What the Escherichia Coli Tells Neurons about Learning

Frontiers in microbiology, 2014

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...

Nature, 2010

The chemotaxis signalling network in Escherichia coli that controls the locomotion of bacteria is a classic model system for signal transduction. This pathway modulates the behaviour of flagellar motors to propel bacteria towards sources of chemical attractants. Although this system relaxes to a steady state in response to environmental changes, the signalling events within the chemotaxis network are noisy and cause large temporal variations of the motor behaviour even in the absence of stimulus. That the same signalling network governs both behavioural variability and cellular response raises the question of whether these two traits are independent. Here, we experimentally establish a fluctuation-response relationship in the chemotaxis system of living bacteria. Using this relationship, we demonstrate the possibility of inferring the cellular response from the behavioural variability measured before stimulus. In monitoring the pre- and post-stimulus switching behaviour of individual bacterial motors, we found that variability scales linearly with the response time for different functioning states of the cell. This study highlights that the fundamental relationship between fluctuation and response is not constrained to physical systems at thermodynamic equilibrium but is extensible to living cells. Such a relationship not only implies that behavioural variability and cellular response can be coupled traits, but it also provides a general framework within which we can examine how the selection of a network design shapes this interdependence.

2021

19 20 The slime mould Physarum polycephalum, an aneural organism, uses information from previous experiences to adjust 21 its behavior but the mechanisms by which this is accomplished remain unknown. This article examines the possible 22 role of oscillations in learning and memory in slime moulds. Slime moulds share surprising similarities with the 23 network of synaptic connections in animal brains. First, their topology derives from a network of interconnected, vein-24 like tubes in which signalling molecules are transported. Second, network motility, which generates slime mould 25 behaviour, is driven by distinct oscillations that organize into spatiotemporal wave patterns. Likewise, neural activity 26 in the brain is organized in a variety of oscillations characterized by different frequencies. Interestingly, the oscillating 27 networks of slime moulds are not precursors of nervous systems but, rather, an alternative architecture. Here we argue 28 that comparable information processing operations can be realized on different architectures sharing similar oscillatory 29 properties. After describing learning abilities and oscillatory activities of P. polycephalum, we explore the relation 30 between network oscillations and learning, and evaluate the organisms global architecture with respect to information 38 restricted to the animal kingdom, but have evolved in all organisms to satisfy the existential needs for survival [1-6]. 39 As Bourgine & Stewart [7] stated, "A system is cognitive if and only if sensory inputs serve to trigger actions in a 40 specific way, so as to satisfy a viability constraint". Cognitive abilities rely on recursive chemical communication of 41 intracellular signalling networks which are highly organized in space and time and which depend on historical and 42 environmental context [8]. The functional link between dynamic processes in cells and their cognitive abilities 43 remains to be elucidated. One of the recurrent patterns observed in cells are self-sustained oscillations which can 44 encode various information [9-12]. The aim of this review is to discuss how learning in a unicellular organism might 45 rely on self-sustained oscillations. Our model system is the acellular slime mould Physarum polycephalum which 46 shows various oscillatory phenomena whose cognitive significance has yet to be demonstrated. 47 Learning, defined as the modification of behavior by experience, is one of the major innovations in the 48 evolution of life. Using past experiences is often critical for optimal decision-making in a fluctuating environment and 49 is involved in every aspect of an organism's life, including foraging and interacting with other individuals. We usually 50 think of learning as a trait that is limited to Neurozoans (i.e., organisms with a central nervous system). Indeed, learning 51 is often equated with neuronal changes like synaptic plasticity, implicitly precluding its existence in aneural organisms 52 such as plants and unicellulars [13]. While the evolutionary benefits of learning are clear, very little is known about 53 its origins. Even the simplest organisms must adapt to changing environments, raising the possibility that mechanisms 54 for learning might pre-date the evolution of nervous systems, possibly existing in a breadth of as-yet unstudied 55 organisms [3,5,6,14-19]. Evidence for learning in single-celled organisms remains scant and, to date, only few 56 unequivocal reports of such processes have been described. Learning in single-celled organisms has been investigated 57 mainly in ciliates and acellular slime moulds, and there are several reliable reports documenting learning in Stentor 58 sp. [20], Paramecium sp. [21], Tetrahymena sp. [22] and P. polycephalum [23]. 59 One of the most fundamental questions in cognitive science is: how can information inferred from interaction 60 with the physical world be encoded in physical/chemical changes in an organism, and how is it decoded as future 61 recall? Although many fundamental processes in the brain have been understood, the current state of the art in 62 neuroscience is still far from providing a complete picture. In particular, there exists no coherent explanation for how 63 higher cognitive function arises from the interplay of elementary biological mechanisms. To truly understand the most 64 fundamental mechanisms underlying learning and memory, it is essential to study the origins of cognition in 65 unicellular organisms that implement learning in a non-neural substrate. 66 Due to its comparably simple structure in relation to its behavioural complexity and due to the ease with which 67 it can be cultivated and manipulated, P. polycephalum presents itself as an ideal model system for relating basal 68 cognitive functions to biological mechanisms [24-26]. In principle, all processes are accessible, in many cases even 69 without disrupting the system and with spatiotemporal resolution constrained only by the experimental setup. In this 93 are pore-like capillaries, allowing respiratory gases, molecules and organelles to be exchanged with the surrounding 94 cytoplasm [31]. This open network allows to maintain homeostasis in cells ranging from 10 square micrometres to 10 95 square meters. The spatial structuration of P. polycephalum's vein network and its dynamics allows an efficient 96 regulation of the cytoplasmic flow within the entire cell [32, 33]. This vein network and the interactions between 97 ectoplasm and the endoplasm have been studied extensively by numerous biophysicists [34, 35]. 98 The vein network is also responsible for cellular motility. P. polycephalum can migrate at a speed of up to 4 99 cm per hour [32] through the interplay of intracellular flow, adhesion and rhythmic contractions of the intracellular 100 actomyosin cytoskeleton [30,32]. These contractions produce a pressure gradient that pushes the endoplasm towards 101 the cell periphery where the veins vanish and the endoplasm can flow freely. Local cytoskeletal reorganization and 102 local alteration of the actin-myosin cortex lead to the formation of pseudopods or fan shaped leading fronts which 103 extend and retract in synchrony with the shuttle streaming of the endoplasm [30,36,37]. Based on internal and external 104 cues, P. polycephalum can adapt and alter its shape, size and motion. The frequency and the amplitude of the 105 cytoskeleton contractions depend on the quality of the environment [38-40]. For instance, higher frequencies are 106 observed in response to attractive, high-quality resources [41], whereas lower frequencies are recorded when P. 107 polycephalum encounters repulsive stimuli such as chemical repellents [40]. As a result, slime moulds migrate toward 108 or away from a variety of external stimuli such as chemicals [38], light [42], temperature [43], humidity [44], gravity

