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bioRxiv2021

Single-cell epigenetic analysis reveals principles of chromatin states in H3.3-K27M gliomas

Cancer cells are highly heterogeneous at the transcriptional level and in their epigenetic state. Methods to study epigenetic heterogeneity are limited in throughput and information obtained per cell. Here, we adapted Cytometry by Time of Flight (CyTOF) to analyze a wide panel of histone modifications in primary tumor-derived lines of Diffused Intrinsic Pontine Glioma (DIPG). DIPG is a lethal glioma, driven by histone H3 lysine 27 mutation (H3-K27M). We identified two epigenetically distinct subpopulations in DIGP, reflecting inherent heterogeneity in expression of the mutant histone. These two subpopulations are robust across tumor lines derived from different patients and show differential proliferation capacity and expression of stem-cell and differentiation markers. Moreover, we demonstrate the use of this high-dimensional data to elucidate potential interactions between histone modifications and epigenetic alterations during the cell-cycle. Our work establishes new concepts for the analysis of epigenetic heterogeneity in cancer that could be applied to diverse biological systems.

cancer biology↗

bioRxiv2023

ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells

Understanding mechanisms underlying various physiological and pathological processes requires accurate and fully automated analysis of dense cell populations that collectively migrate, and specifically, relations between biophysical features and cell cycle progression aspects. A seminal tool that led to a leap in real-time study of cell cycle is the fluorescent ubiquitination-based cell cycle indicator (FUCCI). Here, we introduce ConfluentFUCCI, an open-source graphical user interface-based framework designed for fully automated analysis of cell cycle progression, cellular dynamics, and cellular morphology, in highly dense migrating cell collectives. Leveraging state-of-the-art tools, some incorporate deep learning, ConfluentFUCCI offers accurate nuclear segmentation and tracking using FUCCI tags, enabling comprehensive investigation of cell cycle progression at both the tissue and single-cell levels. We compare ConfluentFUCCI to the most recent relevant tool, showcasing its accuracy and efficiency in handling large datasets. Furthermore, we demonstrate the ability of ConfluentFUCCI to monitor cell cycle transitions, dynamics, and morphology within densely packed epithelial cell populations, enabling insights into mechanotransductive regulation of cell cycle progression. The presented tool provides a robust approach for investigating cell cycle-related phenomena in complex biological systems, offering potential applications in cancer research and other fields.

cell biology↗

bioRxiv2025

Transcriptomic profile of embryoid bodies under hypoxia at single cell level

Oxygen availability is a key regulator of cellular physiology and hypoxia plays a central role driving vasculogenesis and angiogenesis during development. While bulk transcriptomics has revealed important oxygen-regulated gene networks, such approaches cannot resolve the cellular heterogeneity and lineage dynamics characteristic of early differentiation. To address this, we generated a single-cell transcriptomic dataset from murine embryoid bodies, a widely used in vitro model of early embryonic development, cultured 8 or 10 days under hypoxic (1% O2) or normoxic (21% O2) conditions for the final 16 or 48 hours of differentiation. This resource enables detailed exploration of how oxygen availability influences lineage specification, vascular and hematopoietic development, and cellular heterogeneity during early differentiation. Beyond developmental biology, the dataset provides a valuable reference for comparative studies of hypoxia responses, benchmarking of single-cell analysis methods, and integrative investigations into oxygen signaling across diverse biological systems.

cell biology↗

bioRxiv2023

Translation in a Box: Orthogonal Evolution in the Saccharomyces cerevisiae Mitochondrion

