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Results for “Synthetic Biology”

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bioRxiv2023

The Effect of Pseudoknot Base Pairing on Cotranscriptional Structural Switching of the Fluoride Riboswitch

A central question in biology is how RNA sequence changes influence dynamic conformational changes during cotranscriptional folding. Here we investigated this question through the study of transcriptional fluoride riboswitches, non-coding RNAs that sense the fluoride anion through the coordinated folding and rearrangement of a pseudoknotted aptamer domain and a downstream intrinsic terminator expression platform. Using a combination of E. coli RNA polymerase in vitro transcription and cellular gene expression assays, we characterized the function of mesophilic and thermophilic fluoride riboswitch variants. We showed that only variants containing the mesophilic pseudoknot function at 37 {degrees}C. We next systematically varied the pseudoknot sequence and found that a single wobble base pair is critical for function. Characterizing thermophilic variants at 65 {degrees}C through Thermus aquaticus RNA polymerase in vitro transcription showed the importance of this wobble pair for function even at elevated temperatures. Finally, we performed all-atom molecular dynamics simulations which supported the experimental findings, visualized the RNA structure switching process, and provided insight into the important role of magnesium ions. Together these studies provide deeper insights into the role of riboswitch sequence in influencing folding and function that will be important for understanding of RNA-based gene regulation and for synthetic biology applications.

molecular biology↗

bioRxiv2021

Genetic Optimisation of Bacteria-Induced Calcite Precipitation in Bacillus subtilis

BackgroundMicrobially induced calcite precipitation (MICP) is an ancient property of bacteria, which has recently gained considerable attention for biotechnological applications. It occurs as a by-product of bacterial metabolism and involves a combination of chemical changes in the extracellular environment, e.g. pH increase, and presence of nucleation sites on the cell surface or extracellular substances produced by the bacteria. However, the molecular mechanisms underpinning MICP and the interplay between the contributing factors remain poorly understood, thus placing barriers to the full biotechnological and synthetic biology exploitation of bacterial biomineralisation. ResultsIn this study, we adopted a bottom-up approach of systematically engineering Bacillus subtilis, which has no detectable intrinsic MICP activity, for biomineralisation. We showed that heterologous production of urease can induce MICP by local increases in extracellular pH, and this can be enhanced by co-expression of urease accessory genes for urea and nickel uptake, depending on environmental conditions. MICP can be strongly enhanced by biofilm-promoting conditions, which appeared to be mainly driven by production of exopolysaccharide, while the protein component of the biofilm matrix was dispensable. Attempts to modulate the cell surface charge of B. subtilis had surprisingly minor effects, and our results suggest this organism may intrinsically have a very negative cell surface, potentially predisposing it for MICP activity. ConclusionsOur findings give insights into the molecular mechanisms driving MICP in an application-relevant chassis organism and the genetic elements that can be used to engineer de novo or enhanced biomineralisation. This study also highlights mutual influences between the genetic drivers and the chemical composition of the surrounding environment in determining the speed, spatial distribution and resulting mineral crystals of MICP. Taken together, these data pave the way for future rational design of synthetic precipitator strains optimised for specific applications.

microbiology↗

bioRxiv2026

An Open-Source Magnetofluorescence Imaging Platform forPlate-Scale Screening of Magnetic Field Effects in LiveBacteria

