Programming a Pavlovian-like conditioning circuit in Escherichia coli

Brain and cognition: The need for a broader biological perspective to overcome old biases

Even with accumulating knowledge, no consensus regarding the understanding of intelligence or cognition exists, and the universal brain bases for these functions remain unclear. Traditionally, our understanding of cognition is based on self-evident principles that appear indisputable when looking only at our species; however, this can distance us from understanding its essence (anthropocentrism, corticocentrism, intellectocentrism, neurocentrism, and idea of orthogenesis of brain evolution). Herein, we use several examples from biology to demonstrate the usefulness of comparative ways of thinking in relativizing these biases. We discuss the relationship between the number of neurons and cognition and draw attention to the highly developed cognitive performance of animals with small brains, to some "tricks" of evolution, to how animals cope with small hardware, to some animals with high-quality brains with an alternative architecture to vertebrates, and to surprising basal cognitive skills in aneural, unicellular organisms. Cognition can be supplemented by the idea of a multicellular organism as a continuum, with many levels of cognitive function, including the possible basal learning in single cells.

Pavlovian-Type Learning in Environmental Bacteria: Regulation of Herbicide Resistance by Arsenic in Pseudomonas putida

The canonical arsRBC genes of the ars1 operon in Pseudomonas putida KT2440, which confer tolerance to arsenate and arsenite, are followed by a series of additional ORFs culminating in phoN1. The phoN1 gene encodes an acetyltransferase that imparts resistance to the glutamine synthetase inhibitor herbicide phosphinothricin (PPT). The co-expression of phoN1 and ars genes in response to environmental arsenic, along with the physiological effects, was analysed through transcriptomics of cells exposed to the oxyanion and phenotypic characterization of P. putida strains deficient in different components of the bifan motif governing arsenic resistance in this bacterium. Genetic separation of arsRBC and phoN1 revealed that their associated phenotypes operate independently, indicating that their natural co-regulation is not functionally required for simultaneous response to the same signal. The data suggest a scenario of associative evolution, akin to Pavlovian conditioning, where two unrelated but frequently co-occurring signals result in one regulating the other's response - even if there is no functional link between the signal and the response. Such surrogate regulatory events may provide an efficient solution to complex regulatory challenges and serve as a genetic patch to address transient gaps in evolving regulatory networks.

Engineering quorum sensing-based genetic circuits enhances growth and productivity robustness of industrial E. coli at low pH

BACKGROUND

Microbial organisms hold significant potential for converting renewable substrates into valuable chemicals. Low pH fermentation in industrial settings offers key advantages, including reduced neutralizer usage and decreased wastewater generation, particularly in the production of amino acids and organic acids. Engineering acid-tolerant strains represents a viable strategy to enhance productivity in acidic environments. Synthetic biology provides dynamic regulatory tools, such as gene circuits, facilitating precise expression of acid resistance (AR) modules in a just-in-time and just-enough manner.

RESULTS

In this study, we aimed to enhance the robustness and productivity of Escherichia coli, a workhorse for amino acid and organic acid production, in industrial fermentation under mild acidic conditions. We employed an Esa-type quorum sensing circuit to dynamically regulate the expression of an AR module (DsrA-Hfq) in a just-in-time and just-enough manner. Through careful engineering of the critical promoter PesaS and stepwise evaluation, we developed an optimal Esa-PBD(L) circuit that conferred upon an industrial E. coli strain SCEcL3 comparable lysine productivity and enhanced yield at pH 5.5 compared to the parent strain at pH 6.8.

CONCLUSIONS

This study exemplifies the practical application of gene circuits in industrial environments, which present challenges far beyond those of well-controlled laboratory conditions.

A heritable iron memory enables decision-making in Escherichia coli

The importance of memory in bacterial decision-making is relatively unexplored. We show here that a prior experience of swarming is remembered when Escherichia coli encounters a new surface, improving its future swarming efficiency. We conducted >10,000 single-cell swarm assays to discover that cells store memory in the form of cellular iron levels. This "iron" memory preexists in planktonic cells, but the act of swarming reinforces it. A cell with low iron initiates swarming early and is a better swarmer, while the opposite is true for a cell with high iron. The swarming potential of a mother cell, which tracks with its iron memory, is passed down to its fourth-generation daughter cells. This memory is naturally lost by the seventh generation, but artificially manipulating iron levels allows it to persist much longer. A mathematical model with a time-delay component faithfully recreates the observed dynamic interconversions between different swarming potentials. We demonstrate that cellular iron levels also track with biofilm formation and antibiotic tolerance, suggesting that iron memory may impact other physiologies.

