Biological Scenarios Research Articles

Modeling the dynamics of interactions between antibiotic-resistant bacteria and immune response

Understanding the dynamics of interactions between antibiotic-resistant bacteria, the immune response, and the presence of antibiotics is crucial for developing effective strategies to combat antibiotic resistance. In this work, we introduce an innovative mathematical model that delves into the complex dynamics between bacteria that are resistant to antibiotics, the immune system, and the administration of antibiotic treatment. Through qualitative analysis, four distinct stable equilibria have been identified, each representing distinct biological scenarios, providing valuable insights for combating antibiotic resistance.

Larval dispersal and physical connectivity of Pheronema carpenteri populations in the Azores

The study of larval dispersal and connectivity between deep-sea populations is essential for the effective conservation and management of deep-sea environments and the design and implementation of Marine Protected Areas. Dense sponge aggregations, known as “sponge grounds”, are a key component of marine benthic ecosystems, by increasing the structural complexity of the sea floor and providing structure and habitat for many other species. These aggregations are characteristic of the Azores deep-sea environment. These sessile organisms rely primarily on larval dispersal for their reproduction. Connectivity between specific Pheronema carpenteri sponge aggregations in the Azores was studied using a 3-D biophysical dispersal model. Different biological trait scenarios were analyzed, considering spawning seasonality and pelagic larval duration. Model results indicate that regional circulation patterns drive larval dispersion, shaping population connectivity of P. carpenteri sponge aggregations in the Azores, particularly among aggregations in the Central Group of Azorean islands. Some areas present high retention rates, receiving larvae from several sponge aggregations while also being important larval source aggregations. In contrast, aggregations from the Eastern Group may be isolated from the others. Larval dispersal and connectivity patterns were analyzed concerning the current configuration of Marine Protected Areas (MPAs) in the Azores. The results underscored the importance of maintaining protection efforts in existing MPAs and identified stepping-stone locations and specific sites where additional measures could enhance species connectivity in the Azores.

Mapping the potential habitat suitability and opportunities of bush encroacher species in Southern Africa: a case study of the SteamBioAfrica project

Senegalia mellifera (Benth) Seigler & Ebinger., Dichrostachys cinerea (L.) Wight & Arn. and Terminalia sericea Burch. Ex DC., are three important bush encroacher species that contribute to the well-known ecological process named “thicketization” in Southern Africa. This issue has persisted for many years, impacting species distribution, plant communities, soil, and fauna dynamics. According to climate change projections, Southern Africa is expected to become drier and warmer in future scenarios, creating favourable conditions for proliferation of bush encroacher species. MaxEnt is a general-purpose machine learning method widely utilized in various ecological and biological scenarios to predict the potential suitable habitat of species. This is achieved by incorporating presence-only occurrence records and bioclimatic, and topographic variables. The analysis was performed in a Geographic Information System based on the current and future suitable areas for the respective Shared Socioeconomic Pathways (SSP) scenarios according to INM-CM5-0, UK-ESM1-0-LL and MPIES-M1-2-HR climate models. This was done to assess the potential effects of climate change on the distribution patterns of bush encroacher species. Model performance was assessed by the area under the curve (AUC) of the receiver operator characteristic (ROC), with 0.836, 0.822 and 0.738 as results to Terminalia sericea, Dichrostachys cinerea and Senegalia mellifera respectively. The current results show that Senegalia mellifera presents a habitat suitability of 56% (1,460,353 km²) of the total area, while Terminalia sericea and Dichrostachys cinerea have suitability over 37.9% (996,168 km²) and 43.9% (1,154,645 km²) of the area, respectively. These findings indicate that precipitation and temperature variables are the most important factors in explaining the spatial distribution of the bush encroacher species, predicting a future increase between 8–29.4%, 2.8–24%, and 3–24.2% for Senegalia mellifera, Terminalia sericea, and Dichrostachys cinerea respectively. Furthermore, each species has its own set of important variables and different ecological behaviour patterns. These results imply that an improved understanding of the response of woody species to a changing climate is important for managing bush encroachment in savanna ecosystems. Based on our analysis, Senegalia mellifera is currently suitable in 56% of the total area; while 43.9% of the total area is suitable for Dichrostachys cinerea, and 37.87% of the total area is currently suitable for Terminalia sericea. In the future scenarios, the habitat suitability increases for all three species compared to the current state. The unsuitable areas decrease in all the proposed scenarios with respect to the present, predicting a shrub expansion in the future throughout southern Africa. Furthermore, our results imply that Senegalia mellifera is the most potentially affected by bush encroachment in future changes, with a larger distribution area in future scenarios. These findings are supported by many studies, which indicate the probable increase of woody cover and loss of grasslands. Temperature and precipitation patterns are the main drivers behind the distribution of these bush encroachers, increasing or decreasing the competitiveness of these species according to these variables and their phenotypic plasticity. The increase in habitat suitability occurs throughout the case study, but there is a clear trend of shrub expansion towards the south of the case study. The change maps show a pattern of shrub movement towards South Africa and Botswana. This would fit with climate predictions for South Africa, which predict a decrease in rainfall in the east and north of the country, being more suitable for shrub competitive species.