Magnetic field effects (MFEs) in biological systems are typically small and experimentally challenging to measure reproducibly across large sample populations. Existing approaches to measure such effects often rely on low-throughput microscopy or custom-built magnetic stimulation systems that provide limited control over magnetic field geometry, synchronization, or experimental automation. Here, we present an open-source magnetofluorescence imaging platform designed for bacterial plate-scale screening of MFEs in live colonies. The instrument integrates a programmable three-axis vector electromagnet, synchronized fluorescence excitation and imaging, and integrated acquisition software with per-frame metadata logging on a hardware-synchronized data acquisition card. An extensive calibration procedure enables accurate generation of arbitrary magnetic field vectors, while synchronized triggering ensures deterministic alignment between field application, illumination, and image acquisition. The system images an entire 100 mm Petri dish in a single acquisition. Typical experiments monitor hundreds of bacterial colonies simultaneously over multi-hour acquisition sequences. Control software, calibration routines, mechanical design files, and acquisition workflows are provided openly to facilitate replication. Instrument performance is demonstrated through detection of magnetic field-dependent fluorescence changes in E. coli expressing the engineered magnetosensitive fluorescent protein MagLOV2. This instrument provides a flexible and scalable platform for high-throughput magnetobiology, synthetic biology, and quantum biology experiments.

bioengineering↗

bioRxiv2023

Biphasic responses in cell signalling: A unified approach

Biphasic responses are encountered at all levels in biological systems. At the cellular level, biphasic dose-responses are widely encountered in cell signalling and post-translational modification systems and represent safeguards against over-activation or overexpression of species. In this paper we provide a unified theoretical synthesis of biphasic responses in cell signalling systems, by assessing signalling systems ranging from basic biochemical building blocks to canonical network structures to well-characterized exemplars on one hand, and examining different types of doses on the other. By using analytical and computational approaches applied to a range of systems across levels (described by broadly employed models) we reveal (i) design principles enabling the presence of biphasic responses, including in almost all instances, an explicit characterization of the parameter space (ii) structural factors which preclude the possibility of biphasic responses (iii) different combinations of the presence or absence of enzyme-biphasic and substrate-biphasic responses, representing safeguards against overactivation and overexpression respectively (iv) the possibility of broadly robust biphasic responses (v) the complete alteration of signalling behaviour in a network due to biphasic interactions between species (biphasic regulation) (vi) the propensity of different co-existing biphasic responses in the Erk signalling network. These results both individually and in totality have a number of important consequences for systems and synthetic biology.

systems biology↗

bioRxiv2024

AlloPool: An Adaptive Graph Neural Network for Dynamic Allosteric Network Prediction in Protein Systems

Allosteric communication is essential to protein function, facilitating the dynamic regulation of biological responses through the propagation of structural and dynamic changes between regulatory and effector sites in response to stimuli. Traditional approaches to studying protein allostery often rely on static protein structures or abstract representations involving fully connected interaction graphs, which do not capture the temporal and state-dependent nature of these dynamic systems. Here, we introduce AlloPool, a graph neural network (GNN)-based model that iteratively prunes residue interactions to identify minimal, time-dependent interaction networks that govern long-range structural and dynamic responses to chemical or mechanical stimuli. Using temporal attention and graph aggregation, AlloPool accounts for evolving protein conformations in both molecular dynamics (MD) and steered MD (SMD) simulations to predict MD and SMD trajectories. We validate AlloPool on the Pin-1 protein and the ADGRG1 and B1AR receptors, showcasing its ability to accurately recapitulate protein motions, infer allosteric communication pathways, and identify critical allosteric sites. Additionally, AlloPool identifies force-dependent changes in GAIN domain structure and reconstructs directed information flow under mechanical load. Comparative analyses indicate that AlloPool outperforms existing models in MD and SMD trajectory reconstruction, presenting a new framework for analyzing force- and ligand-induced allosteric motions. This work advances the modeling of allosteric systems and offers broad potential for applications in drug discovery, synthetic biology, and protein engineering.

bioinformatics↗

bioRxiv2025

Cell-free systems enable mechanistic characterization of genetically encoded RNA strand exchange circuits for programmable protein expression

