S A Kauffman

Institute for Systems Biology, Seattle, Washington, United States

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Publications (103)262.12 Total impact

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    ABSTRACT: The nature of economic opportunity has recently received significant attention in entrepreneurship, organization science and strategy. The notion of boundedly rational search on an (NK) opportunity landscape has been particularly relevant to these conversations and debates. We argue that the focus on bounded rationality and search is highly problematic for the fields of entrepreneurship and strategy and does not allow us to explain the origins of economic novelty. We contrast the NP problem with the frame problem to illustrate our point, and highlight the role of adjacent possibilities and novel affordances. We discuss the entrepreneurial and economic implications of these arguments by building on unique insights from biology, the natural and computational sciences. Copyright © 2014 Strategic Management Society.
    Strategic Entrepreneurship Journal 06/2014; · 2.05 Impact Factor
  • Stuart A. Kauffman
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    ABSTRACT: Despite Darwin, we remain children of Newton, and dream of a grand theory that is epistemologically complete and would allow prediction of the evolution of the biosphere. The main purpose of this article is to show that this dream is false, and bears on studying patterns of evolution. To do so, I must justify the use of the word “function” in biology, when physics has only happenings. The concept of “function” lifts biology irreducibly above physics, for as we shall see, we cannot prestate the ever new biological functions that arise and constitute the very phase space of evolution. Hence, we cannot mathematize the detailed becoming of the biosphere, nor write differential equations for functional variables we do not know ahead of time, nor integrate those equations, so no laws “entail” evolution. The dream of a grand theory fails. In place of entailing laws, I propose a post-entailing law explanatory framework in which Actuals arise in evolution that constitute new boundary conditions that are enabling constraints that create new, typically unprestatable, Adjacent Possible opportunities for further evolution, in which new Actuals arise, in a persistent becoming. Evolution flows into a typically unprestatable succession of Adjacent Possibles. Given the concept of function, the concept of functional closure of an organism making a living in its world becomes central. Implications for patterns in evolution include historical reconstruction, and statistical laws such as the distribution of extinction events, or species per genus, and the use of formal cause, not efficient cause, laws.
    Biosystems. 01/2014;
  • Sui Huang, Stuart Kauffman
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    ABSTRACT: The increasingly evident limitations of target-selective cancer therapy has stimulated a flurry of ideas for overcoming the development of resistance and recurrence-the near universal reason for therapy failure from which target-selective drugs are not exempt. A widely proposed approach to conquer therapy resistance is to depart from the myopic focus on individual causal pathways and instead target multiple nodes in the cancer cell's gene regulatory network. However, most ideas rely on a simplistic conceptualization of networks: utilizing solely their topology and treating it as a display of causal interactions, while ignoring the integrated dynamics in state space. Here we review the more encompassing formal framework of global network dynamics in which cancer cells, like normal cell types, are high-dimensional attractor states. Then therapy is represented by the network perturbation that will promote the exit from such cancer attractors and reentering a normal attractor. We show in this qualitative and accessible discussion how the idea of a quasi-potential landscape and the theory of least-action-path offer a new formal understanding for computing the set of network nodes (molecular targets) that need to be targeted in concert in order to exit the cancer attractor. But targeting cancer cells based on the network configuration of an "average" cancer cell, however precise, may not suffice to eradicate all tumor cells because of the dynamic non-genetic heterogeneity of cancer cell populations that makes them moving targets and drives the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors.
