Synthetic protocell biology: from reproduction to computation

Fundamental constraints to the logic of living systems

It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could have resulted in organisms with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve. Are these limitations relevant in shaping the fabric of the possible? Here, we argue that fundamental constraints are associated with the logic of living matter. We illustrate this idea by considering the thermodynamic properties of living systems, the linear nature of molecular information, the cellular nature of the building blocks of life, multicellularity and development, the threshold nature of computations in cognitive systems and the discrete nature of the architecture of ecosystems. In all these examples, we present available evidence and suggest potential avenues towards a well-defined theoretical formulation.

Proto-neural networks from thermal proteins

Proteinoids are synthetic polymers that have structural similarities to natural proteins, and their formation is achieved through the application of heat to amino acid combinations in a dehydrated environment. The thermal proteins, initially synthesised by Sidney Fox during the 1960s, has the ability to undergo self-assembly, resulting in the formation of microspheres that resemble cells. These microspheres have fascinating biomimetic characteristics. In recent studies, substantial advancements have been made in elucidating the electrical signalling phenomena shown by proteinoids, hence showcasing their promising prospects in the field of neuro-inspired computing. This study demonstrates the advancement of experimental prototypes that employ proteinoids in the construction of fundamental neural network structures. The article provides an overview of significant achievements in proteinoid systems, such as the demonstration of electrical excitability, emulation of synaptic functions, capabilities in pattern recognition, and adaptability of network structures. This study examines the similarities and differences between proteinoid networks and spontaneous neural computation. We examine the persistent challenges associated with deciphering the underlying mechanisms of emergent proteinoid-based intelligence. Additionally, we explore the potential for developing bio-inspired computing systems using synthetic thermal proteins in forthcoming times. The results of this study offer a theoretical foundation for the advancement of adaptive, self-assembling electronic systems that operate using artificial bio-neural principles.

Multistable Protocells Can Aid the Evolution of Prebiotic Autocatalytic Sets

We present a simple mathematical model that captures the evolutionary capabilities of a prebiotic compartment or protocell. In the model, the protocell contains an autocatalytic set whose chemical dynamics is coupled to the growth-division dynamics of the compartment. Bistability in the dynamics of the autocatalytic set results in a protocell that can exist with two distinct growth rates. Stochasticity in chemical reactions plays the role of mutations and causes transitions from one growth regime to another. We show that the system exhibits 'natural selection', where a 'mutant' protocell in which the autocatalytic set is active arises by chance in a population of inactive protocells, and then takes over the population because of its higher growth rate or 'fitness'. The work integrates three levels of dynamics: intracellular chemical, single protocell, and population (or ecosystem) of protocells.

The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines

In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological agents such as bacteria, bees, C. elegans, primates and humans; as well as AI technologies such as deep neural networks, multi-agent bots, social robots, Siri and Watson. A complexity-based conceptualization provides a useful framework for identifying defining features and classes of conscious and intelligent systems. Starting with cognitive and clinical metrics of consciousness that assess awareness and wakefulness, we ask how AI and synthetically engineered life-forms would measure on homologous metrics. We argue that awareness and wakefulness stem from computational and autonomic complexity. Furthermore, tapping insights from cognitive robotics, we examine the functional role of consciousness in the context of evolutionary games. This points to a third kind of complexity for describing consciousness, namely, social complexity. Based on these metrics, our morphospace suggests the possibility of additional types of consciousness other than biological; namely, synthetic, group-based and simulated. This space provides a common conceptual framework for comparing traits and highlighting design principles of minds and machines.