Iron Memory in E. coli

The importance of memory in bacterial decision-making is relatively unexplored. We show here that a prior experience of swarming is remembered when E. coli encounters a new surface, improving its future swarming efficiency. We conducted >10,000 single-cell swarm assays to discover that cells store memory in the form of cellular iron levels. This memory pre-exists in planktonic cells, but the act of swarming reinforces it. A cell with low iron initiates swarming early and is a better swarmer, while the opposite is true for a cell with high iron. The swarming potential of a mother cell, whether low or high, is passed down to its fourth-generation daughter cells. This memory is naturally lost by the seventh generation, but artificially manipulating iron levels allows it to persist much longer. A mathematical model with a time-delay component faithfully recreates the observed dynamic interconversions between different swarming potentials. We also demonstrate that iron memory can integrate multiple stimuli, impacting other bacterial behaviors such as biofilm formation and antibiotic tolerance.

As soon as there was life, there was danger: the deep history of survival behaviours and the shallower history of consciousness

It is often said that fear is a universal innate emotion that we humans have inherited from our mammalian ancestors by virtue of having inherited conserved features of their nervous systems. Contrary to this common sense-based scientific point of view, I have argued that what we have inherited from our mammalian ancestors, and they from their distal vertebrate ancestors, and they from their chordate ancestors, and so forth, is not a fear circuit. It is, instead, a defensive survival circuit that detects threats, and in response, initiates defensive survival behaviours and supporting physiological adjustments. Seen in this light, the defensive survival circuits of humans and other mammals can be conceptualized as manifestations of an ancient survival function-the ability to detect danger and respond to it-that may in fact predate animals and their nervous systems, and perhaps may go back to the beginning of life. Fear, on the other hand, from my perspective, is a product of cortical cognitive circuits. This conception is not just of academic interest. It also has practical implications, offering clues as to why efforts to treat problems related to fear and anxiety are not more effective, and what might make them better. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

Viewpoint: Pavlovian Materials-Functional Biomimetics Inspired by Classical Conditioning

Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli-responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as "Pavlovian materials" are described, whose stimuli-responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus-triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli-responsive and shape-memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever-more intelligent functions are expected.

Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators

Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical conditioning is an elementary form of associative learning, which inspires us to explore simplified routes even for inanimate materials to respond to new, initially neutral stimuli. Here, we demonstrate that soft actuators composed of thermoresponsive liquid crystal networks "learn" to respond to light upon a conditioning process where light is associated with heating. We apply the concept to soft microrobotics, demonstrating a locomotive system that "learns to walk" under periodic light stimulus, and gripping devices able to "recognize" irradiation colors. We anticipate that actuators that algorithmically emulate elementary aspects of associative learning and whose sensitivity to new stimuli can be conditioned depending on past experiences may provide new routes toward adaptive, autonomous soft microrobotics.

Programmable responsive hydrogels inspired by classical conditioning algorithm

Living systems have inspired research on non-biological dynamic materials and systems chemistry to mimic specific complex biological functions. Upon pursuing ever more complex life-inspired non-biological systems, mimicking even the most elementary aspects of learning is a grand challenge. We demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning, i.e., conditioning, which is among the simplest forms of learning. Algorithmically, associative learning minimally requires responsivity to two different stimuli and a memory element. Herein, nanoparticles form the memory element, where a photoacid-driven pH-change leads to their chain-like assembly with a modified spectral behaviour. On associating selected light irradiation with heating, the gel starts to melt upon the irradiation, originally a neutral stimulus. A logic diagram describes such an evolution of the material response. Coupled chemical reactions drive the system out-of-equilibrium, allowing forgetting and memory recovery. The findings encourage to search non-biological materials towards associative and dynamic properties.

Living systems inspired research on systems chemistry to mimic specific complex biological functions, but mimicking even the most elementary aspects of learning is a grand challenge. Here the authors demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning.

Design and synthesis of synthetic UP elements for modulation of gene expression in Escherichia coli

Metabolic engineering requires fine-tuned gene expression for most pathway optimization applications. To develop a suitable suite of promoters, traditional bacterial promoter engineering efforts have focused on modifications to the core region, especially the −10 and −35 regions, of native promoters. Here, we demonstrate an alternate, unexplored route of promoter engineering through randomization of the UP element of the promoter—a region that contacts the alpha subunit carboxy-terminal domain instead of the sigma subunit of the RNA polymerase holoenzyme. Through this work, we identify five novel UP element sequences through library-based searches in Escherichia coli. The resulting elements were used to activate the E. coli core promoter, rrnD promoter, to levels on par and higher than the prevalent strong bacterial promoter, OXB15. These relative levels of expression activation were transferrable when applied upstream of alternate core promoter sequences, including rrnA and rrnH. This work thus presents and validates a novel strategy for bacterial promoter engineering with transferability across varying core promoters and potential for transferability across bacterial species.

Computing with biological switches and clocks

The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.