    Seminars in Cancer Biology 06/2013; · 7.44 Impact Factor
  • Stuart Kauffman
    Physics of Life Reviews 05/2013; · 6.58 Impact Factor
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    ABSTRACT: The origins of novelty and nature of economic opportunity have recently received significant attention in organization science, strategy and entrepreneurship. The notion and metaphor of search on an (NK) opportunity landscape — or “phase space” — has been particularly relevant to these conversations and debates. While the landscape and phase space notion has certainly been helpful for explaining some important aspects of economic activity, we argue that it also features some critical deficiencies — particularly in explaining the origins of novelty. Existing notions of landscapes do not fully account for the “empty” spaces of possible action, and the unprestatable adjacent possibilities associated with strategy and economic activity. Furthermore, any activity on this space introduces yet more, unstatable possibilities. Thus, simply focusing on the computational limitations, or bounded rationality, of economic actors is not sufficient for explaining novelty. We argue that the central problem of explaining novel economic activity is not so much one of computational insufficiency (the problem of NP-completeness), but a problem of how to account for the readily manifest, emergent novelty we see in the economic sphere (the “frame” problem). While some have recently highlighted problems with the notion of landscapes and focused on factors such as entrepreneurial enactment or effectuation in generating novelty, we provide alternative foundations. We discuss the implications of these arguments by building on unique insights from biology, the natural and computational sciences. Our approach is not meant to replace existing evolutionary explanations of economic activity. Instead we seek to augment these approaches in an effort to clarify the nature of opportunities and the respective contributions of organisms and environments in the emergence of opportunities.
    01/2013;
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    Wim Hordijk, Mike Steel, Stuart Kauffman
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    ABSTRACT: This paper presents new results from a detailed study of the structure of autocatalytic sets. We show how autocatalytic sets can be decomposed into smaller autocatalytic subsets, and how these subsets can be identified and classified. We then argue how this has important consequences for the evolvability, enablement, and emergence of autocatalytic sets. We end with some speculation on how all this might lead to a generalized theory of autocatalytic sets, which could possibly be applied to entire ecologies or even economies.
    Acta Biotheoretica 09/2012; · 0.95 Impact Factor
  • M.andrecut, S. A.kauffman
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    ABSTRACT: In this paper we discuss a noisy mean field model for the genetic toggle switch. We show that this model approximates very well the characteristics of the system, observed using the exact Gillespie stochastic simulation algorithm. Also, we show that the system can be made exponentially stable depending on reaction parameters.
    International Journal of Modern Physics B 01/2012; 20(29). · 0.46 Impact Factor
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    Giuseppe Longo, Maël Montévil, Stuart Kauffman
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    ABSTRACT: Biological evolution is a complex blend of ever changing structural stability, variability and emergence of new phenotypes, niches, ecosystems. We wish to argue that the evolution of life marks the end of a physics world view of law entailed dynamics. Our considerations depend upon discussing the variability of the very "contexts of life": the interactions between organisms, biological niches and ecosystems. These are ever changing, intrinsically indeterminate and even unprestatable: we do not know ahead of time the "niches" which constitute the boundary conditions on selection. More generally, by the mathematical unprestatability of the "phase space" (space of possibilities), no laws of motion can be formulated for evolution. We call this radical emergence, from life to life. The purpose of this paper is the integration of variation and diversity in a sound conceptual frame and situate unpredictability at a novel theoretical level, that of the very phase space. Our argument will be carried on in close comparisons with physics and the mathematical constructions of phase spaces in that discipline. The role of (theoretical) symmetries as invariant preserving transformations will allow us to understand the nature of physical phase spaces and to stress the differences required for a sound biological theoretizing. In this frame, we discuss the novel notion of "enablement". This will restrict causal analyses to differential cases (a difference that causes a difference). Mutations or other causal differences will allow us to stress that "non conservation principles" are at the core of evolution, in contrast to physical dynamics, largely based on conservation principles as symmetries. Critical transitions, the main locus of symmetry changes in physics, will be discussed, and lead to "extended criticality" as a conceptual frame for a better understanding of the living state of matter.
    01/2012;
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    ABSTRACT: Our current understanding of evolution is so tightly linked to template-dependent replication of DNA and RNA molecules that the old idea from Oparin of a self-reproducing 'garbage bag' ('coacervate') of chemicals that predated fully-fledged cell-like entities seems to be farfetched to most scientists today. However, this is exactly the kind of scheme we propose for how Darwinian evolution could have occurred prior to template replication. We cannot confirm previous claims that autocatalytic sets of organic polymer molecules could undergo evolution in any interesting sense by themselves. While we and others have previously imagined inhibition would result in selectability, we found that it produced multiple attractors in an autocatalytic set that cannot be selected for. Instead, we discovered that if general conditions are satisfied, the accumulation of adaptations in chemical reaction networks can occur. These conditions are the existence of rare reactions producing viable cores (analogous to a genotype), that sustains a molecular periphery (analogous to a phenotype). We conclude that only when a chemical reaction network consists of many such viable cores, can it be evolvable. When many cores are enclosed in a compartment there is competition between cores within the same compartment, and when there are many compartments, there is between-compartment competition due to the phenotypic effects of cores and their periphery at the compartment level. Acquisition of cores by rare chemical events, and loss of cores at division, allows macromutation, limited heredity and selectability, thus explaining how a poor man's natural selection could have operated prior to genetic templates. This is the only demonstration to date of a mechanism by which pre-template accumulation of adaptation could occur.