Fake cells and the aura of life: A philosophical diagnostic of synthetic life

Synthetic biology is often seen as the engineering turn in biology. Philosophically speaking, entities created by synthetic biology, from synthetic cells to xenobots, challenge the ontological divide between the organic and inorganic, as well as between the natural and the artificial. Entities such as synthetic cells can be seen as hybrid or transitory objects, or neo-things. However, what has remained philosophically underexplored so far is the impact these hybrid neo-things will have on (our phenomenological experience of) the living world. By extrapolating from Walter Benjamin's account of how technological reproducibility affects the aura of art, we embark upon an exploratory inquiry that seeks to fathom how the technological reproducibility of life itself may influence our experience and understanding of the living. We conclude that, much as technologies that enabled reproduction corroded the aura of original artworks (as Benjamin argued), so too will the aura of life be under siege in the era of synthetic lifeforms. This article zooms in on a specific case study, namely the research project Building a Synthetic Cell (BaSyC) and its mission to create a synthetic cell-like entity, as autonomous as possible, focusing on the properties that differentiate organic from synthetic cells.

Provenance of life: Chemical autonomous agents surviving through associative learning

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning systems have been widely studied in cognitive science and artificial intelligence but are most commonly implemented in highly complex or carefully engineered systems, such as animal brains, artificial neural networks, DNA computing systems, and gene regulatory networks, among others. The ability to encode environmental information and use it to make simple predictions is a benchmark of biological resilience and underpins a plethora of adaptive responses in the living hierarchy, spanning prey animal species anticipating the arrival of predators to epigenetic systems in microorganisms learning environmental correlations. Given the ubiquitous and essential presence of learning behaviors in the biosphere, we aimed to explore whether simple, nonliving dissipative structures could also exhibit associative learning. Inspired by previous modeling of associative learning in chemical networks, we simulated simple systems composed of long- and short-term memory chemical species that could encode the presence or absence of temporal correlations between two external species. The ability to learn this association was implemented in Gray-Scott reaction-diffusion spots, emergent chemical patterns that exhibit self-replication and homeostasis. With the novel ability of associative learning, we demonstrate that simple chemical patterns can exhibit a broad repertoire of lifelike behavior, paving the way for in vitro studies of autonomous chemical learning systems, with potential relevance to artificial life, origins of life, and systems chemistry. The experimental realization of these learning behaviors in protocell or coacervate systems could advance a new research direction in astrobiology, since our system significantly reduces the lower bound on the required complexity for autonomous chemical learning.

From the origin of life to pandemics: emergent phenomena in complex systems

When a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-temporal scales might spontaneously arise. This non-trivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems-from physical to biological and social-and is often related to collective behaviour. It is ubiquitous, from non-living entities such as oscillators that under specific conditions synchronize, to living ones, such as birds flocking or fish schooling. Despite the ample phenomenological evidence of the existence of systems' emergent properties, central theoretical questions to the study of emergence remain unanswered, such as the lack of a widely accepted, rigorous definition of the phenomenon or the identification of the essential physical conditions that favour emergence. We offer here a general overview of the phenomenon of emergence and sketch current and future challenges on the topic. Our short review also serves as an introduction to the theme issue Emergent phenomena in complex physical and socio-technical systems: from cells to societies, where we provide a synthesis of the contents tackled in the issue and outline how they relate to these challenges, spanning from current advances in our understanding on the origin of life to the large-scale propagation of infectious diseases. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Ten future challenges for synthetic biology

After 2 decades of growth and success, synthetic biology has now become a mature field that is driving significant innovation in the bioeconomy and pushing the boundaries of the biomedical sciences and biotechnology. So what comes next? In this article, 10 technological advances are discussed that are expected and hoped to come from the next generation of research and investment in synthetic biology; from ambitious projects to make synthetic life, cell simulators and custom genomes, through to new methods of engineering biology that use automation, deep learning and control of evolution. The non-exhaustive list is meant to inspire those joining the field and looks forward to how synthetic biology may evolve over the coming decades.

Synthetic Biology: Old and New Dilemmas—The Case of Artificial Life

This article aims to examine some of the ethical questions emerging from the use of already existing biotechnological tools and the issues which might occur by synthetic biology’s potential future possibilities. In the first part, the essence of synthetic biology and its relation to the contemporary biotechnological research is analyzed. In the second part, the article examines whether the new biotechnological inventions pose new or revive old moral questions about the ethics of science, engineering, and technology in general. After briefly addressing some of the various issues which are raised by experts, philosophers, but also the general public, concerning synthetic biology in general, it focuses on the topic of “artificial life creation” and presents moral reasons which may or may not allow it. The topic is approached by referring to consequentialist, deontological, but also, virtue theory arguments for and against it and the possibility of a partial permission of “artificial life” experiments, asking whether the benefits outweigh the risks and moral implications is explored. Finally, it proposes an argument in favor of the future exploration of biological innovation, underlying the need for a more balanced access to its beneficial results.

