Population-based microbial computing: a third wave of synthetic biology?

Synthetic biology is an emerging research field, in which engineering principles are applied to natural, living systems. A major goal of synthetic biology is to harness the inherent “biological nanotechnology” of living cells for the purposes of computation, production or diagnosis. As the field evolves, it is gradually developing from a single-cell approach (akin to using standalone computers) to a distributed, population-based approach (akin to using networks of connected machines). We anticipate this eventually representing the “third wave” of synthetic biology (the first two waves being the emergence of modules and systems, respectively, with the second wave still yet to peak). In this paper, we review the developments that are leading to this third wave, and describe some of the existing scientific and technological challenges

Reprogrammable In Vivo Architecture

The biological cell is the intricate, yet ubiquitous component of life, able to grow, adapt and reproduce. The genetic material contained within a cell encodes information which directs its development and behaviour, and this information is passed down from one generation of cell to the next. One emerging interest, resulting from collaborations between the disciplines of Molecular Biology and Computer Science, is to encode computational programs, sets of engineered, information processing instructions, in genetic material, to be executed by living cells.So far, the large majority of in vivo computation research has been based on the detection and conditional manipulation of protein concentrations inside cells, which is the biological method of gene expression. In contrast, this thesis describes how a computational program, encoded in genetic material inside a bacterium, can be triggered by external stimuli to reassemble itself in a directed manner to create a newly arranged computational program.In order to investigate the potential utility of in vivo self-arranging programs, software was designed to explore a search space of candidate computational programs, encoded in genetic material, which are able to rearrange themselves; to simulate these candidates and to evaluate their behaviour against a set of criteria. Rearrangements were facilitated by biological catalysts which can selectively sever and rejoin genetic material in a cooperative manner. Their ability to perform compound operations was found to allow for a general purpose mechanismAs a proof of concept, one of the candidate computational programs, a two-colour switch which can be set irreversibly through its rearrangement, was encoded in genetic material. Measurements of in vivo expression were observed resulting from in vitro rearrangement manipulations, to illustrate its operation

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

Cellular computation and communications using engineered genetic regulatory networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 130-138).In this thesis, I present an engineering discipline for obtaining complex, predictable, and reliable cell behaviors by embedding biochemical logic circuits and programmed intercellular communications into cells. To accomplish this goal, I provide a well-characterized component library, a biocircuit design methodology, and software design tools. I have built and characterized an initial cellular gate library with biochemical gates that implement the NOT, IMPLIES, and AND logic functions in E. coli cells. The logic gates perform computation using DNA-binding proteins, small molecules that interact with these proteins, and segments of DNA that regulate the expression of the proteins. I introduce genetic process engineering, a methodology for modifying the DNA encoding of existing genetic elements to achieve the desired input/output behavior for constructing reliable circuits of significant complexity. I demonstrate the feasibility of digital computation in cells by building several operational in-vivo digital logic circuits, each composed of three gates that have been optimized by genetic process engineering.(cont.) I also demonstrate engineered intercellular communications with programmed enzymatic activity and chemical diffusions to carry messages, using DNA from the Vibrio fischeri lux operon. The programmed communications is essential for obtaining coordinated behavior from cell aggregates. In addition to the above experimental contributions, I have developed BioSPICE, a prototype software tool for biocircuit design. It supports both static and dynamic simulations and analysis of single cell environments and small cell aggregates. Finally, I present the Microbial Colony Language (MCL), a model for programming cell aggregates. The language is expressive enough for interesting applications, yet relies on simple primitives that can be mapped to the engineered biological processes described above.by Ron Weiss.Ph.D

Synthesis of Biological and Mathematical Methods for Gene Network Control

abstract: Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Computational Simulation of Gene Regulatory Networks Implementing an Extendable Synchronous Single-Input Delay Flip-Flop and State Machine

We present a detailed and extendable design of the first synchronous single-input delay flip-flop implemented as a gene regulatory network in Escherichia coli (E. coli). The device, which we call the BioD, has one data input (trans-acting RNA), one clock input (far-red light) and an output that reports the state of the device using green fluorescent protein (GFP). The proposed design builds on Gardner’s toggle switch, to provide a more sophisticated device that can be synchronized with other devices within or without the same cell, and which requires only one data input. We provide a mathematical model of the system and simulation results. The results show that the device behaves in line with desired functionality. Further, we discuss the constraints of the design, which pertain to ranges of parameter values. The BioD is extended via the addition of an update function and input and output interfaces. The result is the BioFSM, which constitutes a synchronous and modular finite state machine, which uses an update function to change its state, stored in the BioD. The BioFSM uses its input and output interfaces for inter-cellular communications. This opens the door to the design of a circular cellular automata (the BioCell), which is envisioned as a number of communicating E. coli colonies, each made of clones of one BioFSM