Communicative & Integrative Biology, 2009

It is generally assumed, both in common-sense argumentations and scientific concepts, that brains and neurons represent late evolutionary achievements which are present only in more advanced animals. Here we overview recently published data clearly revealing that our understanding of bacteria, unicellular eukaryotic organisms, plants, brains and neurons, rooted in the Aristotelian philosophy is flawed. Neural aspects of biological systems are obvious already in bacteria and unicellular biological units such as sexual gametes and diverse unicellular eukaryotic organisms. Altogether, processes and activities thought to represent evolutionary 'recent' specializations of the nervous system emerge rather to represent ancient and fundamental cell survival processes. Lessons from Bacteria From communicative behavior, via 'social cognition to intelligence'. Despite their organismal simplicity, bacteria perform complex communications allowing them to deal with complex environment. Bacteria use special chemical 'language' known as quorum sensing to exchange relevant information and coordinate bacterial populations into supracellular assemblies 1-5 resembling multicellular organisms. 6 Bacteria communicate also with eukaryotic hosts. 7-12 Signal transduction in bacteria resembles neural networks. 13-19 Bacteria sense effectively diverse parameters from their environment and their cognitive 20 and intelligent 13,15 behavior implicate that life has neural features already at the prokaryotic level. For example, information processing by cyanobacteria during their adaptation to phosphate fluctuations involves distinct adaptive modes acting as 'experienced' self-constitution of organism under fluctuating environment. 21 It is relevant in this respect that several proteins mediating neurotransmission across synapses in brains have been found in bacteria too. 22,23 Studies on bacterial resistance to diverse antibiotics concluded that bacteria actively resist these antibiotics via 'cognitive' and 'intelligent' activities including innovation, anticipation and learning. 24,25 Lessons from Unicellular Eukaryotes and Gametes Swimming and crawling of unicellular 'neurons' showing 'cognition and intelligence'. Neural parallels are even more convincing in unicellular eukaryotic organisms. For example, ciliate protozoan Paramecium has been devoted a whole chapter in the recently published book, An Introduction to Nervous Systems. 26 Although not covered in detail here, there are several other convincing examples of swimming unicellular eukaryotes with similarly complex sensory and neuronal behavior such as, for example, predatory Euglena or green alga Chlamydomonas. These have even so-called 'eye-apparatus', which commands, via photo-induced intracellular electric signals, their motor motoric flagella. 27,28 Another example of unicellular eukaryotic organisms clearly showing neural behavior is amoeba Physarum polycephalum. This smart organism even solves geometric puzzles if allowed to show his abilities using clever experimental systems. 29-33 This 'cognitive' smartness and behavioral 'intelligence' of this rather unspectacular organism resembling large aggregate of protoplasm is truly amazing. Crawling over agar plates, it shows unicellular forms of 'learning', 'memory', 'anticipation', 'risk management', and other aspects of 'intelligent behavior'. 29-35 Finally, gametes of multicellular organisms express diverse neuronal molecules which underlie cell-cell communication, chemotaxis and other aspects of sexual reproduction in animals. 36-52 For instance sperm cells and oocytes express numerous neurotransmitters and their receptors. 36-48 These are involved, for example, in sperm acrosome reaction after sperm cells successfully identify and approach the receptive oocytes. 37,44,49-52 Lessons from Plants Root apex cells versus neurons. Recent advances in plant cell biology and neurosciences reveal surprising similarities between plants cells and neurons. They are inherently polar, with signal input and signal output poles, secrete signaling molecules via robust endocytosis-driven vesicle recycling apparatus, and are capable of sensory perception and integration of these multiple sensory perceptions into adaptive actions which serve for survival of organisms harboring these cells specialized for signaling and communication. 53-62 Moreover, neurons and plant cells have in common abilities to generate spontaneously action potentials which convey electric signaling across tissues of multicellular organisms (for plant cells, see refs. 63 and 64).