Programmable Aggregation of Artificial Cells with DNA Signals

Cell aggregation is a complex behavior that is closely related to the viability, differentiation, and migration of cells. An effort to create synthetic analogs could lead to considerable advances in cell physiology and biophysics. Rendering and modulating such a dynamic artificial cell system require mechanisms for receiving, transducing, and transmitting intercellular signals, yet effective tools are limited at present. Here we construct synthetic cells from engineered lipids and show their programmable aggregation behaviors using DNA oligonucleotides as signaling molecules. The artificial cells have transmembrane channels made of DNA origami that are used to recognize and process intercellular signals. We demonstrate that multiple small vesicles aggregate onto a giant vesicle after a transduction of external DNA signals by an intracellular enzyme and that the small vesicles dissociate when receiving "release" signals. This work provides new possibilities for building synthetic protocells capable of chemical communication and coordination.

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.

Using reactive dissipative particle dynamics to understand local shape manipulation of polymer vesicles

Biological cells have long been of interest to researchers due to their capacity to actively control their shape. Accordingly, there is significant interest in generating simplified synthetic protocells that can alter their shape based on an externally or internally generated stimulus. To date, most progress has been made towards controlling the global shape of a protocell, whereas less is known about generating a local shape change. Here, we seek to better understand the possible mechanisms for producing local morphological changes in a popular protocell system, the block copolymer vesicle. Accordingly, we have combined Dissipative Particle Dynamics (DPD) and the Split Reactive Brownian Dynamics algorithm (SRBD) to produce a simulation tool that is capable of modeling the dynamics of self-assembled polymer structures as they undergo chemical reactions. Using this Reactive DPD or RDPD method, we investigate local morphological change driven by either the microinjection of a stimulus or an enzymatically-produced stimulus. We find that sub-vesicle-scale morphological change can be induced by either a solvent stimulus that swells the vesicle membrane, or by a reactant stimulus that alters the chemistry of the block polymer in the membrane corona. Notably, the latter method results in a more persistent local deformation than the former, which we attribute to the slower diffusion of polymer chains relative to the solvent. We quantify this deformation and show that it can be modulated by altering the interaction parameter of the parts of the polymer chain that are affected by the stimulus.

Investigating the Evolution and Development of Biological Systems from the Perspective of Thermo-Kinetics and Systems Theory

Life itself is grander than the sum of its constituent molecules. Any living organism may be regarded as a part of a dissipative process that connects irreversible energy consumption with growth, reproduction, and evolution. Under energy-fuelled, far-from-equilibrium conditions, chemical systems capable of exponential growth can manifest a specific form of stability- dynamic kinetic stability (DKS) - indicating the persistence of self-reproducible entities. This kinetic behavior is associated with thermodynamic conditions far from equilibrium leading to an evolutionary view of the origin of life in which increasing entities have to be associated with the dissipation of free energy. This review aims to reformulate Darwinian theory in physicochemical terms so that it can handle both animate and inanimate systems, thus helping to overcome this theoretical divide. The expanded formulation is based on the principle of dynamic kinetic stability and evidence from the emerging field of systems chemistry. Although the classic Darwinian theory is useful for understanding the origins and evolution of species, it is not meant to primarily build an explicit framework for predicting potential evolution routes. Throughout the last century, the inherently systemic and dynamic nature of the biological systems has been brought to the attention of researchers. During the last decades, "systems" approaches to biology and genome evolution are gaining ever greater significance providing the possibility of a deeper interpretation of the basic concepts of life. Further progress of this approach depends on crossing disciplinary boundaries and complex simulations of biological systems. Evolutionary systems biology (ESB) through the integration of methods from evolutionary biology and systems biology aims to the understanding of the fundamental principles of life as well as the prediction of biological systems evolution.