Understanding morphogenesis in myxobacteria from a theoretical and experimental perspective

Several species of bacteria exhibit multicellular behaviour, with individuals cells cooperatively working together within a colony. Often this has communal benefit since multiple cells acting in unison can accomplish far more than an individual cell can and the rewards can be shared by many cells. Myxobacteria are one of the most complex of the multicellular bacteria, exhibiting a number of different spatial phenotypes. Colonies engage in multiple emergent behaviours in response to starvation culminating in the formation of massive, multicellular fruiting bodies. In this thesis, experimental work and theoretical modelling are used to investigate emergent behaviour in myxobacteria. Computational models were created using FABCell, an open source software modelling tool developed as part of the research to facilitate modelling large biological systems. The research described here provides novel insights into emergent behaviour and suggests potential mechanisms for allowing myxobacterial cells to go from a vegetative state into a fruiting body. A differential equation model of the Frz signalling pathway, a key component in the regulation of cell motility, is developed. This is combined with a three-dimensional model describing the physical characteristics of cells using Monte Carlo methods, which allows thousands of cells to be simulated. The unified model explains how cells can ripple, stream, aggregate and form fruiting bodies. Importantly, the model copes with the transition between stages showing it is possible for the important myxobacteria control systems to adapt and display multiple behaviours

An integrative modelling framework for multicellular systems

Ph. D. Thesis.Multicellular systems exhibit complex population scale behaviour that emerge from the interactions between constituent cells. Integrative modelling (IM) techniques are a valuable tool for studying these systems capturing processes that occur at many temporal and spatial scales. The application of IM to multicellular systems is challenging as it is knowledge and resource intensive, additionally there do not exist effective frameworks or tools, inhibiting its wider application in Systems and Synthetic biology. This thesis presents Simbiotics, a novel IM framework for the modelling of mixed species bacterial consortia. Simbiotics is a spatially explicit multi-scale modelling platform for the design, simulation and analysis of bacterial populations. A library of modules simulating features such as cell geometries, physical force dynamics, genetic circuits, metabolic pathways, chemical diffusion and cell interactions is implemented, that the modeller may compose into their own custom models. Common modelling methods such as Boolean networks, differential equations, Gillespie models and SBML are implemented. With the platform in-silico experiments can be conducted with programmed experiment interactions, data collection and analysis. The framework is extendable and modular, allowing for the library to be updated as knowledge progresses. A novel file format for the reuse and communication of multicellular models and simulation methods is also implemented. Additionally an intuitive graphical user interface, Easybiotics, has been developed allowing for multicellular modelling with minimal programming experience. Four novel case studies are pursued with Simbiotics studying the emergent behaviours of multicellular systems. The effect of physical cell interactions are characterised in the first two studies. Investigation into how chemical signalling and intracellular dynamics influence population dynamics and patterns are studied in the final two case studies. These studies demonstrate how Simbotics can be integrated into a Systems/ Synthetic biology workflow, facilitating the studying of natural systems and as a CAD tool for developing novel synthetic systems.EPSR

Modelling tools and methodologies for rapid protocell prototyping

The field of unconventional computing considers the possibility of implementing computational devices using novel paradigms and materials to produce computers which may be more efficient, adaptable and robust than their silicon based counterparts. The integration of computation into the realms of chemistry and biology will allow the embedding of engineered logic into living systems and could produce truly ubiquitous computing devices. Recently, advances in synthetic biology have resulted in the modification of microorganism genomes to create computational behaviour in living cells, so called “cellular computing”. The cellular computing paradigm offers the possibility of intelligent bacterial agents which may respond and communicate with one another according to chemical signals received from the environment. However, the high levels of complexity when altering an organism which has been well adapted to certain environments over millions of years of evolution suggests an alternative approach in which chemical computational devices can be constructed completely from the bottom up, allowing the designer exquisite control and knowledge about the system being created. This thesis presents the development of a simulation and modelling framework to aid the study and design of bottom-up chemical computers, involving the encapsulation of computational re-actions within vesicles. The new “vesicle computing” paradigm is investigated using a sophisticated multi-scale simulation framework, developed from mesoscale, macroscale and executable biology techniques