Trouble on the horizon: anticipating biological invasions through futures thinking

ABSTRACTAnticipating future biosecurity threats to prevent their occurrence is the most cost‐effective strategy to manage invasive alien species. Yet, biological invasions are complex, highly uncertain processes. High uncertainty drives decision‐making away from strategic preventative measures and towards operational outcomes aimed at post‐invasion management. The limited success of preventative measures in curbing biological invasions reflects this short‐term mindset and decision‐makers should instead apply strategic foresight to imagine futures where biosecurity threats are minimised. Here, four major futures thinking tools (environmental scanning, driver‐mapping, horizon scanning, and scenario planning) that describe probable, possible, plausible and preferable futures are assessed in terms of their potential to support both research and policy addressing biological invasions. Environmental scanning involves surveying existing data sources to detect signals of emerging alien species through knowledge of changes in either the likelihood or consequences of biological invasions. Several approaches are widely used for biosecurity including automated scans of digital media, consensus‐based expert scoring, and prediction markets. Automated systems can be poor at detecting weak signals because of the large volume of ‘noise’ they generate while expert scoring relies on prior knowledge and so fails to identify unknown unknowns which is also true of prediction markets that work well for quite specific known risks. Driver‐mapping uses expert consensus to identify the political, economic, societal, technological, legislative, and environmental forces shaping the future and is a critical component of strategic foresight that has rarely been applied to biological invasions. Considerable potential exists to extend this approach to develop system maps to identify where biosecurity interventions may be most effective and to explore driver complexes to determine megatrends shaping the future of biological invasions. Horizon scanning is a systematic outlook of potential threats and future developments to detect weak signals of emerging issues that exist at the margins of current thinking. Applications have been strongly focused on emerging issues related to research and technological challenges relevant to biosecurity and invasion science. However, most of these emerging issues are already well known in current‐day research. Because horizon scanning is based on expert consensus, it needs to embrace a diversity of cultural, gender, and disciplinary diversity more adequately to ensure participants think intuitively and outside of their own subject boundaries. Scenario planning constructs storylines that describe alternative ways the political, economic, social, technological, legislative, and environmental situation might develop in the future. Biological invasion scenario planning has favoured structured approaches such as standardised archetypes and uncertainty matrices, but scope exists to apply more intuitive thinking by using incasting, backcasting, or causal layered analysis. Futures thinking in biological invasions has not engaged with decision‐makers or other stakeholders adequately and thus outcomes have been light on policy and management priorities. To date, strategic foresight addressing biological invasions has applied each approach in isolation. Yet, an integrated approach to futures thinking that involves a diverse set of stakeholders in exploring the probable, possible, plausible, and preferable futures relating to biological invasions is crucial to the delivery of strategic biosecurity foresight at both national and global scales.

Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing Data.