The ability to re-engineer and creatively evolve the translation system (TS) would allow invention of new coded polymers by altering the amino acid sidechain inventory and by shifting the polypeptide backbone into new chemical spaces. Unfortunately, the TS is difficult to manipulate and is more constrained over evolution than any other biological system. An orthogonal TS, running in parallel to the primary TS within a given host cell, would release constraints and allow TS manipulation. A fully orthogonal TS requires dedicated rRNAs, rProteins, aminoacyl-tRNA synthetases, and initiation and termination factors, none of which interact with the primary TS. The S. cerevisiae mitochondrial TS is fully orthogonal to the cytosolic TS. Mito-rRNAs, mito- rProteins, mito-tRNAs, mito-aminoacyl tRNA synthetases, and mito-translation factors are distinct from, physically separated from, and functionally independent of their cytosolic counterparts. Here, the S. cerevisiae mitochondrial translation system was subjected to various stresses including antibiotics, mutagenesis and truncation of mito-rProteins, or wholesale replacement of mito-rProteins. Directed evolution of these stressed systems was facilitated by controlled transitions between fermentation and respiration, by changing the carbon source in the growth medium; the dependence of S. cerevisiae survival on mitochondrial translation can be toggled on and off. Specific recreation of the resulting mutations recapitulate the evolved phenotypes. The method developed here appears to be a general approach for discovering functional dependencies. Suppressor mutations reveal functional dependencies within the S. cerevisiae mitochondrial TS. For example proteins Rrg9 or Mrx1 interact with the mito-TS and have critical role in its function. The combined results indicate that the S. cerevisiae mitochondrial TS can be engineered and evolved in isolation of the cytosolic TS. SignificanceThe Central Dogma of Molecular Biology rules life on Earth. Information flows from DNA to mRNA to protein. In the last step of the Central Dogma, the translation system decodes mRNA and produces coded proteins by linking amino acids into polymers. Engineering and evolving the translation system could permits full technical control over this process and could lead to the generation of novel polymers. Here, we use the mitochondrial translation system in the budding yeast Saccharomyces cerevisiae for directed evolution of translation. We modify and evolve the translation system both directly and indirectly using antibiotics and gene editing tools and then measure resulting functionality. Our results show this secondary translation system inside S. cerevisiae mitochondria can be used as an approach for translation engineering.

synthetic biology↗

bioRxiv2017

Promoter and transcription factor dynamics tune protein mean and noise strength in a quorum sensing-based feedback synthetic circuit

Gene expression is a fundamental cellular process. Its stochastic fluctuations due to intrinsic and extrinsic sources, known generically as gene expression noise, trigger both beneficial and harmful consequences for the cell behavior.\n\nControlling gene expression noise is of interest in many applications in biotechnology, biomedicine and others. Yet, control of the mean expression level is an equally desirable goal. Here, we analyze a gene synthetic network designed to reduce gene expression noise while achieving a desired mean expression level. The circuit combines a negative feedback loop over the gene of interest, and a cell-to-cell communication mechanism based on quorum sensing. We analyze the ability of the circuit to reduce noise as a function of parameters that can be tuned in the wet-lab, and the role quorum sensing plays. Intrinsic noise is generated by the inherent stochasticity of biochemical reactions. On the other hand, extrinsic noise is due to variability in the cell environment and the amounts of cellular components that affect gene expression. We develop a realistic model of the gene synthetic circuit over the population of cells using mass action kinetics and the stochastic Chemical Langevin Equation to include intrinsic noise, with parameters drawn from a distribution to account for extrinsic noise. Stochastic simulations allow us to quantify the mean expression level and noise strength of all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in E. coli. Our in silico experiments reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. These in silico conclusions are validated by preliminary experimental results. This gene network could have important implications as a robust protein production system in industrial biotechnology.\n\nAuthor SummaryControlling gene expression level is of interest in many applications in biotechnology, biomedicine and others. Yet, the stochastic nature of biochemical reactions plays an important role in biological systems, and cannot be disregarded. Gene expression noise resulting from this stochasticity has been studied over the past years both in vivo, and in silico using mathematical models. Nowadays, synthetic biology approaches allow to design novel biological circuits, drawing on principles elucidated from biology and engineering, for the purpose of decoupled control of mean gene expression and its variance. We propose a gene synthetic circuit with these characteristics, using negative feedback and quorum sensing based cell-to-cell communication to induce population consensus. Our in silico analysis using stochastic simulations with a realistic model reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. Preliminary in vivo results fully agree with the computational ones.

synthetic biology↗

bioRxiv2016

Electroceutical disinfection strategies impair the motility of pathogenic Pseudomonas aeruginosa and Escherichia coli

