Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling.

Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.

Cell microencapsulation techniques for cancer modelling and drug discovery.

Cell encapsulation into spherical microparticles is a promising bioengineering tool in many fields, including 3D cancer modelling and pre-clinical drug discovery. Cancer microencapsulation models can more accurately reflect the complex solid tumour microenvironment than 2D cell culture and therefore would improve drug discovery efforts. However, these microcapsules, typically in the range of 1 - 5000 µm in diameter, must be carefully designed and amenable to high-throughput production. This review therefore aims to outline important considerations in the design of cancer cell microencapsulation models for drug discovery applications and examine current techniques to produce these. Extrusion (dripping) droplet generation and emulsion-based techniques are highlighted and their suitability to high-throughput drug screening in terms of tumour physiology and ease of scale up is evaluated.

3D microencapsulation models of cancer offer a customisable platform to mimic key aspects of solid tumour physiology in vitro. However, many 3D models do not recapitulate the hypoxic conditions and altered tissue stiffness established in many tumour types and stages. Furthermore, microparticles for cancer cell encapsulation are commonly produced using methods that are not necessarily suitable for scale up to high-throughput manufacturing. This review aims to evaluate current technologies for cancer cell-laden microparticle production with a focus on physiological relevance and scalability. Emerging techniques will then be touched on, for production of uniform microparticles suitable for high-throughput drug discovery applications.

Taxis-driven complex patterns of a plankton model.

This paper reports an important conclusion that self-diffusion is not a necessary condition for inducing Turing patterns, while taxis could establish complex pattern phenomena. We investigate pattern formation in a zooplankton-phytoplankton model incorporating phytoplankton-taxis, where phytoplankton-taxis describes the zooplankton that tends to move toward the high-densities region of the phytoplankton population. By using the phytoplankton-taxis sensitivity coefficient as the Turing instability threshold, one shows that the model exhibits Turing instability only when repulsive phytoplankton-taxis is added into the system, while the attractive-type phytoplankton-taxis cannot induce Turing instability of the system. In addition, the system does not exhibit Turing instability when the phytoplankton-taxis disappears. Numerically, we display the complex patterns in 1D, 2D domains and on spherical and zebra surfaces, respectively. In summary, our results indicate that the phytoplankton-taxis plays a pivotal role in giving rise to the Turing pattern formation of the model. Additionally, these theoretical and numerical results contribute to our understanding of the complex interaction dynamics between zooplankton and phytoplankton populations.

Population Pharmacokinetic Models of Venetoclax in Hematologic Malignancies: A Systematic Review.

Several population pharmacokinetic (PPK) models of B cell lymphoma-2 (BCL-2) venetoclax (VEN) have been developed and published to characterize the influencing factors of pharmacokinetics in hematologic malignancies. This review described PPK models of VEN examining the magnitude and types of covariate effects in PK parameters, as well as identified areas that require further investigation in order to facilitate their use. Currently, there are six analyses on PPK models of VEN summarized in this review. Most analyses described the pharmacokinetics of VEN with a two-compartment model and all covariates are categorical. The median estimated apparent clearance (CL/F) was 446 L/Day and apparent volume of distribution of the central compartment (V2/F) was 114.5 L. The median IIV of CL/F reported was 39.5% and V2/F was 46.7%. Most commonly, CYP3A inhibitors, OATP1B3 inhibitors and rituximab co-administration were found to be significant covariates on CL/F. In addition, sex and population were influential covariates on V2/F. A detailed description of the characteristics of PPK models of VEN is provided in this review, as well as the effects of covariates on the PK parameters. For future development of the VEN PPK model, CYP3A inhibitors, rituximab co-administration, OATP1B1 transporter inhibitors, sex, population, and food might be considered. Further research and comprehensive investigations should be undertaken to explore reference ranges for therapeutic drug monitoring, define the potential role of patients with cerebrospinal fluid complications, and assess new or potential covariates. These endeavors will facilitate the development of personalized VEN therapy.

Chaos and bifurcations of a two-dimensional hepatitis C virus model with hepatocyte homeostasis.