Rapid advances in single-cell RNA sequencing (scRNA-seq) have made it possible to characterize cell states at a high resolution view for large scale library. scRNA-seq data contains a great deal of biological information, which can be mainly used to discover cell subtypes and track cell development. However, traditional methods face many challenges in addressing scRNA-seq data with high dimensions and high sparsity. For better analysis of scRNA-seq data, we propose a new framework called MSVGAE based on variational graph auto-encoder and graph attention networks. Specifically, we introduce multiple encoders to learn features at different scales and control for uninformative features. Moreover, different noises are added to encoders to promote the propagation of graph structural information and distribution uncertainty. Therefore, some complex posterior distributions can be captured by our model. MSVGAE maps scRNA-seq data with high dimensions and high noise into the low-dimensional latent space, which is beneficial for downstream tasks. In particular, MSVGAE can handle extremely sparse data. Before the experiment, we create 24 simulated datasets to simulate various biological scenarios and collect 8 real-world datasets. The experimental results of clustering, visualization and marker genes analysis indicate that MSVGAE model has excellent accuracy and robustness in analyzing scRNA-seq data.

A compact, versatile drug-induced splicing switch system with minimal background expression

Gene-switch techniques hold promising applications in contemporary genetics research, particularly in disease treatment and genetic engineering. Here, we developed a compact drug-induced splicing system that maintains low background using a human ubiquitin C (hUBC) promoter and optimized drug (LMI070) binding sequences based on the Xon switch system. To ensure precise subcellular localization of the protein of interest (POI), we inserted a 2A self-cleaving peptide between the extra N-terminal peptide and POI. This streamlined and optimized switch system, named miniXon2G, effectively regulated POIs in different subcellular localizations both invitro and invivo. Furthermore, miniXon2G could be integrated into endogenous gene loci, resulting in precise, reversible regulation of target genes by both endogenous regulators and drugs. Overall, these findings highlight the performance of miniXon2G in controlling protein expression with great potential for general applicability to diverse biological scenarios requiring precise and delicate regulation.

ScCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD’s efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression.

High-Performance Proton Field-Effect Transistor Based on Two-Dimensional Cd Vacancy-Resided Cd0.85PS3Li0.15H0.15.

Ion transport is a critical phenomenon underpinning numerous biological, physical, and chemical systems. Proton transistors leveraging proton transport face significant limitations, such as a low on-off ratio and deficient carrier mobility, which restrict their applicability in biological and other scenarios. This study explores the use of two-dimensional (2D) vacancy-residing transition metal phosphorus trichallcogenide-based membranes as the active layer for proton field-effect transistors. The synthesized Cd0.85PS3Li0.15H0.15 membrane exhibits a well-organized layered structure and high hydrophilicity, with nanometer-sized interlayers containing interconnected water networks. These distinct features facilitate proton conduction, leading to a high proton conductivity value of 0.83 S cm-1 at 98% relative humidity and 90 °C, with an activation energy of 0.26 eV. The Cd0.85PS3Li0.15H0.15-based proton transistor demonstrates tunability via gate voltage, thereby enabling effective modulation of proton flow across source and drain electrodes. The transistor notably showcases superior switching characteristics, with an on/off ratio surpassing 5.51 and a carrier mobility of 8.84 × 10-2 cm2 V-1 s-1. The underlying mechanism for this performance enhancement is attributed to electric-field-induced switching in Cd vacancies. This research boosts the development of highly versatile ionotropic devices by introducing advanced 2D ion-conductive membranes.

Microwave-assisted Facile Synthesized Carbon Dots as "on-off-on" Fluorescence Probes for Mercury and Iodine Ions in Bio-samples and Cell Imaging.

A kind of nitrogen and sulfur co-doped CDs (N, S-CDs) was facilely synthesized using thiourea and citric acid as precursors, which established an "on-off-on" fluorescence probe to sequential detecting mercury and iodine ions inside water and biology samples. Under 360nm excitation, CDs emit blue fluorescence with an optimal emission peak of 425nm (on). The fluorescence of CDs experiences a significant quenching effect upon interaction with Hg2+ ions due to the electron transfer between CDs and Hg2+. This quenching effect is subsequently recovered upon the addition of I- owing to the formation of complexes between Hg2+ and I-. The probe exhibits high selectivity and sensitivity toward Hg2+ and I- with broad linearity in the range of 5-50μM and 15-60μM, respectively, and a low detection limit of 14.336nM and 38.213nM, respectively. The constructed fluorescence probe N, S-CDs has been successfully applied to the detection of Hg2+ and I- in water and biological samples with great recoveries. More importantly, the bioimaging study demonstrated that N, S-CDs are suitable for live monitoring in biological imaging scenarios of Hg2+ and I- in living cells.

