Metabolic stability and epigenesis in randomly constructed genetic nets

Kauffman, Stuart

doi:10.1016/0022-5193(69)90015-0

Cited by 4,136 publications

(2,886 citation statements)

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“…In a pioneering study of pattern formation, Turing (1952) proposed a mechanism whereby a spatially uniform state is destabilized by the cell-cell interactions, leading to the formation of a spatially periodic pattern (Turing, 1952) (see also Newman and Comper (1990)). The existence of multiple stable cellular states in genetic networks was found by Kauffman, which correspond to different cell types (Kauffman, 1969). Although these studies were based on a simple abstract model of a cellular system, they succeeded in extracting the universal logic in all multicellular systems, ranging from a very primitive one to the present sophisticated one.…”

Section: Model For Cell Societymentioning

confidence: 99%

Origin of multicellular organisms as an inevitable consequence of dynamical systems

Furusawa

Kaneko

2002

Anat. Rec.

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The origin of multicellular organisms is studied by considering a cell system that satisfies minimal conditions, that is, a system of interacting cells with intracellular biochemical dynamics, and potentiality in reproduction. Three basic features in multicellular organismscellular diversification, robust developmental process, and emergence of germ-line cells-are found to be general properties of such a system. Irrespective of the details of the model, such features appear when there are complex oscillatory dynamics of intracellular chemical concentrations. Cells differentiate from totipotent stem cells into other cell types due to instability in the intracellular dynamics with cell-cell interactions, as explained by our isologous diversification theory (Furusawa and Kaneko, 1998a;Kaneko and Yomo, 1997). This developmental process is shown to be stable with respect to perturbations, such as molecular fluctuations and removal of some cells. By further imposing an adequate cell-typedependent adhesion force, some cells are released, from which the next generation cell colony is formed, and a multicellular organism life-cycle emerges without any finely tuned mechanisms. This recursive production of multicellular units is stabilized if released cells are few in number, implying the separation of germ cell lines. Furthermore, such an organism with a variety of cellular states and robust development is found to maintain a larger growth speed as an ensemble by achieving a cooperative use of resources, compared to simple cells without differentiation. Our results suggest that the emergence of multicellular organisms is not a "difficult problem" in evolution, but rather is a natural consequence of a cell colony that can grow continuously. Anat Rec 268: 327-342, 2002.

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Section: Model For Cell Societymentioning

confidence: 99%

Origin of multicellular organisms as an inevitable consequence of dynamical systems

Furusawa

Kaneko

2002

Anat. Rec.

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“…C = 2·|A| n·(n−1) ) where n = |V |. The connectivity c [1] is the average in-degree which also equals n · C (resp. (n−1)·C 2 ).…”

Section: Acknowledgementsmentioning

confidence: 99%

“…The theory of biological regulatory networks [1,3,4,65] was born in parallel to the theory of neural networks [66,67]. It is now an extensive research topic which aims at interpreting the "omic" data from bio-array devices.…”

Section: Appendix B Biological Regulation Network Modelingmentioning

confidence: 99%

“…Since researches have focused on modeling genetic regulation networks by Boolean automata networks, many authors have noticed that the num-ber of attractors observed experimentally (i.e., the number of their different possible asymptotic behaviours such as fixed or cyclic configurations of expressed and/or silent genes) is in general very small compared to the number of different network configurations: a Boolean network of size n has 2 n different configurations but only around √ n attractors [1,2]. Although this property is of mathematical order, it has real pertinence also from the biological point of view.…”

Section: Introductionmentioning

confidence: 99%

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“Immunetworks”, intersecting circuits and dynamics

Demongeot

Elena

Noual

et al. 2011

Journal of Theoretical Biology

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This paper proposes a study of biological regulatory networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system ("immunetworks"), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that "inter-lock".

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confidence: 99%

Logical Modelling of Regulatory Networks, Methods and Applications

Chaouiya

Rémy

2013

Bull Math Biol

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About 40 years ago, seminal work by S. Kauffman (1969) and R. Thomas (1973) paved the way to the establishment of a coarse-grained, "logical" modelling of gene regulatory networks. This gave rise to an increasingly active field of research, which ranges from theoretical studies to models of networks controlling a variety of cellular processes (Bornholdt 2008;Glass and Siegelmann 2010). Briefly, in these models, genes (or regulatory components) are assigned discrete values that account for their functional levels of expression (or activity). A regulatory function defines the evolution of the gene level, depending on the levels of its regulators. This abstracted representation of molecular mechanisms is very convenient for handling large networks for which precise kinetic data are lacking.Within the logical framework, one can distinguish several approaches that differ in the way of defining a model and its behaviour. In random Boolean networks, first introduced by S. Kauffman, the components are randomly assigned a set of regulators and a regulatory function that drives their evolution, depending on these regulators (Kauffman 1969). In threshold Boolean networks, the function is derived from a given regulatory structure as a sum of the input signals, possibly considering a threshold (Bornholdt 2008;Li et al. 2004). In the generalised logical approach introduced by R. Thomas, the logical functions are general, but are constrained by the regulatory graph. Whenever necessary or useful, the formalism supports multi-valued variables C. Chaouiya ( )

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Metabolic stability and epigenesis in randomly constructed genetic nets

Cited by 4,136 publications

References 10 publications

Origin of multicellular organisms as an inevitable consequence of dynamical systems

Origin of multicellular organisms as an inevitable consequence of dynamical systems

“Immunetworks”, intersecting circuits and dynamics

Logical Modelling of Regulatory Networks, Methods and Applications

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