In the Center for Models of Life, C-MOL, we use methods from physics to develop models dealing with computation and communication processes in biological systems. The basis for such quantitative models are the different networks that play a central role in much of the research on complex systems, and which range from the regulation of actions in the simplest viruses to the diverse phenomena that take place within ecological and social systems.
Interdisciplinary program at the University of Michigan designed to encourage and facilitate research and education in the general area of nonlinear, dynamical and adaptive systems.
The Volen National Center for Complex Systems was formed for the purpose of studying the brain and intelligence. The Center is composed of faculty members who specialize in artificial intelligence, cognitive science, linguistics, and a wide range of topics in neuroscience including experimental psychology, computational neuroscience, and cellular and molecular neurobiology.
A research center located in Florida, exploring some of the most fascinating questions relating brain complexity, such as: How do we play and listen to music? How are nerve cells connected together in the brain? How does the function of any system depend on how its elements are connected?.
This Complex Systems Group is an interdisciplinary set of people working together at the University of Alaska in Anchorage, where they organize seminars and workshops on complex systems in the natural and social sciences.
The group performs research on numerical statistical physics. This implies the development and improvement of basic algorithms, and their application to specific problems. These problems cover a wide area with emphasis on equilibrium and non-equilibrium systems, soft matter, disordered systems, and biological physics.
The group carries out theoretical research on a broad range of topics related to self-organization in nonequilibrium chemical and biological systems. Basically, we want to understand how simple interactions between simple elements can give rise to complex coherent patterns of collective behaviour in such systems. We are also interested in the problems of design and control of self-organization.
The research, directed by Professor Barabasi, has a simple objective: think networks. It is about how networks emerge, what they look like, and how they evolve; and how networks impact on understanding of complex systems. To understand networks, our research has taken us to rather unexpected areas.
Our world has a network construction. Networks are everywhere: cellular networks, the Internet, the Web, social and economics networks. All these networks are extremely compact, infinite-dimensional objects with complex architectures. The questions are: How are the networks organized? How do these complex architectures emerge? How do networks evolve? What is special about processes taking place on the complex networks? How to design an optimal network? .
Exploring the statistical patterns of organization of complex networks with special emphasis in molecular networks (such as RNA folding graphs).
The study of complex networks has become an important interdisciplinary field of research in XXI century. Its impact in biology, society, technology and ecology is expected to be a tremendous revolution. In particular I concentrate on the study of global and local topological properties of these networks. I am interested in developing statistical-mechanics concepts which permits to understand the organization and function of such networks.
Motter's Lab involves a team of researchers studying complex networks, both dynamically and topologically, using different perspectives. Different topics and areas are covered, from cascading and searching processes to spectral theory in both biological and non-biological systems.
Our group's goal is to develop models that provide insight into the emergence, evolution, and stability of complex systems. To this end we develop and validate models that can be studied by means of computational experiments. Our approach focus on the identification of the mechanisms determining the dynamics of a given system. We then translate these mechanisms into a parsimonious set of rules that can be implemented and investigated by computational means.
This group has developed a very active agenda on artificial chemistries, artificial gene regulatory systems and evolvable systems. They have also studied the emergence of complex networks in these artificial systems.
Our research is on the structure and function of networks, particularly social and information networks, which are studied using a combination of empirical methods, analysis, and computer simulation. Among other things, we have investigated scientific coauthorship networks, citation networks, email networks, friendship networks, epidemiological contact networks, and animal social networks.
Our research is on the structure and function of networks, particularly social and information networks, which are studied using a combination of empirical methods, analysis, and computer simulation. Among other things, we have investigated scientific coauthorship networks, citation networks, email networks, friendship networks, epidemiological contact networks, and animal social networks.
Many complex systems can be modeled as a network, where the vertices are the elements of the system and the edges represent the interactions between them. Coupled biological and chemical systems, neural networks, social interacting species, computer networks or the Internet are only few of such examples. Characterizing the structural (the connectivity) properties of the networks is then the first step in order to understand the complex dynamics of these systems.
Our living world is made up of simple elementary forces and constitutents. But how do these work together to make life, evolution, brains, genomes, immune systems, societies? We study general principles of complex systems in nature from the perspective of statistical physics, often reducing a system to the most simple working model for a particular phenomenon.
We are interested in genetic networks and their evolution. Molecular biology explores the minutiae of a machinery of daunting complexity. Organismal biology, on the other hand, is far removed from this machinery. In between, a huge gap exists in our knowledge. One of our goals is to help fill this gap. To do so, we study genetic and metabolic networks. These networks form bridges between genes and organisms, connecting molecules to whole organisms and their survival.
Understanding the protein circuits that perform computations within the cell is a central problem in biology. From the point of view of physics, cells offer the challenge of understanding the collective behavior of interacting molecular machines designed to operate with remarkable precision under strong biological constraints. Our lab studies biological networks and circuits using a combined experimental and theoretical approach, aiming to uncover general underlying principles that govern their functioning.
Feedback and decision taking are both elements of biological systems. In some cases, e.g. in development and in lysis-lysogeny decisions of temperate phages, a gene regulative system needs to decide which of a few possible states it wants to be in. When such decisions are to be taken, positive feedback and sensitivity to single molecule fluctuations become important. We in particular consider decision making in the core of temperate phages. Response systems. In particular for responses related to external shocks, the genetic system needs to increase production of certain repair proteins fast. We model a number of such response systems, including Heat shock, unfolded protein response and the SOS response.
I am mostly interested in properties of various complex networks . Such networks operate inside living cells, make the backbone of the Internet, connect webpages in the WWW, make up our brain, etc. My research on bio-molecular networks falls under the category of research on systems biology, bioinformatics, or computational biology. In my paper with Kim Sneppen from the Niels Bohr Institute in Copenhagen we analyzed topological correlations present in protein interaction and regulatory networks in yeast. Later we applied the same technique to deduce correlation properties of the Internet.
Many systems in nature can be described as large networks (nodes or vertices connected by links or edges): Friendship networks, computer networks, metabolic networks, power grids, scientific citations, neural networks. These networks have non trivial statistical properties that we describe and model.
The most respected scientific journal. Although biased to biological research, it covers a very broad range of topics and it's the most cited.
The other most respected scientific journal. Probably as famous as Nature but with probably less bias towards biology.
Probably the third most cited multidisciplinary scientific journal. Coverage in PNAS spans the biological, physical, and social sciences.
One of the most reputed publisher in physics. Complex systems mostly present in its Letters and the series E. It offers all papers in PDF format even for the pre-electronic era with superb quality.
The web page of the pioneers in the field. Links to all papers, a gallery of beautiful images, and the datasets they used.
A research project in the area of networks with subtopics like biochemical networks, internet, formation, structure, scaling and synchronization in networks.
He leads the networks group at the University of Porto, in Portugal.