Durable, Photostable Omniphobic Synthetic Leather Surfaces with Anti-Biofouling Properties for Hygienic Applications.

Globally, the public health domain is increasingly emphasizing the need for surfaces that can resist bacterial contamination, as the consumption of bacteria-infected substance may cause illnesses. Thus, this study aimed to modify polyurethane (PU) synthetic leather surfaces by coating their upper layer with fluorine-functionalized nano-silica particles (FNPs). This simple modification imparted omniphobic characteristics, realizing anti-biofouling and self-cleaning properties. The effectiveness in preventing bacterial adhesion was confirmed by the dip-inoculation method using Escherichia coli O157:H7 and Staphylococcus epidermidis. Bacterial adhesion was evaluated based on bacterial counts using the pour plate method and by directly enumerating from scanning electron microscopy images. The attachment of bacteria to the modified omniphobic FNPs-coated PU leather surface decreased by over 98.2% compared to that on the bare surface. We expect that the method developed in this study will significantly reduce or even eliminate the potential risks associated with various biological cross-contamination scenarios, thereby enhancing hygiene standards.

Possible detection of atmospheric bioaerosol via LiDAR: a wavelength-based simulation study

This study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats.Graphical

Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In-Flow Identification of Single Cells.

A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi-frequency cell trajectories. Like in a "ping-pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so-derived cell motions are characterized via time-lapse microscopy, cell tracking, and state-of-the-art machine learning algorithms, like the wavelet scattering transform (WST). As a cell-electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug-induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine-like oxidized low-density lipoprotein receptor-1 (LOX-1) transcript levels in human colorectal adenocarcinoma (DLD-1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP-based assays addressing cancer heterogeneity for precision medicine and pharmacological research.

More Is Faster: Why Population Size Matters in Biological Search.

Many biological scenarios have multiple cooperating searchers, and the timing of the initial first contact between any one of those searchers and its target is critically important. However, we are unaware of biological models that predict how long it takes for the first of many searchers to discover a target. We present a novel mathematical model that predicts initial first contact times between searchers and targets distributed at random in a volume. We compare this model with the extreme first passage time approach in physics that assumes an infinite number of searchers all initially positioned at the same location. We explore how the number of searchers, the distribution of searchers and targets, and the initial distances between searchers and targets affect initial first contact times. Given a constant density of uniformly distributed searchers and targets, the initial first contact time decreases linearly with both search volume and the number of searchers. However, given only a single target and searchers placed at the same starting location, the relationship between the initial first contact time and the number of searchers shifts from a linear decrease to a logarithmic decrease as the number of searchers grows very large. More generally, we show that initial first contact times can be dramatically faster than the average first contact times and that the initial first contact times decrease with the number of searchers, while the average search times are independent of the number of searchers. We suggest that this is an underappreciated phenomenon in biology and other collective search problems.

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.

LymphoDose: a lymphocyte dose estimation framework—application to brain radiotherapy