Electrotaxis or galvanotaxis refers to the migration pattern of cells induced in response to electrical potential. Although it has been extensively studied in mammalian cells, electrotaxis has not been explored in detail in bacterial cells; information regarding the impact of current on pathogenic bacteria is severely lacking. Therefore, we designed a series of single and multi-cue experiments to assess the impact of varying currents on bacterial motility dynamics in pathogenic multi-drug resistant (MDR) strains of Pseudomonas aeruginosa and Escherichia coli using a microfluidic platform. Motility plays key roles in bacterial migration and the colonization of surfaces during the formation of biofilms, which are inherently recalcitrant to removal and resistant to traditional disinfection strategies (e.g. antibiotics). Use of the microfluidic platform allows for exposure to current, which can be supplied at a range that is biocidal to bacteria, yet physiologically safe in humans (single cue). This system also allows for multi-cue experiments where acetic acid, a relatively safe compound with anti-fouling/antimicrobial properties, can be combined with current to enhance disinfection. These strategies may offer substantial therapeutic benefits, specifically for the treatment of biofilm infections, such as those found in the wound environment. Furthermore, microfluidic systems have been successfully used to model the unique microfluidic dynamics present in the wound environment, suggesting that these investigations could be extended to more complex biological systems. Our results showed that the application of current in combination with acetic acid has profound inhibitory effects on MDR strains of P. aeruginosa and E. coli, even with brief applications. Specifically, E. coli motility dynamics and cell survival were significantly impaired starting at a concentration of 125 A DC and 0.31% acetic acid, while P. aeruginosa was impaired at 70 A and 0.31% acetic acid. As these strains are relevant wound pathogens, it is likely that this strategy would be effective against similar strains in vivo and could represent a new approach to hasten wound healing.

bioengineering↗

bioRxiv2020

Scale-free dynamics in animal groups and brain networks

Collective phenomena fascinate by the emergence of order in systems composed of a myriad of small entities. They are ubiquitous in nature and can be found over a vast range of scales in physical and biological systems. Their key feature is the seemingly effortless emergence of adaptive collective behavior that cannot be trivially explained by the properties of the systems individual components. This perspective focuses on recent insights into the similarities of correlations for two apparently disparate phenomena: flocking in animal groups and neuronal ensemble activity in the brain. We first will summarize findings on the spontaneous organization in bird flocks and macro-scale human brain activity utilizing correlation functions and insights from critical dynamics. We then will discuss recent experimental findings that apply these approaches to the collective response of neurons to visual and motor processing, i.e. to local perturbations of neuronal networks at the meso- and microscale. We show how scale-free correlation functions capture the collective organization of neuronal avalanches in evoked neuronal populations in nonhuman primates and between neurons during visual processing in rodents. These experimental findings suggest that the coherent collective neural activity observed at scales much larger than the length of the direct neuronal interactions is demonstrative of a phase transition. We discuss the experimental support for either discontinuous or continuous phase transitions. We conclude that at or near a phase-transition neuronal information can propagate in the brain with the same efficiency as proposed to occur in the collective adaptive response observed in some animal groups.

neuroscience↗

bioRxiv2021

ORT: A workflow linking genome-scale metabolic models with reactive transport codes

MotivationNutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. ResultsWe propose a workflow ( Omics to Reactive Transport - ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Availability and ImplementationInteractive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258 Contactrebecca.rubinstein@subsurfaceinsights.com or roelof.versteeg@subsurfaceinsights.com Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2025

COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer

Single-omics approaches often provide a limited perspective on complex biological systems, whereas multi-omics integration enables a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting complex relationships between biological features--both within and across views--remains a major challenge. To address these challenges, we introduce COSIME (Cooperative Multi-view Integration with a Scalable and Interpretable Model Explainer). COSIME applies the backpropagation of a learnable optimal transport algorithm to deep neural networks, thus enabling the learning of latent features from several views to predict disease phenotypes. It also incorporates Monte Carlo sampling to enable interpretable assessments of both feature importance and pairwise feature interactions for both within and across views. We applied COSIME to both simulated and real-world datasets--including single-cell transcriptomics, spatial transcriptomics, epigenomics and metabolomics--to predict Alzheimers disease-related phenotypes. Benchmarking of existing methods demonstrated that COSIME improves prediction accuracy and provides interpretability. For example, it reveals that synergistic interactions between astrocyte and microglia genes associated with Alzheimers disease are more likely to localize at the edges of the middle temporal gyrus. Finally, COSIME is also publicly available as an open source tool.

bioinformatics↗

bioRxiv2019

Predicting the time of entry of nanoparticles in cellular membranes

The understanding of the molecular interactions between nanoparticles (NPs) and biological systems is crucial for the systematic advance in many high-impact fields, such as biomedicine and nanotechnology. A key aspect to understand and predict the biological effect of NPs, e.g., cytotoxicity, bioavailability, is their interaction with membranes, specifically the mechanisms that regulate passive transport, which controls the permeation of most small molecules. In this paper, we introduce a new streamlined theoretical model that is able to predict the interactions between NPs and biological membranes (average permeation time), by separating the NPs characteristics (i.e., size, shape, solubility) from the membrane properties (density distribution). This factorization allows the inclusion of data obtained from both experimental and computational sources, as well as rapid estimation of large sets of permutation in new membranes. We validated our approach, by comparing our prediction for the interactions between different carbonaceous NPs and lipid bilayers with both experiments of measuring graphene quantum dot leakage encapsulated in lipid vesicles and time of entry from MD simulations.

