Molecular circuits for associative learning in single-celled organisms

Fernando, Chrisantha; Liekens, Anthony; Bingle, Lewis; Beck, Christian; Lenser, Thorsten; Stekel, Dov J.; Rowe, Jonathan E.

doi:10.1098/rsif.2008.0344

Cited by 110 publications

(110 citation statements)

References 28 publications

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…The idea that networks of genes can perform computational functions in the cell is now well established (see Fernando et al, 2009;Haynes et al, 2008;Ben-Hur and Siegelmann, 2004;Ziv et al, 2007). As a simple example consider the regulation of metabolic pathways in bacteria.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter settings for information processing in gene regulatory networks

2011

View full text Add to dashboard Cite

Gene networks can often be interpreted as computational circuits. This article investigates the computational properties of gene regulatory networks defined in terms of the speed and the accuracy of the output of a gene network. It will be shown that there is no single optimal set of parameters, but instead, there is a trade-off between speed and accuracy. Using the trade-off it will also be shown how systems with various parameters can be ranked with respect to their computational efficiency. Numerical analysis suggests that the trade-off can be improved when the output gene is repressing itself, even though the accuracy or the speed of the auto-regulated system may be worse than the unregulated system.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal parameter settings for information processing in gene regulatory networks

2011

View full text Add to dashboard Cite

show abstract

“…Here X can activate the response R whereas Y will do it (when X ¼ 0) only if an intermediate module M (that needs to be previously activated by X þ Y) has the right expression level. This kind of design and others of similar inspiration [23] require sophisticated wiring if implemented inside one cell.…”

Section: The Logic Of Multicellular Learningmentioning

confidence: 99%

“…These studies propose different ways of approaching the problem of building synthetic systems capable of diverse levels of bacterial information processing. One example is the AL circuit presented by Fernando et al that could be implemented in Escherichia coli as a model organism [23]. It was inspired by previous theoretical studies that used model neural networks to explain the process under a minimal set of assumptions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synthetic associative learning in engineered multicellular consortia

Macía

Vidiella

Solé

2017

J. R. Soc. Interface.

View full text Add to dashboard Cite

Associative learning (AL) is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was recognized early as a general trait of complex multicellular organisms but is also found in 'simpler' ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell, and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display AL responses to two classes of stimuli and displaying long-and short-term memory (i.e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.

show abstract

“…We have a situation where the metabolism of an organism enables and is dependent upon systems-level, emergent properties including the capacity of the organism to create an organism/environment interaction, to constitute information about itself and its 38 Tagkopoulos et al (2008) and Fernando et al (2009) confirm associative learning in colonies of bacteria. It is still, as far as I can see, an insufficiently explored question whether a single bacterial cell is capable of associative learning.…”

Section: Metabolism Is Learningmentioning

confidence: 99%

The Metabolic Core of Environmental Education

Affifi

2016

Stud Philos Educ

View full text Add to dashboard Cite

I consider the case of the ''simplest'' living beings-bacteria-and examine how their embodied activity constitutes an organism/environment interaction, out of which emerges the possibility of learning from an environment. I suggest that this mutual coemergence of organism and environment implies a panbiotic educational interaction that is at once the condition for, and achievement of, all living beings. Learning and being learned from are entangled in varied ways throughout the biosphere. Education is not an exclusively human project, it is part of the ancient evolutionary process of elaborating and diversifying the relational possibilities inherent in metabolism that has brought forth our diverse and flourishing world. Abstracted from context, ''education'' appears as a set of ways in which humans actively engage and take responsibility for the learning outcomes of relationships with each other. Insofar as we consider this distinction absolute rather than a performed construct to be assessed by its consequences, we blind ourselves to the educative dimension of other species as well as our many miseducative engagements with them. If learning processes are inherent and constitutive of ecological communities, education theorists should devote their pedagogical sensitivities and insights to the crucial challenge of developing educative sustainable human-nature relations.

show abstract

Molecular circuits for associative learning in single-celled organisms

Cited by 110 publications

References 28 publications

Optimal parameter settings for information processing in gene regulatory networks

Optimal parameter settings for information processing in gene regulatory networks

Synthetic associative learning in engineered multicellular consortia

The Metabolic Core of Environmental Education

Contact Info

Product

Resources

About