Nucleic acid circuits are powerful tools for programming biology, but the design principles for operating these circuits in complex cellular environments remain poorly understood compared to simple in vitro settings. Cell-free expression systems (CFES) are uniquely suited to address this challenge, as they can integrate measurements ideal for either in vitro or in cyto settings. Further, because CFES are open systems, they enable a level of control over component concentration unattainable in cells. Here, we use cell-free systems to characterize genetically encoded RNA circuits that operate via toehold-mediated strand exchange (TMSE). These circuits have been extensively characterized in vitro and were recently deployed as translational riboregulators in E. coli, revealing key differences between the two environments. By systematically modulating the design parameters of these RNA circuits in both purified protein and lysate-based CFES, we elucidate the mechanisms linking TMSE to protein expression. Further, we combine measurements and alterations in CFES composition that are infeasible in cells to investigate interactions between cellular components and RNA circuit components, identifying a potential interaction with ribosomes that informs circuit design. Our results establish a unified set of principles for designing and operating genetically encoded TMSE circuits across in vitro, CFE, and bacterial environments, which should catalyze the widespread adoption of this platform for new applications in molecular programming, synthetic biology, and biotechnology.

bioengineering↗

bioRxiv2026

Autonomous Homeostatic Synthetic Cells via Self-Gating DNA Nanopores

Homeostasis is a fundamental hallmark of living organisms, arising from the complex interplay between biochemical reactions and regulatory feedback systems. Reconstituting such self-regulating behaviour in minimal synthetic cells enables continuous, persistent operation of biochemical reactions for extended amount of time. In this work, we demonstrate a minimal homeostatic synthetic cell capable of autonomous flux regulation using DNA nanotechnology and bottom-up synthetic biology. Our homeostatic architecture consists of Giant Unilamellar Vesicles (GUVs) equipped with gated DNA nanopores, encapsulated in vitro transcription (IVT) machinery, and an RNA degradation system. We achieve homeostasis under varying external chemical stimuli specifically varying concentrations of rNTPs by implementing a negative feedback loop between rNTP influx and RNA production. In our system, DNA nanopores facilitate the influx of rNTPs from the external environment, driving internal transcription. Crucially, the transcription process generates RNA "blockers" designed to bind and gate the DNA nanopores, thereby attenuating further rNTP influx. Our system is dynamic as encapsulated RNases slowly degrade the RNA blockers, allowing the pores to reopen as blocker concentration goes down. We first characterise the functionality and gating efficiency of the DNA nanopores using both pre-synthesised and in situ produced DNA and RNA blockers. We then demonstrate that rNTP flux through these pores is sufficient to drive IVT within the GUVs. Finally, by integrating these modules, we demonstrate robust homeostasis: the system maintains a steady-state level of RNA production for up to 16 hours. By harnessing the controllability of negative feedback loop, we demonstrate thresholding of the homeostasis level using single-stranded regulator DNA. This work establishes a versatile framework for engineering adaptive and self-sustaining responsive nanomaterials and synthetic cell chassis.

biophysics↗

bioRxiv2017

Probabilistic Recovery Of Cryptic Haplotypes From Metagenomic Data

The cryptic diversity of microbial communities represent an untapped biotechnological resource for biomining, biorefining and synthetic biology. Revealing this information requires the recovery of the exact sequence of DNA bases (or \"haplotype\") that constitutes the genes and genomes of every individual present. This is a computationally difficult problem complicated by the requirement for environmental sequencing approaches (metagenomics) due to the resistance of the constituent organisms to culturing in vitro.\n\nHaplotypes are identified by their unique combination of DNA variants. However, standard approaches for working with metagenomic data require simplifications that violate assumptions in the process of identifying such variation. Furthermore, current haplotyping methods lack objective mechanisms for choosing between alternative haplotype reconstructions from microbial communities.\n\nTo address this, we have developed a novel probabilistic approach for reconstructing haplotypes from complex microbial communities and propose the \"metahaplome\" as a definition for the set of haplotypes for any particular genomic region of interest within a metagenomic dataset. Implemented in the twin software tools Hansel and Gretel, the algorithm performs incremental probabilistic haplotype recovery using Naive Bayes -- an efficient and effective technique.\n\nOur approach is capable of reconstructing the haplotypes with the highest likelihoods from metagenomic datasets without a priori knowledge or making assumptions of the distribution or number of variants. Additionally, the algorithm is robust to sequencing and alignment error without altering or discarding observed variation and uses all available evidence from aligned reads. We validate our approach using synthetic metahaplomes constructed from sets of real genes, and demonstrate its capability using metagenomic data from a complex HIV-1 strain mix. The results show that the likelihood framework can allow recovery from microbial communities of cryptic functional isoforms of genes with 100% accuracy.