1998

I contrast an ecological account of natural agency with the traditional Cartesian conception using recent research in bacterial cognition and cellular decision making as a test case. I argue that the Cartesian conception-namely, the view that agency presupposes cognition-generates a dilemma between mechanism, the view that bacteria are mere automata, and intellectualism, the view that they exhibit full-blown cognition. Unicellular organisms, however, occupy a middle ground between these two extremes. On the one hand, their capacities and activities are too adaptive to count as mere machines. On the other hand, they lack the open-ended responsiveness of cognitive agents to rational norms. An ecological conception of agency as the gross behavioral capacity to respond to affordances, I argue, does not presuppose cognition and allows for degrees of agency along a continuum, from the simplest adaptive agents, such as unicellular organisms, to the most sophisticated cognitive agents. Bacteria, I conclude, are adaptive agents, hence not mere automata, but not cognitive agents.

bioRxiv (Cold Spring Harbor Laboratory), 2019

The response of microbes to external signals is mediated by biochemical networks with intrinsic time scales. These time scales give rise to a memory that impacts cellular behaviour. Here we study theoretically the role of cellular memory in Escherichia coli chemotaxis. Using an agent-based model, we show that cells with memory navigating rugged chemoattractant landscapes can enhance their drift speed by extracting information from environmental correlations. Maximal advantage is achieved when the memory is comparable to the time scale of fluctuations as perceived during swimming. We derive an analytical approximation for the drift velocity in rugged landscapes that explains the enhanced velocity, and recovers standard Keller-Segel gradient-sensing results in the limits when memory and fluctuation time scales are well separated. Our numerics also show that cellular memory can induce bet-hedging at the population level resulting in long-lived multi-modal distributions in heterogeneous landscapes.

ABSTRACT: The perception of the environment as well as sensorimotor coordination of unicellulars organisms competence as the foundation of a cognitive principle that goes beyond minimum metabolic processes. I study the recognition of the environment in case of slime mold and bacteria as significant learning processes in differentiating between hostile environment or optimal and physical transformations of each agency on the basis of received signals in both the membrane and within the agencies. Sensorimotor coordination mechanisms of metabolic adaptation expanded shapes. Some of these coordination mechanisms are linked to the perception with the environment through pulses, as temporal processing of signals. KEY WORDS: Umwelt, subjectual, enaction, haptic perception, Weber-Fechner Law, bio-fenomenology. RESUMEN: La percepción del entorno, así como la coordinación sensomotora de los organismos unicelulares competen como fundamentos de un principio cognitivo mínimo que va más allá de l...

Journal of Experimental Biology, 2004

Learning is a complex process, involving subtle changes in the properties of neurons and the connections between them. But trying to determine what changes occur in the nervous system during learning, and relating a specific neural network to a behaviour, is difficult in intricate vertebrate brains. So Brian Burrell and Christie Sahley, from Purdue University, turned their attention to learning processes in a much simpler animal -the medicinal leech. These little bloodsuckers' nervous systems are far less complex than those of vertebrates, so it is easier to investigate how transmission at the connection between two neurons -the synapse -changes and may contribute to learning.

Frontiers in Cellular and Infection Microbiology, 2014

The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.