    Biology Direct 01/2012; 7:1; discussion 1. · 2.72 Impact Factor
  • Journal of critical care 06/2011; 26(3):325-7. · 2.13 Impact Factor
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    ABSTRACT: The response to different kinds of perturbations of a discrete model of gene regulatory network, which is a generalization of the random Boolean network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.
    Journal of computational biology: a journal of computational molecular cell biology 03/2011; 18(4):559-77. · 1.69 Impact Factor
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    ABSTRACT: Despite myriads of possible gene expression profiles, cells tend to be found in a confined number of expression patterns. The dynamics of Boolean models of gene regulatory networks has proven to be a likely candidate for the description of such self-organisation phenomena. Because cells do not live in isolation, but they constantly shape their functions to adapt to signals from other cells, this raises the question of whether the cooperation among cells entails an expansion or a reduction of their possible steady states. Multi random Boolean networks are introduced here as a model for interaction among cells that might be suitable for the investigation of some generic properties regarding the influence of communication on the diversity of cell behaviours. In spite of its simplicity, the model exhibits a non-obvious phenomenon according to which a moderate exchange of products among adjacent cells fosters the variety of their possible behaviours, which on the other hand are more similar to one another. On the contrary, a more invasive coupling would lead cells towards homogeneity.
    IET Systems Biology 03/2011; 5(2):137-44. · 1.54 Impact Factor
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    ABSTRACT: Biological proteins are known to fold into specific 3D conformations. However, the fundamental question has remained: Do they fold because they are biological, and evolution has selected sequences which fold? Or is folding a common trait, widespread throughout sequence space? To address this question arbitrary, unevolved, random-sequence proteins were examined for structural features found in folded, biological proteins. Libraries of long (71 residue), random-sequence polypeptides, with ensemble amino acid composition near the mean for natural globular proteins, were expressed as cleavable fusions with ubiquitin. The structural properties of both the purified pools and individual isolates were then probed using circular dichroism, fluorescence emission, and fluorescence quenching techniques. Despite this necessarily sparse "sampling" of sequence space, structural properties that define globular biological proteins, namely collapsed conformations, secondary structure, and cooperative unfolding, were found to be prevalent among unevolved sequences. Thus, for polypeptides the size of small proteins, natural selection is not necessary to account for the compact and cooperative folded states observed in nature.
    Genes. 01/2011; 2(3):608-26.
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    M Andrecut, S A Kauffman
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    ABSTRACT: We discuss the complex dynamics of a nonlinear random networks model as a function of the connectivity k between the elements of the network. We show that this class of networks exhibits an order-chaos phase transition for a critical connectivity k{c}=2 . Also, we show that both pairwise correlation and complexity measures are maximized in dynamically critical networks. These results are in good agreement with the previously reported studies on random Boolean networks and random threshold networks, and show once again that critical networks provide an optimal coordination of diverse behavior.
    Physical Review E 08/2010; 82(2 Pt 1):022105. · 2.31 Impact Factor
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    ABSTRACT: The asymptotic dynamics of random Boolean networks subject to random fluctuations is investigated. Under the influence of noise, the system can escape from the attractors of the deterministic model, and a thorough study of these transitions is presented. We show that the dynamics is more properly described by sets of attractors rather than single ones. We generalize here a previous notion of ergodic sets, and we show that the Threshold Ergodic Sets so defined are robust with respect to noise and, at the same time, that they do not suffer from a major drawback of ergodic sets. The system jumps from one attractor to another of the same Threshold Ergodic Set under the influence of noise, never leaving it. By interpreting random Boolean networks as models of genetic regulatory networks, we also propose to associate cell types to Threshold Ergodic Sets rather than to deterministic attractors or to ergodic sets, as it had been previously suggested. We also propose to associate cell differentiation to the process whereby a Threshold Ergodic Set composed by several attractors gives rise to another one composed by a smaller number of attractors. We show that this approach accounts for several interesting experimental facts about cell differentiation, including the possibility to obtain an induced pluripotent stem cell from a fully differentiated one by overexpressing some of its genes.