In this paper, we delve into the intricate local dynamics at equilibria within a two-dimensional model of hepatitis C virus (HCV) alongside hepatocyte homeostasis. The study investigates the existence of bifurcation sets and conducts a comprehensive bifurcation analysis to elucidate the system's behavior under varying conditions. A significant focus lies on understanding how changes in parameters can lead to bifurcations, which are pivotal points where the qualitative behavior of the system undergoes fundamental transformations. Moreover, the paper introduces and employs hybrid control feedback and Ott-Grebogi-Yorke strategies as tools to manage and mitigate chaos inherent within the HCV model. This chaos arises due to the presence of flip and Neimark-Sacker bifurcations, which can induce erratic behavior in the system. Through the implementation of these control strategies, the study aims to stabilize the system and restore it to a more manageable and predictable state. Furthermore, to validate the theoretical findings and the efficacy of the proposed control strategies, extensive numerical simulations are conducted. These simulations serve as a means of confirming the theoretical predictions and provide insight into the practical implications of the proposed control methodologies. By combining theoretical analysis with computational simulations, the paper offers a comprehensive understanding of the dynamics of the HCV model and provides valuable insights into potential strategies for controlling and managing chaos in such complex biological systems.

The Gamma concept approach as a tool to predict fresh produce supporting or not the growth of L. monocytogenes.

Challenge tests are commonly employed to evaluate the growth behavior of L. monocytogenes in food matrices; they are known for being expensive and time-consuming. An alternative could be the use of predictive models to forecast microbial behavior under different conditions. In this study, the growth behavior of L. monocytogenes in different fresh produce was evaluated using a predictive model based on the Gamma concept considering pH, water activity (aw), and temperature as input factors. An extensive literature search resulted in a total of 105 research articles selected to collect growth/no growth behavior data of L. monocytogenes. Up to 808 L. monocytogenes behavior values and physicochemical characteristics were extracted for different fruits and vegetables. The predictive performance of the model as a tool for identifying the produce commodities supporting the growth of L. monocytogenes was proved by comparing with the experimental data collected from the literature. The model provided satisfactory predictions on the behavior of L. monocytogenes in vegetables (>80% agreement with experimental observations). For leafy greens, a 90% agreement was achieved. In contrast, the performance of the Gamma model was less satisfactory for fruits, as it tends to overestimate the potential of acid commodities to inhibit the growth of L. monocytogenes.

A Computational Framework for the Administration of 5-Aminovulinic Acid Before Glioblastoma Surgery.

5-Aminolevulinic Acid (5-ALA) is the only fluorophore approved by the FDA as an intraoperative optical imaging agent for fluorescence-guided surgery in patients with glioblastoma. The dosing regimen is based on rodent tests where a maximum signal occurs around 6 h after drug administration. Here, we construct a computational framework to simulate the transport of 5-ALA through the stomach, blood, and brain, and the subsequent conversion to the fluorescent agent protoporphyrin IX at the tumor site. The framework combines compartmental models with spatially-resolved partial differential equations, enabling one to address questions regarding quantity and timing of 5-ALA administration before surgery. Numerical tests in two spatial dimensions indicate that, for tumors exceeding the detection threshold, the time to peak fluorescent concentration is 2-7 h, broadly consistent with the current surgical guidelines. Moreover, the framework enables one to examine the specific effects of tumor size and location on the required dose and timing of 5-ALA administration before glioblastoma surgery.

Germination responses of Lens Culiunaris L. seeds to osmotic potentials at cardinal temperatures using hydrothermal time model.

