Molecular automata assembly: principles and simulation of bacterial membrane construction

Lahoz-Beltrá, Rafael

doi:10.1016/s0303-2647(97)00048-8

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…However, we consider that due to the continuous nature of enzymatic reactions it would be more realistic to simulate such reactions based on analog circuits instead of digital electronic circuits. Thus, the operational amplifier that has been largely used for modeling and simulation in ecology during the 1970s and in the 1980s in the hardware realization of artificial neural networks (Rietman, 1988) could be used for simulating other dynamical systems, i.e., bacterial taxis learning and evolution, assuming that proteins (i.e., enzymes) are computational elements (Kirby and Conrad, 1986;Lahoz-Beltra et al, 1993Lahoz-Beltra, 1997, 2001) similar to McCulloch-Pitts artificial neurons (Bray, 1995), although several caveats in that regard will be introduced at the end of this section.…”

Section: Discussionmentioning

confidence: 99%

“…Unicellular organisms (i.e., bacteria) are capable of exploring their environment by utilizing the motility of their flagellar apparatus as well as a sophisticated biochemical navigation system that controls the flagellar performances (Macnab, 1996), searching for nutrients or moving away from toxic substances or dangerous physical conditions. The rotation of the flagellar apparatus is an electrochemical phenomenon that involves several proteins and can be considered as a genuine molecular motor (Jones and Aizawa, 1991) whose self-assembly and operation has been modeled and simulated using molecular automata (Lahoz-Beltra, 1997. Clockwise and counter-clockwise rotations of the flagellar motor give motile bacteria the force they need to swim towards favorable environments or away from dangerous ones.…”

Section: Introductionmentioning

confidence: 99%

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Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic ‘two component system’ protein network

2004

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic ‘two component system’ protein network

2004

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“…Other studies suggest that this general approach can be broadly applied to mechanical and enzymatic protein function (Lahoz-Beltra, 1997). In fact a theoretical model of cells as a collection of molecular machines has been proposed (Alberts, 1998) and it seems likely that a combination of molecular machines and network analysis will yield substantial insights into the coupling of cellular information dynamics with cellular function.…”

Section: Cellular Information Utilizationmentioning

confidence: 99%

Information Theory in Living Systems, Methods, Applications, and Challenges

Gatenby

Frieden

2006

Bull. Math. Biol.

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Living systems are distinguished in nature by their ability to maintain stable, ordered states far from equilibrium. This is despite constant buffeting by thermodynamic forces that, if unopposed, will inevitably increase disorder. Cells maintain a steep transmembrane entropy gradient by continuous application of information that permits cellular components to carry out highly specific tasks that import energy and export entropy. Thus, the study of information storage, flow and utilization is critical for understanding first principles that govern the dynamics of life. Initial biological applications of information theory (IT) used Shannon's methods to measure the information content in strings of monomers such as genes, RNA, and proteins. Recent work has used bioinformatic and dynamical systems to provide remarkable insights into the topology and dynamics of intracellular information networks. Novel applications of Fisher-, Shannon-, and Kullback-Leibler informations are promoting increased understanding of the mechanisms by which genetic information is converted to work and order. Insights into evolution may be gained by analysis of the the fitness contributions from specific segments of genetic information as well as the optimization process in which the fitness are constrained by the substrate cost for its storage and utilization. Recent IT applications have recognized the possible role of nontraditional information storage structures including lipids and ion gradients as well as information transmission by molecular flux across cell membranes. Many fascinating challenges remain, including defining the intercellular information dynamics of multicellular organisms and the role of disordered information storage and flow in disease.

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“…According to Tagkopoulos et al (2008), homeostasis explains microbial responses to environmental stimuli—by means of intracellular networks, microbes could exhibit predictive behavior in a fashion similar to metazoan nervous systems. Even more, bacteria are able to explore the environment within which they grow by utilizing the motility of their flagellar system (Lahoz-Beltra, 2007) and deploying a sophisticated “chemotactic” navigation system that samples the environmental conditions surrounding the cell and systematically guides away from the unfavorable conditions and toward the favorable ones.…”

Section: Introductionmentioning

confidence: 99%

Bacterial computing: a form of natural computing and its applications

Lahoz-Beltrá¹,

Navarro²,

Marijuán³

2014

Front. Microbiol.

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The capability to establish adaptive relationships with the environment is an essential characteristic of living cells. Both bacterial computing and bacterial intelligence are two general traits manifested along adaptive behaviors that respond to surrounding environmental conditions. These two traits have generated a variety of theoretical and applied approaches. Since the different systems of bacterial signaling and the different ways of genetic change are better known and more carefully explored, the whole adaptive possibilities of bacteria may be studied under new angles. For instance, there appear instances of molecular “learning” along the mechanisms of evolution. More in concrete, and looking specifically at the time dimension, the bacterial mechanisms of learning and evolution appear as two different and related mechanisms for adaptation to the environment; in somatic time the former and in evolutionary time the latter. In the present chapter it will be reviewed the possible application of both kinds of mechanisms to prokaryotic molecular computing schemes as well as to the solution of real world problems.

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Molecular automata assembly: principles and simulation of bacterial membrane construction

Cited by 10 publications

References 34 publications

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic ‘two component system’ protein network

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic ‘two component system’ protein network

Information Theory in Living Systems, Methods, Applications, and Challenges

Bacterial computing: a form of natural computing and its applications

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