Dissipative self-assembly, competition and inhibition in a self-reproducing protocell model

The bottom-up synthesis of artificial, life-like systems promises to enable the study of emergent properties distinctive to life. Here, we report protocell systems generated from phase-separated building blocks. Vesicle protocells self-reproduce through a phase-transfer mechanism, catalysing their own formation. Dissipative self-assembly by the protocells is achieved when a hydrolysis step to destroy the surfactant is introduced. Competition between micelle and vesicle based replicators for a common feedstock shows that environmental conditions can control what species predominates: under basic conditions vesicles predominate, but in a neutral medium micelles are selected for via a mechanism which inhibits vesicle formation. Finally, the protocells enable orthogonal reactivity by catalysing in situ formation of an amphiphilic organocatalyst, which after incorporation into the vesicle bilayer enantioselectively forms a secondary product.

Towards Design of Self-Organizing Biomimetic Systems

The ability of designing biosynthetic systems with well-defined functional biomodules from scratch is an ambitious and revolutionary goal to deliver innovative, engineered solutions to future challenges in biotechnology and process systems engineering. In this work, several key challenges including modularization, functional biomodule identification, and assembly are discussed. In addition, an in silico protocell modeling approach is presented as a foundation for a computational model-based toolkit for rational analysis and modular design of biomimetic systems.

The Origin of a Trans-Generational Organization in the Phenomenon of Biogenesis

One of the central issues of the whole process of biogenesis is how to understand the progressive constitution of a large (in spatial and temporal terms) system that transcends the individual sphere of proto-metabolic organizations and includes collective networks, both synchronous (i.e., proto-ecosystem webs) and asynchronous (i.e., trans-generational protocell populations). This paper analyzes the appearance of a minimal form of reproduction in the process of biogenesis from an organizational perspective. This perspective highlights the problem of how a process transcending the actual organization of the reproducing entities (i.e., protocells) could have a causal power. It is proposed that this problem may be explained if we consider that reproduction generates a kind of feedback between the actual concatenation of the processes of each reproducing cycle and the type continuity that a reliable iteration of these cycles creates. Thus, reproduction generates a new form of self-maintaining system linking "organismal" and "evolutionary" domains, since the consequence of the iteration of self-reproducing cycles is the long-term continuity of a specific type of SM compartmentalized organization, and the functional role of a particular self-reproducing organization (token) lies in its capacity to trigger a diachronic succession of similar self-reproducing organizations, i.e., a lineage.

Minimalist Protocell Design: A Molecular System Based Solely on Proteins that Form Dynamic Vesicular Membranes Embedding Enzymatic Functions

Life in its molecular context is characterized by the challenge of orchestrating structure, energy and information processes through compartmentalization and chemical transformations amenable to mimicry of protocell models. Here we present an alternative protocell model incorporating dynamic membranes based on amphiphilic elastin-like proteins (ELPs) rather than phospholipids. For the first time we demonstrate the feasibility of combining vesicular membrane formation and biocatalytic activity with molecular entities of a single class: proteins. The presented self-assembled protein-membrane-based compartments (PMBCs) accommodate either an anabolic reaction, based on free DNA ligase as an example of information transformation processes, or a catabolic process. We present a catabolic process based on a single molecular entity combining an amphiphilic protein with tobacco etch virus (TEV) protease as part of the enclosure of a reaction space and facilitating selective catalytic transformations. Combining compartmentalization and biocatalytic activity by utilizing an amphiphilic molecular building block with and without enzyme functionalization enables new strategies in bottom-up synthetic biology, regenerative medicine, pharmaceutical science and biotechnology.

What can we learn from the construction of in vitro replication systems?

