Toward Engineering Biosystems With Emergent Collective Functions

Synthetic microbial ecology: engineering habitats for modular consortia

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

Biodiversity as a firewall to engineered microbiomes for restoration and conservation

The possibility of abrupt transitions threatens to poise ecosystems into irreversibly degraded states. Synthetic biology has recently been proposed to prevent them from crossing tipping points. However, there is little understanding of the impact of such intervention on the resident communities. Can such modification have 'unintended consequences', such as loss of species? Here, we address this problem by using a mathematical model that allows us to simulate this intervention scenario explicitly. We show how the indirect effect of damping the decay of shared resources results in biodiversity increase, and last but not least, the successful incorporation of the synthetic within the ecological network and very small-positive changes in the population size of the resident community. Furthermore, extensions and implications for future restoration and terraformation strategies are discussed.

ABCD of IA: A multi-scale agent-based model of T cell activation in inflammatory arthritis

Biomaterial-based agents have been demonstrated to regulate the function of immune cells in models of autoimmunity. However, the complexity of the kinetics of immune cell activation can present a challenge in optimizing the dose and frequency of administration. Here, we report a model of autoreactive T cell activation, which are key drivers in autoimmune inflammatory joint disease. The model is termed a multi-scale Agent-Based, Cell-Driven model of Inflammatory Arthritis (ABCD of IA). Using kinetic rate equations and statistical theory, ABCD of IA simulated the activation and presentation of autoantigens by dendritic cells, interactions with cognate T cells and subsequent T cell proliferation in the lymph node and IA-affected joints. The results, validated with in vivo data from the T cell driven SKG mouse model, showed that T cell proliferation strongly correlated with the T cell receptor (TCR) affinity distribution (TCR-ad), with a clear transition state from homeostasis to an inflammatory state. T cell proliferation was strongly dependent on the amount of antigen in antigenic stimulus event (ASE) at low concentrations. On the other hand, inflammation driven by Th17-inducing cytokine mediated T cell phenotype commitment was influenced by the initial level of Th17-inducing cytokines independent of the amount of arthritogenic antigen. The introduction of inhibitory artificial antigen presenting cells (iaAPCs), which locally suppress T cell activation, reduced T cell proliferation in a dose-dependent manner. The findings in this work set up a framework based on theory and modeling to simulate personalized therapeutic strategies in IA.

Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0

Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.

Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.

Generative models of morphogenesis in developmental biology

Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.

Computational capabilities of a multicellular reservoir computing system

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions-computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.

Advanced medical micro-robotics for early diagnosis and therapeutic interventions

Recent technological advances in micro-robotics have demonstrated their immense potential for biomedical applications. Emerging micro-robots have versatile sensing systems, flexible locomotion and dexterous manipulation capabilities that can significantly contribute to the healthcare system. Despite the appreciated and tangible benefits of medical micro-robotics, many challenges still remain. Here, we review the major challenges, current trends and significant achievements for developing versatile and intelligent micro-robotics with a focus on applications in early diagnosis and therapeutic interventions. We also consider some recent emerging micro-robotic technologies that employ synthetic biology to support a new generation of living micro-robots. We expect to inspire future development of micro-robots toward clinical translation by identifying the roadblocks that need to be overcome.

Seven challenges in the multiscale modeling of multicellular tissues

The growth and dynamics of multicellular tissues involve tightly regulated and coordinated morphogenetic cell behaviors, such as shape changes, movement, and division, which are governed by subcellular machinery and involve coupling through short- and long-range signals. A key challenge in the fields of developmental biology, tissue engineering and regenerative medicine is to understand how relationships between scales produce emergent tissue-scale behaviors. Recent advances in molecular biology, live-imaging and ex vivo techniques have revolutionized our ability to study these processes experimentally. To fully leverage these techniques and obtain a more comprehensive understanding of the causal relationships underlying tissue dynamics, computational modeling approaches are increasingly spanning multiple spatial and temporal scales, and are coupling cell shape, growth, mechanics, and signaling. Yet such models remain challenging: modeling at each scale requires different areas of technical skills, while integration across scales necessitates the solution to novel mathematical and computational problems. This review aims to summarize recent progress in multiscale modeling of multicellular tissues and to highlight ongoing challenges associated with the construction, implementation, interrogation, and validation of such models. This article is categorized under: Reproductive System Diseases > Computational Models Metabolic Diseases > Computational Models Cancer > Computational Models.