    Journal of Theoretical Biology 07/2010; 265(2):185-93. · 2.35 Impact Factor
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    ABSTRACT: We are a group of scientists and business people who share a common interest in the application to financial systems of a scientific methodology called agent-based modeling (ABM). We believe this methodology represents a scientifically validated and powerful tool to facilitate the Commission's regulation of equity markets. We believe that its use would enable the Commission to make scientifically informed responses to questions posed in the Release and to anticipate both the intended and unintended consequences of proposed regulations. We also note that a number of other agencies of government now successfully employ this type of science. The Commission's facilitation of the establishment of a national market system as directed by Congress in 1934 has been significantly affected by the emergence of new computing and communications technologies. These have affected the architectural structure of the system itself as well as how it functions. This is now reflected in a shift from manual trading to automated trading over increasingly faster time scales in increasingly more complex networks. The pace of this change has increased dramatically in only the last decade. As the Release notes, this now poses a number of important new regulatory issues for the Commission. ABM could help address these issues, as subsequently described in this letter.
    04/2010;
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    M Andrecut, D Foster, H Carteret, S A Kauffman
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    ABSTRACT: Random Threshold Networks (RTNs) are an idealized model of diluted, non-symmetric spin glasses, neural networks or gene regulatory networks. RTNs also serve as an interesting general example of any coordinated causal system. Here we study the conditions for maximal information transfer and behavior diversity in RTNs. These conditions are likely to play a major role in physical and biological systems, perhaps serving as important selective traits in biological systems. We show that the pairwise mutual information is maximized in dynamically critical networks. Also, we show that the correlated behavior diversity is maximized for slightly chaotic networks, close to the critical region. Importantly, critical networks maximize coordinated, diverse dynamical behavior across the network and across time: the information transmission between source and receiver nodes and the diversity of dynamical behaviors, when measured with a time delay between the source and receiver, are maximized for critical networks.
    Journal of computational biology: a journal of computational molecular cell biology 08/2009; 16(7):909-16. · 1.69 Impact Factor
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    Sui Huang, Ingemar Ernberg, Stuart Kauffman
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    ABSTRACT: Cell lineage commitment and differentiation are governed by a complex gene regulatory network. Disruption of these processes by inappropriate regulatory signals and by mutational rewiring of the network can lead to tumorigenesis. Cancer cells often exhibit immature or embryonic traits and dysregulated developmental genes can act as oncogenes. However, the prevailing paradigm of somatic evolution and multi-step tumorigenesis, while useful in many instances, offers no logically coherent reason for why oncogenesis recapitulates ontogenesis. The formal concept of "cancer attractors", derived from an integrative, complex systems approach to gene regulatory network may provide a natural explanation. Here we present the theory of attractors in gene network dynamics and review the concept of cell types as attractors. We argue that cancer cells are trapped in abnormal attractors and discuss this concept in the light of recent ideas in cancer biology, including cancer genomics and cancer stem cells, as well as the implications for differentiation therapy.