Replication is a central function of living organisms. Several types of replication systems have been constructed in vitro from various molecules, including peptides, DNA, RNA, and proteins. In this review, I summarize the progress in the construction of replication systems over the past few decades and discuss what we can learn from their construction. I introduce various types of replication systems, supporting the feasibility of the spontaneous appearance of replication early in Earth's history. In the latter part of the review, I focus on parasitic replicators, one of the largest obstacles for sustainable replication. Compartmentalization is discussed as a possible solution.

The evolutionary dynamics of metabolic protocells

Protocell multilevel selection models have been proposed to study the evolutionary dynamics of vesicles encapsulating a set of replicating, competing and mutating sequences. The frequency of the different sequence types determines protocell survival through a fitness function. One of the defining features of these models is the genetic load generated when the protocell divides and its sequences are assorted between the offspring vesicles. However, these stochastic assortment effects disappear when the redundancy of each sequence type is sufficiently high. The fitness dependence of the vesicle with its sequence content is usually defined without considering a realistic account on how the lower level dynamics would specify the protocell fitness. Here, we present a protocell model with a fitness function determined by the output flux of a simple metabolic network with the aim of understanding how the evolution of both kinetic and topological features of metabolism would have been constrained by the particularities of the protocell evolutionary dynamics. In our model, the sequences inside the vesicle are both the carriers of information and Michaelis-Menten catalysts exhibiting saturation. We found that the saturation of the catalysts controlling the metabolic fluxes, achievable by modifying the kinetic or stoichiometric parameters, provides a mechanism to ameliorate the assortment load by increasing the redundancy of the catalytic sequences required to achieve the maximum flux. Regarding the network architecture, we conclude that combinations of parallel network motifs and bimolecular catalysts are a robust way to increase the complexity of the metabolism enclosed by the protocell.

The protocell hypothesis conjectures the existence of a vesicle containing catalytic and replicating sequences as the primordial cellular organization during the early stages of the evolution of life. Mathematical models of protocells traditionally consider RNA sequences being encapsulated and having both an informational and a catalytic role in the same molecule. Because of this dual function, the protocell sequences are evolutionarily constrained. Mathematical models have been extensively used to study the evolutionary dynamics of protocells with a focus on the processes, like mutation or stochastic sequence assortment upon division, that affect the protocell information capacity in terms of the coexistence of different sequence types. Here we introduce a simple model of metabolic networks whose output determines the survival of the protocell with the aim of studying the effect of modifying the kinetic and architectural properties of the network on sequence coexistence. We find that stochastic assortment and mutation limit the architectures able to be encapsulated by the protocell with a given fraction of the population harbouring all possible sequence types.

Chemical roots of biological evolution: the origins of life as a process of development of autonomous functional systems

In recent years, an extension of the Darwinian framework is being considered for the study of prebiotic chemical evolution, shifting the attention from homogeneous populations of naked molecular species to populations of heterogeneous, compartmentalized and functionally integrated assemblies of molecules. Several implications of this shift of perspective are analysed in this critical review, both in terms of the individual units, which require an adequate characterization as self-maintaining systems with an internal organization, and also in relation to their collective and long-term evolutionary dynamics, based on competition, collaboration and selection processes among those complex individuals. On these lines, a concrete proposal for the set of molecular control mechanisms that must be coupled to bring about autonomous functional systems, at the interface between chemistry and biology, is provided.

Agent-based models for the emergence and evolution of grammar

Human languages are extraordinarily complex adaptive systems. They feature intricate hierarchical sound structures, are able to express elaborate meanings and use sophisticated syntactic and semantic structures to relate sound to meaning. What are the cognitive mechanisms that speakers and listeners need to create and sustain such a remarkable system? What is the collective evolutionary dynamics that allows a language to self-organize, become more complex and adapt to changing challenges in expressive power? This paper focuses on grammar. It presents a basic cycle observed in the historical language record, whereby meanings move from lexical to syntactic and then to a morphological mode of expression before returning to a lexical mode, and discusses how we can discover and validate mechanisms that can cause these shifts using agent-based models.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