paraSBOLv: a foundation for standard-compliant genetic design visualization tools

Diagrams constructed from standardized glyphs are central to communicating complex design information in many engineering fields. For example, circuit diagrams are commonplace in electronics and allow for a suitable abstraction of the physical system that helps support the design process. With the development of the Synthetic Biology Open Language Visual (SBOLv), bioengineers are now positioned to better describe and share their biological designs visually. However, the development of computational tools to support the creation of these diagrams is currently hampered by an excessive burden in maintenance due to the large and expanding number of glyphs present in the standard. Here, we present a Python package called paraSBOLv that enables access to the full suite of SBOLv glyphs through the use of machine-readable parametric glyph definitions. These greatly simplify the rendering process while allowing extensive customization of the resulting diagrams. We demonstrate how the adoption of paraSBOLv can accelerate the development of highly specialized biodesign visualization tools or even form the basis for more complex software by removing the burden of maintaining glyph-specific rendering code. Looking forward, we suggest that incorporation of machine-readable parametric glyph definitions into the SBOLv standard could further simplify the development of tools to produce standard-compliant diagrams and the integration of visual standards across fields.

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation

We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

Bottom-Up Synthesis of Artificial Cells: Recent Highlights and Future Challenges

The bottom-up approach in synthetic biology aims to create molecular ensembles that reproduce the organization and functions of living organisms and strives to integrate them in a modular and hierarchical fashion toward the basic unit of life-the cell-and beyond. This young field stands on the shoulders of fundamental research in molecular biology and biochemistry, next to synthetic chemistry, and, augmented by an engineering framework, has seen tremendous progress in recent years thanks to multiple technological and scientific advancements. In this timely review of the research over the past decade, we focus on three essential features of living cells: the ability to self-reproduce via recursive cycles of growth and division, the harnessing of energy to drive cellular processes, and the assembly of metabolic pathways. In addition, we cover the increasing efforts to establish multicellular systems via different communication strategies and critically evaluate the potential applications.

Cheetah: A Computational Toolkit for Cybergenetic Control

Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.

Contextual dependencies expand the re-usability of genetic inverters

The implementation of Boolean logic circuits in cells have become a very active field within synthetic biology. Although these are mostly focussed on the genetic components alone, the context in which the circuit performs is crucial for its outcome. We characterise 20 genetic NOT logic gates in up to 7 bacterial-based contexts each, to generate 135 different functions. The contexts we focus on are combinations of four plasmid backbones and three hosts, two Escherichia coli and one Pseudomonas putida strains. Each gate shows seven different dynamic behaviours, depending on the context. That is, gates can be fine-tuned by changing only contextual parameters, thus improving the compatibility between gates. Finally, we analyse portability by measuring, scoring, and comparing gate performance across contexts. Rather than being a limitation, we argue that the effect of the genetic background on synthetic constructs expands functionality, and advocate for considering context as a fundamental design parameter.

Biofilm and swarming emergent behaviours controlled through the aid of biophysical understanding and tools

Bacteria can organise themselves into communities in the forms of biofilms and swarms. Through chemical and physical interactions between cells, these communities exhibit emergent properties that individual cells alone do not have. While bacterial communities have been mainly studied in the context of biochemistry and molecular biology, recent years have seen rapid advancements in the biophysical understanding of emergent phenomena through physical interactions in biofilms and swarms. Moreover, new technologies to control bacterial emergent behaviours by physical means are emerging in synthetic biology. Such technologies are particularly promising for developing engineered living materials (ELM) and devices and controlling contamination and biofouling. In this minireview, we overview recent studies unveiling physical and mechanical cues that trigger and affect swarming and biofilm development. In particular, we focus on cell shape, motion and density as the key parameters for mechanical cell-cell interactions within a community. We then showcase recent studies that use physical stimuli for patterning bacterial communities, altering collective behaviours and preventing biofilm formation. Finally, we discuss the future potential extension of biophysical and bioengineering research on microbial communities through computational modelling and deeper investigation of mechano-electrophysiological coupling.