bioinformatics↗

bioRxiv2023

An Enhancer Trap system to track developmental dynamics in Marchantia polymorpha

A combination of streamlined genetics, experimental tractability and relative morphological simplicity compared to vascular plants makes the liverwort Marchantia polymorpha an ideal model system for studying many aspects of plant biology. Here we describe a transformation vector combining a constitutive fluorescent membrane marker with a nuclear marker that is regulated by nearby enhancer elements, and use this to produce a library of enhancer trap lines for Marchantia. Screening gemmae from these lines allowed for identification and characterization of novel marker lines, including markers for rhizoids and oil cells. The library allowed the identification of a margin tissue running around the thallus edge, highlighted during thallus development. Expression of this marker is correlated with auxin levels. We generated multiple markers for the meristematic apical notch region, which have different spatial expression patterns, reappear at different times during meristem regeneration following apical notch excision and have varying responses to auxin supplementation or inhibition. This reveals that there are proximodistal substructures within the apical notch that could not be observed otherwise. We employed our markers to study Marchantia sporeling development, observing meristem emergence as defining the protonema-to-prothallus stage transition, and subsequent production of margin tissue during the prothallus stage. Exogenous auxin treatment stalls meristem emergence at the protonema stage but does not inhibit cell division, resulting in callus-like sporelings with many rhizoids, whereas pharmacologically inhibiting auxin synthesis and transport does not prevent meristem emergence. This enhancer trap system presents a useful resource for the community and will contribute to future Marchantia research. Significance StatementThe liverwort Marchantia polymorpha is an emerging model species with huge potential for synthetic biology. We generated an enhancer trap library as a resource for Marchantia research and characterized marker lines for several important cell types (rhizoids, thallus margin, apical notch and oil cells). These marker lines can be used to track cellular dynamics in growing plants, exemplified by studying auxin responses and sporeling growth, revealing new insights into thallus development and cell fate specification.

plant biology↗

bioRxiv2025

A Pipeline for Screening Small Molecule-Enhanced Protein Stability in A Bacterial Orphan Receptor

Bacterial one-component signaling proteins integrate sensory and gene regulation functions within the same polypeptide, creating powerful natural sensors of environmental conditions which can also be adapted into powerful tools for synthetic biology and biotechnology. A key sensor motif within many of these proteins is the Per- ARNT-Sim (PAS) domain, known for its conserved fold yet highly divergent sequences, allowing for a broad range of ligands to control PAS protein function by changes in small molecule binding occupancy or configuration. This diversity introduces a challenging step - identification of ligands for "orphan" PAS proteins which show signatures of ligand binding but no copurifying high-affinity bound small molecules - into characterization and engineering of such proteins. In this study, we characterized CU228, a putative PAS-HTH transcription factor from Candidatus Solibacter usitatus, as a novel model system for searching for novel ligands by small molecule stabilization. Bioinformatics and structural analyses predicted a PAS domain with an Trp-rich internal cavity, suggesting potential small molecule interactions. Using a [~]760 compound fragment library, differential scanning fluorimetry identified three ligands (KG-96, KG- 408, and KG-484) that substantially increased CU228 thermal stability with {Delta}Tm values up to +10{degrees}C. Microfluidic modulation spectroscopy (MMS) revealed ligand-induced preservation of -helical and {beta}-sheet integrity under thermal stress. Saturation transfer difference NMR confirmed direct binding of all three ligands and enabled estimation of micromolar-range dissociation constants, consistent with expected fragment-level affinity. Our findings expand the analytical toolbox for probing protein-ligand interactions in flexible, signal-responsive systems, laying the groundwork for designing synthetic chemogenetic variants of one-component transcription factors.