The major synthetic evolutionary transitions

Evolution is marked by well-defined events involving profound innovations that are known as 'major evolutionary transitions'. They involve the integration of autonomous elements into a new, higher-level organization whereby the former isolated units interact in novel ways, losing their original autonomy. All major transitions, which include the origin of life, cells, multicellular systems, societies or language (among other examples), took place millions of years ago. Are these transitions unique, rare events? Have they instead universal traits that make them almost inevitable when the right pieces are in place? Are there general laws of evolutionary innovation? In order to approach this problem under a novel perspective, we argue that a parallel class of evolutionary transitions can be explored involving the use of artificial evolutionary experiments where alternative paths to innovation can be explored. These 'synthetic' transitions include, for example, the artificial evolution of multicellular systems or the emergence of language in evolved communicating robots. These alternative scenarios could help us to understand the underlying laws that predate the rise of major innovations and the possibility for general laws of evolved complexity. Several key examples and theoretical approaches are summarized and future challenges are outlined.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

Giant liposome formation toward the synthesis of well-defined artificial cells

This review focuses on microfluidic technologies for giant liposome formations which emulate environments of biological cells.

Synthetic transitions: towards a new synthesis

The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of non-genetic information, language or even consciousness. Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. In both cases, transitions can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of major transitions, both natural and artificial. Here, we review some advances made at the crossroads between statistical physics, artificial life, synthetic biology and evolutionary robotics.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

A Synthetic Multicellular Memory Device

Changing environments pose a challenge to living organisms. Cells need to gather and process incoming information, adapting to changes in predictable ways. This requires in particular the presence of memory, which allows different internal states to be stored. Biological memory can be stored by switches that retain information on past and present events. Synthetic biologists have implemented a number of memory devices for biological applications, mostly in single cells. It has been shown that the use of multicellular consortia provides interesting advantages to implement biological circuits. Here we show how to build a synthetic biological memory switch using an eukaryotic consortium. We engineered yeast cells that can communicate and retain memory of changes in the extracellular environment. These cells were able to produce and secrete a pheromone and sense a different pheromone following NOT logic. When the two strains were cocultured, they behaved as a double-negative-feedback motif with memory. In addition, we showed that memory can be effectively changed by the use of external inputs. Further optimization of these modules and addition of other cells could lead to new multicellular circuits that exhibit memory over a broad range of biological inputs.

Negative scaling relationship between molecular diversity and resource abundances

Cell reproduction involves replication of diverse molecule species, in contrast to a simple replication system with fewer components. To address this question of diversity, we study theoretically a cell system with catalytic reaction dynamics that grows by uptake of environmental resources. It is shown that limited resources lead to increased diversity of components within the system, and the number of coexisting species increases with a negative power of the resource uptake. The relationship is explained from the optimum growth speed of the cell, determined by a tradeoff between the utility of diverse resources and the concentration onto fewer components to increase the reaction rate.

The Search for Candidate Relevant Subsets of Variables in Complex Systems

We describe a method to identify relevant subsets of variables, useful to understand the organization of a dynamical system. The variables belonging to a relevant subset should have a strong integration with the other variables of the same relevant subset, and a much weaker interaction with the other system variables. On this basis, extending previous work on neural networks, an information-theoretic measure, the dynamical cluster index, is introduced in order to identify good candidate relevant subsets. The method does not require any previous knowledge of the relationships among the system variables, but relies on observations of their values over time. We show its usefulness in several application domains, including: (i) random Boolean networks, where the whole network is made of different subnetworks with different topological relationships (independent or interacting subnetworks); (ii) leader-follower dynamics, subject to noise and fluctuations; (iii) catalytic reaction networks in a flow reactor; (iv) the MAPK signaling pathway in eukaryotes. The validity of the method has been tested in cases where the data are generated by a known dynamical model and the dynamical cluster index is applied in order to uncover significant aspects of its organization; however, it is important that it can also be applied to time series coming from field data without any reference to a model. Given that it is based on relative frequencies of sets of values, the method could be applied also to cases where the data are not ordered in time. Several indications to improve the scope and effectiveness of the dynamical cluster index to analyze the organization of complex systems are finally given.