Objective. Severe radiation-induced lymphopenia occurs in 40% of patients treated for primary brain tumors and is an independent risk factor of poor survival outcomes. We developed an in-silico framework that estimates the radiation doses received by lymphocytes during volumetric modulated arc therapy brain irradiation. Approach. We implemented a simulation consisting of two interconnected compartmental models describing the slow recirculation of lymphocytes between lymphoid organs ( M1 ) and the bloodstream ( M2 ). We used dosimetry data from 33 patients treated with chemo-radiation for glioblastoma to compare three cases of the model, corresponding to different physical and biological scenarios: (H1) lymphocytes circulation only in the bloodstream i.e. circulation in M2 only; (H2) lymphocytes recirculation between lymphoid organs i.e. circulation in M1 and M2 interconnected; (H3) lymphocytes recirculation between lymphoid organs and deep-learning computed out-of-field (OOF) dose to head and neck (H&N) lymphoid structures. A sensitivity analysis of the model’s parameters was also performed. Main results. For H1, H2 and H3 cases respectively, the irradiated fraction of lymphocytes was 99.8 ± 0.7%, 40.4 ± 10.2% et 97.6 ± 2.5%, and the average dose to irradiated pool was 309.9 ± 74.7 mGy, 52.6 ± 21.1 mGy and 265.6 ± 48.5 mGy. The recirculation process considered in the H2 case implied that irradiated lymphocytes were irradiated in the field only 1.58 ± 0.91 times on average after treatment. The OOF irradiation of H&N lymphoid structures considered in H3 was an important contribution to lymphocytes dose. In all cases, the estimated doses are low compared with lymphocytes radiosensitivity, and other mechanisms could explain high prevalence of RIL in patients with brain tumors. Significance. Our framework is the first to take into account OOF doses and recirculation in lymphocyte dose assessment during brain irradiation. Our results demonstrate the need to clarify the indirect effects of irradiation on lymphopenia, in order to potentiate the combination of radio-immunotherapy or the abscopal effect.

Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals

In recent years, optical imaging and efficient computational approaches have improved the ability to analyse and understand biological phenomena. Immunofluorescence (IF) is a widely used immunochemical technique that provides information about protein localisation and expression levels. However, the manual analysis of IF images can present important limitations, such as operator workload and interpretative bias. Thus, the development of automated tools for IF signal computation is crucial. Several software programs have been proposed to address this challenge, but there is still a need for more accurate and reliable systems. In this work, we present Q-IF, a software for automatically measuring cellular IF signals with an intuitive and easy-to-use interface. We describe the software and validate its results in different biological scenarios using SH-SY5Y neuroblastoma cells, human fibroblasts, and rat brain sections. The Q-IF system automatically carries out the entire process, from IF signal quantification to statistical analysis, thus evading operator biases and speeding up the analysis workflow. Our results demonstrate the accuracy and reliability of the Q-IF system, highlighting its potential as a valuable tool for IF analysis in biological research.

Immuno-Haematological Indices Stabilizing Attributes of Co-Enzyme Q10 Supplementation in Thioacetamide Poisoned Wistar Rats

Coenzyme Q10 (CoQ10 or Co_Q10) as a known endogenous compound has been linked to some therapeutic functions, thus, the need for further exploration in more dysfunctional biological scenarios. This present study, thus, evaluated the stabilizing attributes of Co-enzyme Q10 supplementation in thioacetamide (TAA) altered immuno-haematological system in Wistar rats. One hundred and twenty (120) male Wistar rats weighing 250 and 280g were used for the study and were randomly separated into four sub-groups of 30 rats per treatment interval of week 3, 6, 9 and 12. In the individual weeks, the rats were further grouped into six different groups of five rats each. The groups included Group 1-control and received 1ml of normal saline (intraperitoneally―ip), Group 2 received only 200 mg/kg TAA (ip) twice weekly, Group 3 received 5mg/kg Co_Q10, daily per oral (po), Group 4 received 10mg/kg Co_Q10 daily (po), Group 5 received 200 mg/kg TAA (ip) twice weekly and 5mg/kg Co_Q10 (po) daily and Group 6 received 200 mg/kg TAA (ip) twice weekly and 10mg/kg Co_Q10 (po) daily. After weeks 3, 6, 9 and 12 of treatments, blood samples were collected and instantly transferred into well labeled blood sample bottles with anticoagulant. After appropriate laboratory and statistical analyses, the quantitative results were obtained. The result showed that following TAA toxicity, the levels of white blood cells (WBC) were significantly (P<0.05) reduced; meanwhile, those of macrophage and tissue necrotic factor alpha (TNFα) were remarkably (P<0.05) increased. However, upon the supplementations with Co-Q10 in the respective weeks (3, 6, 9 and 12) of treatment, all the aforementioned deranged values of WBC, macrophages and TNFα were significantly (P<0.05) stabilized/normalized (i.e. brought to similar range of the normal untreated rats). Therefore, Co-Q10 supplementation may be of beneficial therapeutic value in particular haematological dysfunctional scenarios.