biochemistry↗

bioRxiv2018

Model Classification for Uncertainty Reduction in Biochemical Kinetic Models

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.\n\nAuthor SummaryKinetic models are the most promising tool for understanding the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and thus we need large-scale models for dependable in silico analyses of metabolism. However, uncertainty in kinetic parameters impedes the development of kinetic models, and uncertainty levels increase with the model size. Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed. In this work, we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.

systems biology↗

bioRxiv2020

Assessing the Quality of Cotranscriptional Folding Simulations

Structural changes in RNAs are an important contributor to controlling gene expression not only at the post-transcriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5 region of the primary transcript regulates the downstream functional unit - usually an ORF - through premature termination of transcription. Such elements not only occur naturally but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labour-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach both because of the size of the molecules and the time scales of interest. Even at the simplified level of secondary structures further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape for each individual transcription step by a coarse-grained representation that only retains a small set of low-energy local minima and the energy barriers between them. The folding dynamics between two transcriptional elongation steps is modeled as a Markov process on this representation. Maps between pairs of consecutive coarse-grained landscapes make it possible to follow the folding process as it changes in response to transcription elongation. In its original implementation, the BarMap software provides a general framework to investigate RNA folding dynamics on temporally changing landscapes. It is, however, difficult to use in particular for specific scenarios such as cotranscriptional folding. To overcome this limitation, we developed the user-friendly BarMap-QA pipeline described in detail in this contribution. It is illustrated here by an elaborate example that emphasizes the careful monitoring of several quality measures. Using an iterative workflow, a reliable and complete kinetics simulation of a synthetic, transcription regulating riboswitch is obtained using minimal computational resources. All programs and scripts used in this contribution are free software and available for download as a source distribution for Linux(R), or as a platform-independent Docker(R) image including support for Apple macOS(R) and Microsoft Windows(R).

bioinformatics↗

bioRxiv2021

Biology and engineering of integrative and conjugative elements: Construction and analyses of hybrid ICEs reveal element functions that affect species-specific efficiencies

Integrative and conjugative elements (ICEs) are mobile genetic elements that reside in a bacterial host chromosome and are prominent drivers of bacterial evolution. They are also powerful tools for genetic analyses and engineering. Transfer of an ICE to a new host involves many steps, including excision from the chromosome, DNA processing and replication, transfer across the envelope of the donor and recipient, processing of the DNA, and eventual integration into the chromosome of the new host (now a stable transconjugant). Interactions between an ICE and its hosts throughout the life cycle likely influence the efficiencies of acquisition by new hosts. Here, we investigated how different functional modules of two ICEs, Tn916 and ICEBs1, affect the transfer efficiencies into different host bacteria. We constructed hybrid elements that utilize the high-efficiency regulatory and excision modules of ICEBs1 and the conjugation genes of Tn916. These elements produced more transconjugants than Tn916, likely due to increased excision frequencies. We also found that several Tn916 and ICEBs1 components can substitute for one other. Using B. subtilis donors and three Enterococcus species as recipients, we found that different hybrid elements were more readily acquired by some species than others, demonstrating species-specific interactions in steps of the ICE life cycle. This work demonstrates that hybrid elements utilizing the efficient regulatory functions of ICEBs1 can be built to enable efficient transfer into and engineering of a variety of other species. Author summary (non-technical)Horizontal gene transfer helps drive microbial evolution, enabling bacteria to rapidly acquire new genes and traits. Integrative and conjugative elements (ICEs) are mobile genetic elements that reside in a bacterial host chromosome and are prominent drivers of horizontal gene transfer. They are also powerful tools for genetic analyses and engineering. Some ICEs carry genes that confer obvious properties to host bacteria, including antibiotic resistances, symbiosis, and pathogenesis. When activated, an ICE-encoded machine is made that can transfer the element to other cells, where it then integrates into the chromosome of the new host. Specific ICEs transfer more effectively into some bacterial species compared to others, yet little is known about the determinants of the efficiencies and specificity of acquisition by different bacterial species. We made and utilized hybrid ICEs, composed of parts of two different elements, to investigate determinants of transfer efficiencies. Our findings demonstrate that there are species-specific interactions that help determine efficiencies of stable acquisition, and that this explains, in part, the efficiencies of different ICEs. These hybrid elements are also useful in genetic engineering and synthetic biology to move genes and pathways into different bacterial species with greater efficiencies than can be achieved with naturally occurring ICEs.