    Seminars in Cell and Developmental Biology 08/2009; 20(7):869-76. · 6.20 Impact Factor
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    ABSTRACT: The process of cellular differentiation is governed by complex dynamical biomolecular networks consisting of a multitude of genes and their products acting in concert to determine a particular cell fate. Thus, a systems level view is necessary for understanding how a cell coordinates this process and for developing effective therapeutic strategies to treat diseases, such as cancer, in which differentiation plays a significant role. Theoretical considerations and recent experimental evidence support the view that cell fates are high dimensional attractor states of the underlying molecular networks. The temporal behavior of the network states progressing toward different cell fate attractors has the potential to elucidate the underlying molecular mechanisms governing differentiation. Using the HL60 multipotent promyelocytic leukemia cell line, we performed experiments that ultimately led to two different cell fate attractors by two treatments of varying dosage and duration of the differentiation agent all-trans-retinoic acid (ATRA). The dosage and duration combinations of the two treatments were chosen by means of flow cytometric measurements of CD11b, a well-known early differentiation marker, such that they generated two intermediate populations that were poised at the apparently same stage of differentiation. However, the population of one treatment proceeded toward the terminally differentiated neutrophil attractor while that of the other treatment reverted back toward the undifferentiated promyelocytic attractor. We monitored the gene expression changes in the two populations after their respective treatments over a period of five days and identified a set of genes that diverged in their expression, a subset of which promotes neutrophil differentiation while the other represses cell cycle progression. By employing promoter based transcription factor binding site analysis, we found enrichment in the set of divergent genes, of transcription factors functionally linked to tumor progression, cell cycle, and development. Since many of the transcription factors identified by this approach are also known to be implicated in hematopoietic differentiation and leukemia, this study points to the utility of incorporating a dynamical systems level view into a computational analysis framework for elucidating transcriptional mechanisms regulating differentiation.
    BMC Systems Biology 03/2009; 3:20. · 2.98 Impact Factor
  • Roy Wilds, Stuart A Kauffman, Leon Glass
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    ABSTRACT: We study the evolution of complex dynamics in a model of a genetic regulatory network. The fitness is associated with the topological entropy in a class of piecewise linear equations, and the mutations are associated with changes in the logical structure of the network. We compare hill climbing evolution, in which only mutations that increase the fitness are allowed, with neutral evolution, in which mutations that leave the fitness unchanged are allowed. The simple structure of the fitness landscape enables us to estimate analytically the rates of hill climbing and neutral evolution. In this model, allowing neutral mutations accelerates the rate of evolutionary advancement for low mutation frequencies. These results are applicable to evolution in natural and technological systems.
    Chaos (Woodbury, N.Y.) 10/2008; 18(3):033109. · 1.80 Impact Factor

Publication Stats

4k Citations
262.12 Total Impact Points

Institutions

  • 2013–2014
    • Institute for Systems Biology
      Seattle, Washington, United States
    • Tampere University of Technology
      Tammerfors, Province of Western Finland, Finland
  • 2010–2013
    • University of Vermont
      Burlington, Vermont, United States
  • 1970–2013
    • The University of Calgary
      • Institute for Biocomplexity and Informatics
      Calgary, Alberta, Canada
  • 2011
    • Università Ca' Foscari Venezia
      Venetia, Veneto, Italy
  • 2007
    • Università degli Studi di Modena e Reggio Emilia
      • Department of Communication and Economics
      Modène, Emilia-Romagna, Italy
    • University of Toronto
      Toronto, Ontario, Canada
  • 2006
    • George Mason University
      Fairfax, Virginia, United States
  • 2004–2005
    • University of New Mexico
      • Department of Cell Biology and Physiology
      Albuquerque, NM, United States
  • 1994–2003
    • Santa Fe Institute
      Santa Fe, New Mexico, United States
  • 2000
    • Cornell University
      Ithaca, New York, United States
  • 1981–1993
    • University of Pennsylvania
      • • Department of Biochemistry and Biophysics
      • • Department of Medicine
      Philadelphia, PA, United States
  • 1990
    • Milton Keynes College
      Milton Keynes, England, United Kingdom
  • 1986–1988
    • Clarkson University
      • Department of Biology
      Potsdam, NY, United States
  • 1982
    • Hospital of the University of Pennsylvania
      • Department of Biochemistry and Biophysics
      Philadelphia, Pennsylvania, United States
  • 1972–1975
    • University of Chicago
      Chicago, Illinois, United States
  • 1974
    • National Cancer Institute (USA)
      Maryland, United States
  • 1973
    • University of Rochester
      Rochester, New York, United States
  • 1969
    • Massachusetts Institute of Technology
      Cambridge, Massachusetts, United States
    • University of California, San Francisco
      • Department of Anatomy
      San Francisco, California, United States