Synthetic Biology: A Bridge between Artificial and Natural Cells.

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications

The Algorithmic Origins of Life

Although it has been notoriously difficult to pin down precisely what it is that makes life so distinctive and remarkable, there is general agreement that its informational aspect is one key property, perhaps the key property. The unique informational narrative of living systems suggests that life may be characterized by context-dependent causal influences, and in particular, that top-down (or downward) causation -- where higher-levels influence and constrain the dynamics of lower-levels in organizational hierarchies -- may be a major contributor to the hierarchal structure of living systems. Here we propose that the origin of life may correspond to a physical transition associated with a shift in causal structure, where information gains direct, and context-dependent causal efficacy over the matter it is instantiated in. Such a transition may be akin to more traditional physical transitions (e.g. thermodynamic phase transitions), with the crucial distinction that determining which phase (non-life or life) a given system is in requires dynamical information and therefore can only be inferred by identifying causal architecture. We discuss some potential novel research directions based on this hypothesis, including potential measures of such a transition that may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.Comment: 13 pages, 1 tabl

Cellular imitations

Synthetic biologists typically construct new pathways within existing cells. While useful, this approach in many ways ignores the undefined but necessary components of life. A growing number of laboratories have begun to try to remove some of the mysteries of cellular life by building life-like systems from non-living component parts. Some of these attempts rely on purely chemical and physical forces alone without the aid of biological molecules, while others try to build artificial cells from the parts of life, such as nucleic acids, proteins, and lipids. Both bottom-up strategies suffer from the complication of trying to build something that remains undefined. The result has been the development of research programs that try to build systems that mimic in some way recognized living systems. Since it is difficult to quantify the mimicry of life, success often times is evaluated with a degree of subjectivity. Herein we highlight recent advances in mimicking the organization and behavior of cellular life from the bottom-up

Towards Synthetic Life: Establishing a Minimal Segrosome for the Rational Design of Biomimetic Systems

DNA segregation is a fundamental life process, crucial for renewal, reproduction and propagation of all forms of life. Hence, a dedicated segregation machinery, a segrosome, must function reliably also in the context of a minimal cell. Conceptionally, the development of such a minimal cell follows a minimalistic approach, aiming at engineering a synthetic entity only consisting of the essential key elements necessary for a cell to survive. In this thesis, various prokaryotic segregation systems were explored as possible candidates for a minimal segrosome. Such a minimal segrosome could be applied for the rational design of biomimetic systems including, but not limited to, a minimal cell. DNA segregation systems of type I (ParABS) and type II (ParMRC) were compared for ensuring genetic stabilities in vivo using vectors derived from the natural secondary chromosome of Vibrio cholerae. The type II segregation system R1-ParMRC was chosen as the most promising candidate for a minimal segrosome, and it was characterized and reconstituted in vitro. This segregation system was encapsulated into biomimetic micro-compartments and its lifetime prolonged by coupling to ATP-regenerating as well as oxygen-scavenging systems. The segregation process was coupled to in vitro DNA replication using DNA nanoparticles as a mimic of the condensed state of chromosomes. Furthermore, another type II segregation system originating from the pLS20 plasmid from Bacillus subtilis (Alp7ARC) was reconstituted in vitro as a secondary orthogonal segrosome. Finally, a chimeric RNA segregation system was engineered that could be applied for an RNA-based protocell. Overall, this work demonstrates successful bottom-up assemblies of functional molecular machines that could find applications in biomimetic systems and lead to a deeper understanding of living systems

Nanoporous silica-based protocells at multiple scales for designs of life and nanomedicine.

Various protocell models have been constructed de novo with the bottom-up approach. Here we describe a silica-based protocell composed of a nanoporous amorphous silica core encapsulated within a lipid bilayer built by self-assembly that provides for independent definition of cell interior and the surface membrane. In this review, we will first describe the essential features of this architecture and then summarize the current development of silica-based protocells at both micro- and nanoscale with diverse functionalities. As the structure of the silica is relatively static, silica-core protocells do not have the ability to change shape, but their interior structure provides a highly crowded and, in some cases, authentic scaffold upon which biomolecular components and systems could be reconstituted. In basic research, the larger protocells based on precise silica replicas of cells could be developed into geometrically realistic bioreactor platforms to enable cellular functions like coupled biochemical reactions, while in translational research smaller protocells based on mesoporous silica nanoparticles are being developed for targeted nanomedicine. Ultimately we see two different motivations for protocell research and development: (1) to emulate life in order to understand it; and (2) to use biomimicry to engineer desired cellular interactions

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

Developments in the tools and methodologies of synthetic biology.

Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a body of knowledge from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community

Mechanistic Information and Causal Continuity

Some biological processes (our examples are DNA expression and a reflex response in the leech) move from step to step in a way that cannot be completely understood solely in terms of causes and correlations. This paper develops a notion of mechanistic information that can be used to explain the continuities of such processes. We compare them to processes (including the Krebs cycle) that do not involve information. We compare our conception of mechanistic information to some familiar notions including Crick’s idea of genetic information, Shannon-Weaver information, and Millikan’s biosemantic information

Self-Replicating Machines in Continuous Space with Virtual Physics

JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their external relationships are governed by a simulated physics that includes Brownian motion, viscosity, and spring-like attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary "seed" pattern is put in a "soup" of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life