Rational Design of an Ultrasensitive Quorum-Sensing Switch

One of the purposes of synthetic biology is to develop rational methods that accelerate the design of genetic circuits, saving time and effort spent on experiments and providing reliably predictable circuit performance. We applied a reverse engineering approach to design an ultrasensitive transcriptional quorum-sensing switch. We want to explore how systems biology can guide synthetic biology in the choice of specific DNA sequences and their regulatory relations to achieve a targeted function. The workflow comprises network enumeration that achieves the target function robustly, experimental restriction of the obtained candidate networks, global parameter optimization via mathematical analysis, selection and engineering of parts based on these calculations, and finally, circuit construction based on the principles of standardization and modularization. The performance of realized quorum-sensing switches was in good qualitative agreement with the computational predictions. This study provides practical principles for the rational design of genetic circuits with targeted functions.

Phylogenetic origins of biological cognition: convergent patterns in the early evolution of learning

Various forms of elementary learning have recently been discovered in organisms lacking a nervous system, such as protists, fungi and plants. This finding has fundamental implications for how we view the role of convergent evolution in biological cognition. In this article, I first review the evidence for basic forms of learning in aneural organisms, focusing particularly on habituation and classical conditioning and considering the plausibility for convergent evolution of these capacities. Next, I examine the possible role of convergent evolution regarding these basic learning abilities during the early evolution of nervous systems. The evolution of nervous systems set the stage for at least two major events relevant to convergent evolution that are central to biological cognition: (i) nervous systems evolved, perhaps more than once, because of strong selection pressures for sustaining sensorimotor strategies in increasingly larger multicellular organisms and (ii) associative learning was a subsequent adaptation that evolved multiple times within the neuralia. Although convergent evolution of basic forms of learning among distantly related organisms such as protists, plants and neuralia is highly plausible, more research is needed to verify whether these forms of learning within the neuralia arose through convergent or parallel evolution.

Using Sub-Network Combinations to Scale Up an Enumeration Method for Determining the Network Structures of Biological Functions

Deduction of biological regulatory networks from their functions is one of the focus areas of systems biology. Among the different techniques used in this reverse-engineering task, one powerful method is to enumerate all candidate network structures to find suitable ones. However, this method is severely limited by calculation capability: due to the brute-force approach, it is infeasible for networks with large number of nodes to be studied using traditional enumeration method because of the combinatorial explosion. In this study, we propose a new reverse-engineering technique based on the enumerating method: sub-network combinations. First, a complex biological function is divided into several sub-functions. Next, the three-node-network enumerating method is applied to search for sub-networks that are able to realize each of the sub-functions. Finally, complex whole networks are constructed by enumerating all possible combinations of sub-networks. The optimal ones are selected and analyzed. To demonstrate the effectiveness of this new method, we used it to deduct the network structures of a Pavlovian-like function. The whole Pavlovian-like network was successfully constructed by combining robust sub-networks, and the results were analyzed. With sub-network combination, the complexity has been largely reduced. Our method also provides a functional modular view of biological systems.

Layering genetic circuits to build a single cell, bacterial half adder

BACKGROUND

Gene regulation in biological systems is impacted by the cellular and genetic context-dependent effects of the biological parts which comprise the circuit. Here, we have sought to elucidate the limitations of engineering biology from an architectural point of view, with the aim of compiling a set of engineering solutions for overcoming failure modes during the development of complex, synthetic genetic circuits.

RESULTS

Using a synthetic biology approach that is supported by computational modelling and rigorous characterisation, AND, OR and NOT biological logic gates were layered in both parallel and serial arrangements to generate a repertoire of Boolean operations that include NIMPLY, XOR, half adder and half subtractor logics in a single cell. Subsequent evaluation of these near-digital biological systems revealed critical design pitfalls that triggered genetic context-dependent effects, including 5' UTR interferences and uncontrolled switch-on behaviour of the supercoiled σ54 promoter. In particular, the presence of seven consecutive hairpins immediately downstream of the promoter transcription start site severely impeded gene expression.

CONCLUSIONS

As synthetic biology moves forward with greater focus on scaling the complexity of engineered genetic circuits, studies which thoroughly evaluate failure modes and engineering solutions will serve as important references for future design and development of synthetic biological systems. This work describes a representative case study for the debugging of genetic context-dependent effects through principles elucidated herein, thereby providing a rational design framework to integrate multiple genetic circuits in a single prokaryotic cell.

From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.

Optogenetic characterization methods overcome key challenges in synthetic and systems biology

Systems biologists aim to understand how organism-level processes, such as differentiation and multicellular development, are encoded in DNA. Conversely, synthetic biologists aim to program systems-level biological processes, such as engineered tissue growth, by writing artificial DNA sequences. To achieve their goals, these groups have adapted a hierarchical electrical engineering framework that can be applied in the forward direction to design complex biological systems or in the reverse direction to analyze evolved networks. Despite much progress, this framework has been limited by an inability to directly and dynamically characterize biological components in the varied contexts of living cells. Recently, two optogenetic methods for programming custom gene expression and protein localization signals have been developed and used to reveal fundamentally new information about biological components that respond to those signals. This basic dynamic characterization approach will be a major enabling technology in synthetic and systems biology.