Employing TDMA Protocol in Neural Nanonetworks in Case of Neuron Specific Faults

Many neurodegenerative diseases arise from the malfunctioning neurons in the pathway where the signal is carried. In this paper, we propose neuron specific TDMA/multiplexing and demultiplexing mechanisms to convey the spikes of a receptor neuron over a neighboring path in case of an irreversible path fault existing in its original path. The multiplexing mechanism depends on neural delay box (NDB) which is composed of a relay unit and a buffering unit. The relay unit can be realized as a nanoelectronic device. The buffering unit can be implemented either via neural delay lines as employed in optical switching systems or via nanoelectronic delay lines, i.e., delay flip flops. Demultiplexing is realized by a demultiplexer unit according to the time slot assignment information. Besides, we propose the use of neural interfaces in the NDBs and the demultiplexer unit for detecting and stimulating the generation of spikes. The objective of the proposed mechanisms is to substitute a malfunctioning path, increase the number of spikes delivered and correctly deliver the spikes to the intended part of the somatosensory cortex. The results demonstrate that significant performance improvement on the successively delivered number of spikes is achievable when delay lines are employed as neural buffers in NDBs.

Can a minimal replicating construct be identified as the embodiment of cancer?

Genomic instability is a hallmark of cancer. Cancer cells that exhibit abnormal chromosomes are characteristic of most advanced tumours, despite the potential threat represented by accumulated genetic damage. Carcinogenesis involves a loss of key components of the genetic and signalling molecular networks; hence some authors have suggested that this is part of a trend of cancer cells to behave as simple, minimal replicators. In this study, we explore this conjecture and suggest that, in the case of cancer, genomic instability has an upper limit that is associated with a minimal cancer cell network. Such a network would include (for a given microenvironment) the basic molecular components that allow cells to replicate and respond to selective pressures. However, it would also exhibit internal fragilities that could be exploited by appropriate therapies targeting the DNA repair machinery. The implications of this hypothesis are discussed.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Toward Spatially Regulated Division of Protocells: Insights into the E. coli Min System from in Vitro Studies

For reconstruction of controlled cell division in a minimal cell model, or protocell, a positioning mechanism that spatially regulates division is indispensable. In Escherichia coli, the Min proteins oscillate from pole to pole to determine the division site by inhibition of the primary divisome protein FtsZ anywhere but in the cell middle. Remarkably, when reconstituted under defined conditions in vitro, the Min proteins self-organize into spatiotemporal patterns in the presence of a lipid membrane and ATP. We review recent progress made in studying the Min system in vitro, particularly focusing on the effects of various physicochemical parameters and boundary conditions on pattern formation. Furthermore, we discuss implications and challenges for utilizing the Min system for division site placement in protocells.

Growth and division in a dynamic protocell model

In this paper a new model of growing and dividing protocells is described, whose main features are (i) a lipid container that grows according to the composition of the molecular milieu (ii) a set of “genetic memory molecules” (GMMs) that undergo catalytic reactions in the internal aqueous phase and (iii) a set of stochastic kinetic equations for the GMMs. The mass exchange between the external environment and the internal phase is described by simulating a semipermeable membrane and a flow driven by the differences in chemical potentials, thereby avoiding to resort to sometimes misleading simplifications, e.g., that of a flow reactor. Under simple assumptions, it is shown that synchronization takes place between the rate of replication of the GMMs and that of the container, provided that the set of reactions hosts a so-called RAF (Reflexive Autocatalytic, Food-generated) set whose influence on synchronization is hereafter discussed. It is also shown that a slight modification of the basic model that takes into account a rate-limiting term, makes possible the growth of novelties, allowing in such a way suitable evolution: so the model represents an effective basis for understanding the main abstract properties of populations of protocells.

