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

Paola, Vieri Di; Marijuán, Pedro C.; Lahoz-Beltrá, Rafael

doi:10.1016/j.biosystems.2004.01.003

Cited by 10 publications

(13 citation statements)

References 48 publications

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“…As a link between the intra-cellular and multi-cellular levels, the adaptive behavior of unicellular organisms was described as an example of learning and memory formation. In one approach, the signal transduction network of bacteria was considered as a learning network of artificial neurons [132]. As we described in Chapter 3.A., the modular reorganization of yeast cells upon various types of stresses provides a general adaptation mechanism in crisis events, which can be regarded as the first step of the learning and memory formation process [54].…”

Section: Network Mechanisms Of Adaptation Learning and Memory Formationmentioning

confidence: 99%

System Level Mechanisms of Adaptation, Learning, Memory Formation and Evolvability: The Role of Chaperone and Other Networks

Gyurko

Sőti

Steták

et al. 2014

CPPS

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During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides 'learningcompetent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

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Section: Network Mechanisms Of Adaptation Learning and Memory Formationmentioning

confidence: 99%

System Level Mechanisms of Adaptation, Learning, Memory Formation and Evolvability: The Role of Chaperone and Other Networks

Gyurko

Sőti

Steták

et al. 2014

CPPS

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show abstract

“…More information on energy taxis and its role in the microbial world can be found in Alexandre et al (2004) and the references therein. An interesting model of energy taxis in bacteria can be found in Di Paola et al (2004), in which the link between the organism's sensory system and its flagellar motor is modelled using McCulloch-Pitts artificial neurons implemented in electronic circuits using operational amplifiers. The Rubam system is inherently capable of modelling energy taxis by mapping the organism's sensory signals (m i ) to its effectors, as will be shown in Section 3.2.…”

Section: Environment/organism Interactionsmentioning

confidence: 99%

A rule-based approach to the modelling of bacterial ecosystems

2006

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“…Bacterial learning and evolution are but two different and related mechanisms for adaptation to the environment, in somatic time the former and in evolutionary time the latter (Di Paola et al, 2004). In this chapter we review the possible application of both mechanisms to prokaryotic molecular computing as well as to the solution of real world problems.…”

Section: Introductionmentioning

confidence: 99%

“…We review how proteins form molecular complexes and networks related to molecular signaling functions and bacterial information processing. Modeling proteins as McCulloch-Pitts neurons reviews a hardware model (Di Paola et al, 2004) demonstrating how proteins of the intervening signal transduction networks could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins (Di Paola et al, 2004) of the signaling system may be considered as McCulloch-Pitts artificial neurons (McCulloch and Pitts, 1943) that transfer and process information from the bacterium's membrane to the flagella motor.…”

Section: Introductionmentioning

confidence: 99%

“…Bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a biochemical navigation system that samples the environmental conditions surrounding the cell (Di Paola et al, 2004). In this chapter we described how in some important aspects proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Cited by 10 publications

References 48 publications

System Level Mechanisms of Adaptation, Learning, Memory Formation and Evolvability: The Role of Chaperone and Other Networks

System Level Mechanisms of Adaptation, Learning, Memory Formation and Evolvability: The Role of Chaperone and Other Networks

A rule-based approach to the modelling of bacterial ecosystems

Bacterial computing: a form of natural computing and its applications

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