biophysics↗

bioRxiv2026

Information-Theoretic Origins of Metabolic Scaling

Universal scaling laws are a signature of complex biological systems, yet the mechanisms by which microscopic stochastic processes generate robust macroscopic behaviour remain debated. Here we develop an information-theoretic approach to identify robust biological scaling using a stochastic ontogenetic growth model, in which correlated cellular bursts propagate from microscopic noise to organism-level metabolic fluctuations. We show that an information-neutral regime, defined as the minimum Kullback-Leibler divergence between fluctuation distributions generated under different microscopic correlation structures, identifies stochastic regimes for which macroscopic observables become maximally insensitive to microscopic details. Within this regime, metabolic scaling exponents converge across species sizes to a narrow range close to linear scaling relationship. The information-theoretic optimum is robust to temporal coarse-graining, indicating non-critical scale robustness without requiring proximity to a phase transition. Our results suggest that information neutrality provides a general principle for identifying robust stochastic mechanisms underlying biological scaling and illustrate how universal macroscopic behaviour can emerge from noisy microscopic dynamics.

ecology↗

bioRxiv2018

Ancestral Reconstruction of Protein Interaction Networks

The molecular and cellular basis of novelty is a major open question in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term \"phylogenetic latent variable models\" (PLVMs, pronounced \"plums\") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

evolutionary biology↗

bioRxiv2022

Addressable and adaptable intercellular communication via DNA messaging

Engineered consortia are a major research focus for synthetic biologists because they can implement sophisticated behaviors inaccessible to single-strain systems. However, this functional capacity is constrained by their constituent strains ability to engage in complex communication. DNA messaging, by enabling information-rich channel-decoupled communication, is a promising candidate architecture for implementing complex communication. But its major advantage, its messages dynamic mutability, is still unexplored. We develop a framework for addressable and adaptable DNA messaging that leverages all three of these advantages and implement it in a plasmid conjugation-based communication channel. Our system can bias the transfer of messages to targeted receiver strains by 100-to 1000-fold, and their recipient lists can be dynamically updated in situ to control the flow of information through the population. This work lays the foundation for future developments that further utilize the unique advantages of DNA messaging to engineer previously-inaccessible levels of complexity into biological systems.

synthetic biology↗

bioRxiv2022

Optogenetic Maxwell Demon to Exploit Intrinsic Noise and Control Cell Differentiation Despite Time Delays and Extrinsic Variability

The field of synthetic biology focuses on creating modular components which can be used to generate complex and controllable synthetic biological systems. Unfortunately, the intrinsic noise of gene regulation can be large enough to break these systems. Noise is largely treated as a nuisance and much past effort has been spent to create robust components that are less influenced by noise. However, extensive analysis of noise combined with smart microscopy tools and optognenetic actuators can create control opportunities that would be difficult or impossible to achieve in the deterministic setting. In previous work, we proposed an Optogenetic Maxwells Demons (OMD) control problem and found that deep understanding and manipulation of noise could create controllers that break symmetry between cells, even when those cells share the same optogenetic input and identical gene regulation circuitry. In this paper, we extend those results to analyze (in silico) the robustness of the OMD control under changes in system volume, with time observation/actuation delays, and subject to parametric model uncertainties.

synthetic biology↗

bioRxiv2022

The trans-regulatory landscape of gene networks in plants

The effector domains of transcription factors play a key role in controlling gene expression; however, their functional nature is poorly understood, hampering our ability to explore this fundamental dimension of gene regulatory networks. To map the trans-regulatory landscape in a complex eukaryote, we systematically characterized the putative effector domains of over 400 Arabidopsis thaliana transcription factors for their capacity to modulate transcription. We demonstrate that transcriptional effector activity can be integrated into gene regulatory networks capable of elucidating the functional dynamics underlying gene expression patterns. We further show how newly characterized domains can enhance genome engineering efforts and reveal how plant transcriptional activators share regulatory features conserved across distantly related eukaryotes. Our results provide a framework to systematically characterize the regulatory role of transcription factors at a genome-scale in order to understand the transcriptional wiring of biological systems.

