1,776 research outputs found
Synthetic associative learning in engineered multicellular consortia
Associative learning is one of the key mechanisms displayed by living
organisms in order to adapt to their changing environments. It was early
recognized to be a general trait of complex multicellular organisms but also
found in "simpler" ones. It has also been explored within synthetic biology
using molecular circuits that are directly inspired in neural network models of
conditioning. These designs involve complex wiring diagrams to be implemented
within one single cell and the presence of diverse molecular wires become a
challenge that might be very difficult to overcome. Here we present three
alternative circuit designs based on two-cell microbial consortia able to
properly display associative learning responses to two classes of stimuli and
displaying long and short-term memory (i. e. the association can be lost with
time). These designs might be a helpful approach for engineering the human gut
microbiome or even synthetic organoids, defining a new class of decision-making
biological circuits capable of memory and adaptation to changing conditions.
The potential implications and extensions are outlined.Comment: 5 figure
Information Processing and Distributed Computation in Plant Organs
The molecular networks plant cells evolved to tune their development in response to the environment are becoming increasingly well understood. Much less is known about how these programs function in the multicellular context of organs and the impact this spatial embedding has on emergent decision-making. Here I address these questions and investigate whether the computational control principles identified in engineered information processing systems also apply to plant development. Examples of distributed computing underlying plant development are presented and support the presence of shared mechanisms of information processing across these domains. The coinvestigation of computation across plant biology and computer science can provide novel insight into the principles of plant development and suggest novel algorithms for use in distributed computing
From Microbial Communities to Distributed Computing Systems
A distributed biological system can be defined as a system whose components are
located in different subpopulations, which communicate and coordinate their actions
through interpopulation messages and interactions. We see that distributed systems
are pervasive in nature, performing computation across all scales, from microbial
communities to a flock of birds. We often observe that information processing within
communities exhibits a complexity far greater than any single organism. Synthetic
biology is an area of research which aims to design and build synthetic biological
machines from biological parts to perform a defined function, in a manner similar
to the engineering disciplines. However, the field has reached a bottleneck in the
complexity of the genetic networks that we can implement using monocultures, facing
constraints from metabolic burden and genetic interference. This makes building
distributed biological systems an attractive prospect for synthetic biology that would
alleviate these constraints and allow us to expand the applications of our systems
into areas including complex biosensing and diagnostic tools, bioprocess control and
the monitoring of industrial processes. In this review we will discuss the fundamental
limitations we face when engineering functionality with a monoculture, and the key
areas where distributed systems can provide an advantage. We cite evidence from
natural systems that support arguments in favor of distributed systems to overcome
the limitations of monocultures. Following this we conduct a comprehensive overview
of the synthetic communities that have been built to date, and the components that
have been used. The potential computational capabilities of communities are discussed,
along with some of the applications that these will be useful for. We discuss some of
the challenges with building co-cultures, including the problem of competitive exclusion
and maintenance of desired community composition. Finally, we assess computational
frameworks currently available to aide in the design of microbial communities and identify
areas where we lack the necessary tool
Pathways to cellular supremacy in biocomputing
Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found.A.G.-M. was supported by the SynBio3D project of the UK Engineering and Physical Sciences Research Council (EP/R019002/1) and the European CSA on biological standardization BIOROBOOST (EU grant number 820699). T.E.G. was supported by a Royal Society University Research Fellowship (grant UF160357) and BrisSynBio, a BBSRC/ EPSRC Synthetic Biology Research Centre (grant BB/L01386X/1). P.Z. was supported by the EPSRC Portabolomics project (grant EP/N031962/1). P.C. was supported by SynBioChem, a BBSRC/EPSRC Centre for Synthetic Biology of Fine and Specialty Chemicals (grant BB/M017702/1) and the ShikiFactory100 project of the European Union’s Horizon 2020 research and innovation programme under grant agreement 814408
Multicellular Computing Using Conjugation for Wiring
Recent efforts in synthetic biology have focussed on the implementation of logical functions within living cells. One aim is
to facilitate both internal ‘‘re-programming’’ and external control of cells, with potential applications in a wide range of
domains. However, fundamental limitations on the degree to which single cells may be re-engineered have led to a growth
of interest in multicellular systems, in which a ‘‘computation’’ is distributed over a number of different cell types, in a
manner analogous to modern computer networks. Within this model, individual cell type perform specific sub-tasks, the
results of which are then communicated to other cell types for further processing. The manner in which outputs are
communicated is therefore of great significance to the overall success of such a scheme. Previous experiments in distributed
cellular computation have used global communication schemes, such as quorum sensing (QS), to implement the ‘‘wiring’’
between cell types. While useful, this method lacks specificity, and limits the amount of information that may be transferred
at any one time. We propose an alternative scheme, based on specific cell-cell conjugation. This mechanism allows for the
direct transfer of genetic information between bacteria, via circular DNA strands known as plasmids. We design a multicellular
population that is able to compute, in a distributed fashion, a Boolean XOR function. Through this, we describe a
general scheme for distributed logic that works by mixing different strains in a single population; this constitutes an
important advantage of our novel approach. Importantly, the amount of genetic information exchanged through
conjugation is significantly higher than the amount possible through QS-based communication. We provide full
computational modelling and simulation results, using deterministic, stochastic and spatially-explicit methods. These
simulations explore the behaviour of one possible conjugation-wired cellular computing system under different conditions,
and provide baseline information for future laboratory implementations
Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks
Background
The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry.
Methodology/Principal Findings
To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks.
Conclusions/Significance
Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.National Institutes of Health (U.S.) (Grant # 7R01GM74712-5)United States. Defense Advanced Research Projects Agency (contract HR0011-10-C-0168)National Science Foundation (U.S.) (NSF CAREER award 0968682)BBN Technologie
In silico transitions to multicellularity
The emergence of multicellularity and developmental programs are among the
major problems of evolutionary biology. Traditionally, research in this area
has been based on the combination of data analysis and experimental work on one
hand and theoretical approximations on the other. A third possibility is
provided by computer simulation models, which allow to both simulate reality
and explore alternative possibilities. These in silico models offer a powerful
window to the possible and the actual by means of modeling how virtual cells
and groups of cells can evolve complex interactions beyond a set of isolated
entities. Here we present several examples of such models, each one
illustrating the potential for artificial modeling of the transition to
multicellularity.Comment: 21 pages, 10 figures. Book chapter of Evolutionary transitions to
multicellular life (Springer
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