Author

# Stuart A. Kauffman

Other affiliations: University of Cincinnati, University of Vermont, University of New Mexico ...read more

Bio: Stuart A. Kauffman is an academic researcher from Institute for Systems Biology. The author has contributed to research in topics: Boolean network & Physics. The author has an hindex of 69, co-authored 311 publications receiving 37547 citations. Previous affiliations of Stuart A. Kauffman include University of Cincinnati & University of Vermont.

##### Papers published on a yearly basis

##### Papers

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01 Jan 1993

TL;DR: The structure of rugged fitness landscapes and the structure of adaptive landscapes underlying protein evolution, and the architecture of genetic regulatory circuits and its evolution.

Abstract: 1. Conceptual outline of current evolutionary theory PART I: ADAPTATION ON THE EDGE OF CHAOS 2. The structure of rugged fitness landscapes 3. Biological implications of rugged fitness landscapes 4. The structure of adaptive landscapes underlying protein evolution 5. Self organization and adaptation in complex systems 6. Coevolving complex systems PART II: THE CRYSTALLIZATION OF LIFE 7. The origins of life: a new view 8. The origin of a connected metabolism 9. Autocatalytic polynucleotide systems: hypercycles, spin glasses and coding 10. Random grammars PART III: ORDER AND ONTOGENY 11. The architecture of genetic regulatory circuits and its evolution 12. Differentiation: the dynamical behaviors of genetic regulatory networks 13. Selection for gene expression in cell type 14. Morphology, maps and the spatial ordering of integrated tissues

7,835 citations

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TL;DR: The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes”.

4,250 citations

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01 Jan 1995

TL;DR: Complexity theory is one of the most controversial areas of current scientific research as mentioned in this paper, which suggests that there are hidden tendencies in nature to select ordered states, even when statistically they are vastly outnumbered by chaotic possibilities.

Abstract: Complexity theory is one of the most controversial areas of current scientific research. Developing out of chaos theory, complexity suggests that there are hidden tendencies in nature to select ordered states, even when statistically they are vastly outnumbered by chaotic possibilities: that there is a deep natural impulse towards order, counteracting the degenerative tendencies of the Second Law of Thermodynamics. Like chaos, complexity is a multidisciplinary area of research and those involved include physicists, economists and biologists. This is a study of complexity.

2,897 citations

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TL;DR: This article develops parts of a universal theory of adaptation on correlated landscapes by adaptive processes that have sufficient numbers of mutations per individual to "jump beyond" the correlation lengths in the underlying landscape.

1,354 citations

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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.

Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

39,297 citations

28 Jul 2005

TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

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TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.

Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them.
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network.
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other.
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

18,415 citations

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TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

17,647 citations

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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

14,171 citations