Modelling lipid competition dynamics in heterogeneous protocell populations

Recent experimental work in the field of synthetic protocell biology has shown that prebiotic vesicles are able to 'steal' lipids from each other. This phenomenon is driven purely by asymmetries in the physical state or composition of the vesicle membranes, and, when lipid resource is limited, translates directly into competition amongst the vesicles. Such a scenario is interesting from an origins of life perspective because a rudimentary form of cell-level selection emerges. To sharpen intuition about possible mechanisms underlying this behaviour, experimental work must be complemented with theoretical modelling. The aim of this paper is to provide a coarse-grain mathematical model of protocell lipid competition. Our model is capable of reproducing, often quantitatively, results from core experimental papers that reported distinct types vesicle competition. Additionally, we make some predictions untested in the lab, and develop a general numerical method for quickly solving the equilibrium point of a model vesicle population.

How to make a synthetic multicellular computer

Biological systems perform computations at multiple scales and they do so in a robust way. Engineering metaphors have often been used in order to provide a rationale for modeling cellular and molecular computing networks and as the basis for their synthetic design. However, a major constraint in this mapping between electronic and wet computational circuits is the wiring problem. Although wires are identical within electronic devices, they must be different when using synthetic biology designs. Moreover, in most cases the designed molecular systems cannot be reused for other functions. A new approximation allows us to simplify the problem by using synthetic cellular consortia where the output of the computation is distributed over multiple engineered cells. By evolving circuits in silico, we can obtain the minimal sets of Boolean units required to solve the given problem at the lowest cost using cellular consortia. Our analysis reveals that the basic set of logic units is typically non-standard. Among the most common units, the so called inverted IMPLIES (N-Implies) appears to be one of the most important elements along with the NOT and AND functions. Although NOR and NAND gates are widely used in electronics, evolved circuits based on combinations of these gates are rare, thus suggesting that the strategy of combining the same basic logic gates might be inappropriate in order to easily implement synthetic computational constructs. The implications for future synthetic designs, the general view of synthetic biology as a standard engineering domain, as well as potencial drawbacks are outlined.

An automated training paradigm reveals long-term memory in planarians and its persistence through head regeneration

Planarian flatworms are a popular system for research into the molecular mechanisms that enable these complex organisms to regenerate their entire body, including the brain. Classical data suggest that they may also be capable of long-term memory. Thus, the planarian system may offer the unique opportunity to study brain regeneration and memory in the same animal. To establish a system for the investigation of the dynamics of memory in a regenerating brain, we developed a computerized training and testing paradigm that avoided the many issues that confounded previous, manual attempts to train planarians. We then used this new system to train flatworms in an environmental familiarization protocol. We show that worms exhibit environmental familiarization, and that this memory persists for at least 14 days - long enough for the brain to regenerate. We further show that trained, decapitated planarians exhibit evidence of memory retrieval in a savings paradigm after regenerating a new head. Our work establishes a foundation for objective, high-throughput assays in this molecularly tractable model system that will shed light on the fundamental interface between body patterning and stored memories. We propose planarians as key emerging model species for mechanistic investigations of the encoding of specific memories in biological tissues. Moreover, this system is lik ely to have important implications for the biomedicine of stem-cell-derived treatments of degenerative brain disorders in human adults.

Motility at the origin of life: its characterization and a model

Due to recent advances in synthetic biology and artificial life, the origin of life is currently a hot topic of research. We review the literature and argue that the two traditionally competing replicator-first and metabolism-first approaches are merging into one integrated theory of individuation and evolution. We contribute to the maturation of this more inclusive approach by highlighting some problematic assumptions that still lead to an ximpoverished conception of the phenomenon of life. In particular, we argue that the new consensus has so far failed to consider the relevance of intermediate time scales. We propose that an adequate theory of life must account for the fact that all living beings are situated in at least four distinct time scales, which are typically associated with metabolism, motility, development, and evolution. In this view, self-movement, adaptive behavior, and morphological changes could have already been present at the origin of life. In order to illustrate this possibility, we analyze a minimal model of lifelike phenomena, namely, of precarious, individuated, dissipative structures that can be found in simple reaction-diffusion systems. Based on our analysis, we suggest that processes on intermediate time scales could have already been operative in prebiotic systems. They may have facilitated and constrained changes occurring in the faster- and slower-paced time scales of chemical self-individuation and evolution by natural selection, respectively.