microbiology↗

bioRxiv2026

A Unified Control of Cellular Differentiation: From Temporal Multistability to Spatial Pattern Formation in Gene Regulatory Networks

How genetically identical cells spontaneously break symmetry to assume divergent fates is a fundamental problem in developmental biology. While modern genomics has mapped the vast molecular repertoire involved in gene regulation, understanding the mechanism of cell state transitions that drive differentiation remains a formidable challenge. To address this, we use a reaction-kinetic framework to analyze recurring motifs of two and three competing master regulators. While typically such circuits are studied numerically, we show that assuming symmetry in nodes and interactions provides exact analytical description of the bifurcations governing cell fate transitions. We find that the possible cell fates across all considered topologies are dictated by a single dimensionless quantity, {beta}--the ratio of protein degradation to production rates. In the binary Toggle Switch (TS), decreasing {beta} destabilizes the symmetric (stem cell) state, giving rise to two asymmetric (differentiated) fates via a supercritical pitchfork bifurcation. In the three-component Toggle Triad (TT), low values of {beta} yield three asymmetric fates through subcritical pitchfork bifurcation, creating an intermediate range of {beta} where both symmetric and asymmetric fates are simultaneously stable. For the Self-Activating Toggle Switch (SATS), we identify a new parameter for the self-activation threshold ({theta}) and show that decreasing{theta} progressively stabilizes the uncommitted state, leading to a regime of tristability. Building on these temporal bifurcations, we next address the feasibility of spatial structure formation: can these multistable fates stably coexist within a spatial domain? Through a minimal model of cell-cell communication via free diffusion, we extend these motifs into reaction-diffusion systems, which reveals a direct role of network topology on spatial organization. We prove that any heterogeneous pattern in two-node circuits is inherently transient and unstable. In contrast, the three-node repressive network supports the stable spatial coexistence of differentiated phenotypes through pure diffusion, a phenomenon we analyze by studying heteroclinic interface solutions as building blocks. By reducing complex regulatory dynamics to tractable models with physically meaningful parameters, we establish a minimal framework which relates topology to cell fate. Finally, the effects of temporal multistability on pattern formation provide an excellent studying ground for morphogenesis, synthetic biology, and the overarching problem of spatiotemporal self-organization.

systems biology↗

bioRxiv2014

A curated C. difficile strain 630 metabolic network: prediction of essential targets and inhibitors

Clostridium difficile is the leading cause of hospital-borne infections occurring when the natural intestinal flora is depleted following antibiotic treatment. We present iMLTC804cdf, an extensively curated reconstructed metabolic network for the C. difficile pathogenic strain 630. iMLTC804cdf contains 804 genes, 705 metabolites and 766 metabolic, 145 exchange and 118 transport reactions. iMLTC804cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date. We validate the model with simulated growth assays in different media and carbon sources and use it to predict essential genes. We obtain 88.8% accuracy in the prediction of gene essentiality when compared to experimental data for B. subtilis homologs. We predict the existence of 83 essential genes and 68 essential gene pairs, a number of which are unique to C. difficile and have non-existing or predicted non-essential human homologs. For 19 of these potential therapeutic targets, we find 72 inhibitors of homologous proteins that could serve as starting points in the development of new antibiotics, including approved drugs with the potential for drug repositioning.\n\nSystems & Synthetic Biology Subject Category: Genome Scale & Integrative Biology\n\nMolecular & Cell Biology Subject Category: Pharmacology & Drug Discovery