plant biology↗

bioRxiv2024

Emergent Collective Behavior Evolves More Rapidly Than Individual Behavior Among Ant Species

Emergence is a fundamental concept in biology and other disciplines, but whether emergent phenotypes evolve similarly to non-emergent phenotypes is unclear. The hypothesized process of emergent evolution posits that evolutionary change in collective behavior is irreducible to evolutionary change in the intrinsic behaviors of isolated individuals. As a result, collective behavior might evolve more rapidly and diversify more between populations compared to individual behavior. To test if collective behavior evolves emergently, we conducted a large comparative study using 22 ant species and gathered over 1,500 behavioral rhythm time series from hundreds of colonies and isolated individuals, totaling over 1.5 years of behavioral data. We show that analogous traits measured at individual and collective levels exhibit distinct evolutionary patterns. The estimated rates of phenotypic evolution for the rhythmicity of activity in ant colonies were faster than the evolutionary rates of the same behavior measured in isolated individual ants, and total variation across species in collective behavior was higher than variation in individual behavior. We hypothesize that more rapid evolution and higher variation is a general feature of emergent phenotypes relative to lower-level phenotypes across complex biological systems.

evolutionary biology↗

bioRxiv2024

Modification of the microstructure of the CERN- CLEAR-VHEE beam at the picosecond scale modifies ZFE morphogenesis but has no impact on hydrogen peroxide production.

FLASH has emerged as a significant breakthrough for the future of radiation oncology, as it reduces complications while preserving the tumor killing efficacy. To define the beam parameters for future clinical translation, Very High Energy Electrons (VHEE) delivered at CLEAR and able to reach deep seated tumors were used in conjunction with a FLASH-validated Intermediate Energy Electron (IIE) beam and a 160-225 keV X-ray beam, collectively able to deliver dose rates spanning from 1 Gy/min to 1011 Gy/s. High-throughput chemical assays were used to investigate radiochemical effects of FLASH, while zebrafish embryos served as a model to evaluate its impact on biological outcomes and morphogenesis. This study is the first comprehensive exploration investigating the impact of a large range of dose rates and various temporal parameters from early physico-chemical events to a complex biological system. Data derived at CLEAR revealed that the intensity of the bunch is a critical factor for observing the sparing effect of FLASH and uncovered an unforeseen biological response when electrons are delivered over the picosecond timescale. Present data also suggests that scanning with high intensity beamlets will be optimal for the future clinical translation of FLASH. HighlightsTo investigate the physics parameters required to trigger the FLASH sparing effect, CLEAR/VHEE/CERN beam macro/microstructure was varied. We show that delivery at the picosecond scale: - reduces alteration in the morphogenesis of zebrafish embryos, but - has no impact on secondary hydrogen peroxide production, Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=175 HEIGHT=200 SRC="FIGDIR/small/629203v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1df896aorg.highwire.dtl.DTLVardef@b6b824org.highwire.dtl.DTLVardef@1f35964org.highwire.dtl.DTLVardef@171d60f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Principles underlying implementation of nearly-homeostatic biological networks

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/646789v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@3161e2org.highwire.dtl.DTLVardef@1140a1org.highwire.dtl.DTLVardef@aa3c53org.highwire.dtl.DTLVardef@5e9ba5_HPS_FORMAT_FIGEXP M_FIG C_FIG A nearly-homeostatic biological system keeps the steady-state output (like internal body temperature) within a narrow range, regardless of different persistent levels of environmental perturbations (like external temperatures). A nearly-homeostatic system can guarantee performance of a therapeutic device in different patient contexts or can be a detector that generates a response only upon encountering an anomaly. We have developed the inverse homeostasis perspective to determine the impact of each parameter on the systems homeostatic performance, which has allowed us to vastly widen the region of implementable parameter sets supporting near-homeostasis compared to the predominant approach of minimizing the controllers integration leakiness. To implement nearly-homeostatic systems without measuring parameter values, we have used feedback-free auxiliary systems to accurately approximate steady-state response curves of key components of the feedback system and adjusted those curves to attain desired characteristics. Our approach has not only discovered a new mechanism of near-homeostasis, but also serves as a new framework to reinterpret whether the homeostatic performance observed in previously published systems arises from the mechanisms originally proposed to explain the behaviour.

synthetic biology↗