A simple model of nutrient recycling and dormancy in a chemostat: Mathematical analysis and a second-order nonstandard finite difference method

A chemostat is an apparatus that sustains a homogeneous environment through continuous inflow and outflow. Presented is a chemostat model that characterizes the dynamics of dormancy-capable microorganisms. This model of coupled systems of nonlinear ordinary differential equations (ODEs) can apply to various types of organisms, such as different species of bacteria, archaea, algae, fungi, viruses, and protozoa. However, these species reside in different environments and rely on different sets of nutrients. Thus, the model adapts to each species’ limiting nutrient through nutrient recycling. This paper includes a complete stability analysis, supporting phase plane portraits, and accompanying bifurcation diagrams. The paper also proposes an advanced second-order, positivity-preserving, and elementary stable nonstandard finite difference method for solving the mathematical model. Series of numerical simulations are presented that support the theoretical results and explore different biological scenarios. The stability analysis reveals that (1) if the overall dilution, death, and conversion are less than the overall growth, both the dormant and active populations persist when introduced to a chemostat; and (2) if the overall dilution, death, and conversion are more than the overall growth, the microorganism population in its entirety will die off when introduced to a chemostat. Furthermore, the model study suggests that neither dormancy nor nutrient recycling provides substantial survival advantages in a basic chemostat when no threat to the active microorganism is present.

Early adapting to trends: self-stabilizing information spread using passive communication

How to efficiently and reliably spread information in a system is one of the most fundamental problems in distributed computing. Recently, inspired by biological scenarios, several works focused on identifying the minimal communication resources necessary to spread information under faulty conditions. Here we study the self-stabilizing bit-dissemination problem, introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem considers a fully-connected network of nagents, with a binary world of opinions, one of which is called correct. At any given time, each agent holds an opinion bit as its public output. The population contains a source agent which knows which opinion is correct. This agent adopts the correct opinion and remains with it throughout the execution. We consider the basic PULL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {PULL}$$\\end{document} model of communication, in which each agent observes relatively few randomly chosen agents in each round. The goal of the non-source agents is to quickly converge on the correct opinion, despite having an arbitrary initial configuration, i.e., in a self-stabilizing manner. Once the population converges on the correct opinion, it should remain with it forever. Motivated by biological scenarios in which animals observe and react to the behavior of others, we focus on the extremely constrained model of passive communication, which assumes that when observing another agent the only information that can be extracted is the opinion bit of that agent. We prove that this problem can be solved in a poly-logarithmic in n number of rounds with high probability, while sampling a logarithmic number of agents at each round. Previous works solved this problem faster and using fewer samples, but they did that by decoupling the messages sent by agents from their output opinion, and hence do not fit the framework of passive communication. Moreover, these works use complex recursive algorithms with refined clocks that are unlikely to be used by biological entities. In contrast, our proposed algorithm has a natural appeal as it is based on letting agents estimate the current tendency direction of the dynamics, and then adapt to the emerging trend.

A synthetic microbial Daisyworld: planetary regulation in the test tube.

The idea that the Earth system self-regulates in a habitable state was proposed in the 1970s by James Lovelock, who conjectured that life plays a self-regulatory role on a planetary-level scale. A formal approach to such hypothesis was presented afterwards under a toy model known as the Daisyworld. The model showed how such life-geosphere homeostasis was an emergent property of the system, where two species with different properties adjusted their populations to the changing external environment. So far, this ideal world exists only as a mathematical or computational construct, but it would be desirable to have a real, biological implementation of Lovelock's picture beyond our one biosphere. Inspired by the exploration of synthetic ecosystems using genetic engineering and recent cell factory designs, here we propose a possible implementation for a microbial Daisyworld. This includes: (i) an explicit proposal for an engineered design of a two-strain consortia, using pH as the external, abiotic control parameter and (ii) several theoretical and computational case studies including two, three and multiple species assemblies. The special alternative implementations and their implications in other synthetic biology scenarios, including ecosystem engineering, are outlined.