Systems Biology↗

bioRxiv2014

A simple method for constructing magnetic Escherichia coli

Magnetic force can serve as an ideal way to control the spatial behavior of microorganisms, because of its flexibility and penetrability. By incubation with the biocompatible compound, ammonium ferric citrate, as an iron source, we magnetized Escherichia coli, the most programmable chassis in synthetic biology. To enhance the magnetization efficiency, the ferritin protein, FtnA, from E. coli was cloned and overexpressed in strain BL21(DE3). The magnetization effect was observed within 30 min after harvest of bacteria, and the concentration of ammonium ferric acid used could be as low as 0.5 mM. Using different shapes of magnetic fields, different patterns could be generated easily. Our method may set up the foundation for a rational design of spatial structure of cell communities, which is important for their actual application.

Microbiology↗

bioRxiv2014

Multicellularity makes somatic differentiation evolutionarily stable

Many multicellular organisms produce two cell lineages: germ cells, whose descendants form the next generation, and somatic cells which support, protect, and disperse the germ cells. This distinction has evolved independently in dozens of multicellular taxa but is absent in unicellular species. We propose that unicellular, soma-producing populations are intrinsically susceptible to invasion by non-differentiating mutants which ultimately eradicate the differentiating lineage. We argue that multicellularity can prevent the victory of such mutants. To test this hypothesis, we engineer strains of the budding yeast Saccharomyces cerevisiae that differ only in the presence or absence of multicellularity and somatic differentiation, permitting direct comparisons between organisms with different lifestyles. We find that non-differentiating mutants overtake unicellular populations but are outcompeted by multicellular differentiating strains, suggesting that multicellularity confers evolutionary stability to somatic differentiation.\n\nOne Sentence SummaryUsing a synthetic biological approach, we show that multicellularity protects species that produce somatic cells from exploitation by common mutants.

Evolutionary Biology↗

bioRxiv2015

Design and strain selection criteria for bacterial communication networks

In this paper we discuss data and methodological challenges for building bacterial communication networks using two examples: Escherichia coli as a flagellate bacterium and of Geobacter sulfurreducens as a biofilm forming bacterium. We first highlight the link between the bacterial network communication design with respect to metabolic information processing design. The potentialities of designing routing network schemes described previously in literature and based on bacteria motility and genetic message exchanges will depend on the genes coding for the intracellular and intercellular signalling pathways. In bacteria, the \"mobilome\" is related to horizontal gene transfer. Bacteria trade off the acquisition of new genes which could improve their survival (and often their communication bandwidth), keeping their genome enough small to assure quick DNA replication and increase fast the biomass to speed up cell division. First, by using a multi-objective optimisation procedure, we search for the optimal trade off between energy production, which is a requirement for the motility, and the biomass growth, which is related to the overall survival and fitness of the bacterium. We use flux balance analysis of genome-scale biochemical network of Escherichia coli k-13 MG1655. Then, as a second case study we analyze the electric properties and biomass trade-off of the bacterium Geobacter sulfurreducens which constructs an electric biofilm where electrons move across the nanowires. Here we discuss the potentialities of optimisation methodologies to design and select bacterial strains with desiderata properties. The optimisation methodologies establish also a relation between metabolism, network communication and computing. Moreover, we point to genetic design and synthetic biology as key areas to develop bacterial nano communication networks.

Systems Biology↗