BACKGROUND: Lentil is a significant legume that are consumed as a staple food and have a significant economic impact around the world. The purpose of the present research on lentil was to assess the hydrothermal time model's capacity to explain the dynamics of Lens culinaris L. var. Markaz-09 seed germination, as well as to ascertain the germination responses at various sub-optimal temperatures (T) and water potentials (Ψ). In order to study lentil seed germination (SG) behavior at variable water potentials (Ψs) and temperatures (Ts). A lab experiment employing the hydrothermal time model was created. Seeds were germinated at six distinct temperatures: 15 0С, 20 0С, 25 0С, 30 0С, 35 0С, and 40 0С, with five Ψs of 0, -0.3, -0.6, -0.9, and - 1.2 MPa in a PEG-6000 (Polyethylene glycol 6000) solution. RESULTS: The results indicated that the agronomic parameters like Germination index (GI), Germination energy (GE), Timson germination index (TGI), were maximum in 25 0C at (-0.9 MPa) and lowest at 40 0C in 0 MPa. On other hand, mean germination time (MGT) value was highest at 15 0C in -1.2 MPa and minimum at 40 0C in (-0.6 MPa) while Mean germination rate (MGR) was maximum at 40 0C in (0 MPa) and minimum at 15 0C in (-0.6 MPa). CONCLUSIONS: The HTT model eventually defined the germination response of Lens culinaris L. var. Markaz-09 (Lentil) for all Ts and Ψs, allowing it to be employed as a predictive tool in Lens culinaris L. var. Markaz-09 (Lentil) seed germination simulation models.

Geometric phase predicts locomotion performance in undulating living systems across scales.

Self-propelling organisms locomote via generation of patterns of self-deformation. Despite the diversity of body plans, internal actuation schemes and environments in limbless vertebrates and invertebrates, such organisms often use similar traveling waves of axial body bending for movement. Delineating how self-deformation parameters lead to locomotor performance (e.g. speed, energy, turning capabilities) remains challenging. We show that a geometric framework, replacing laborious calculation with a diagrammatic scheme, is well-suited to discovery and comparison of effective patterns of wave dynamics in diverse living systems. We focus on a regime of undulatory locomotion, that of highly damped environments, which is applicable not only to small organisms in viscous fluids, but also larger animals in frictional fluids (sand) and on frictional ground. We find that the traveling wave dynamics used by mm-scale nematode worms and cm-scale desert dwelling snakes and lizards can be described by time series of weights associated with two principal modes. The approximately circular closed path trajectories of mode weights in a self-deformation space enclose near-maximal surface integral (geometric phase) for organisms spanning two decades in body length. We hypothesize that such trajectories are targets of control (which we refer to as "serpenoid templates"). Further, the geometric approach reveals how seemingly complex behaviors such as turning in worms and sidewinding snakes can be described as modulations of templates. Thus, the use of differential geometry in the locomotion of living systems generates a common description of locomotion across taxa and provides hypotheses for neuromechanical control schemes at lower levels of organization.

C. elegans foraging as a model for understanding the neuronal basis of decision-making.

Animals have evolved to seek, select, and exploit food sources in their environment. Collectively termed foraging, these ubiquitous behaviors are necessary for animal survival. As a foundation for understanding foraging, behavioral ecologists established early theoretical and mathematical frameworks which have been subsequently refined and supported by field and laboratory studies of foraging animals. These simple models sought to explain how animals decide which strategies to employ when locating food, what food items to consume, and when to explore the environment for new food sources. These foraging decisions involve integration of prior experience with multimodal sensory information about the animal's current environment and internal state. We suggest that the nematode Caenorhabditis elegans is well-suited for a high-resolution analysis of complex goal-oriented behaviors such as foraging. We focus our discussion on behavioral studies highlighting C. elegans foraging on bacteria and summarize what is known about the underlying neuronal and molecular pathways. Broadly, we suggest that this simple model system can provide a mechanistic understanding of decision-making and present additional avenues for advancing our understanding of complex behavioral processes.

Establishment of a Four-Cell In Vitro Blood-Brain Barrier Model With Human Primary Brain Cells.

The blood-brain barrier (BBB) constitutes a crucial protective anatomical layer with a microenvironment that tightly controls material transit. Constructing an in vitro BBB model to replicate in vivo features requires the sequential layering of constituent cell types. Maintaining heightened integrity in the observed tight junctions during both the establishment and post-experiment phases is crucial to the success of these models. We have developed an in vitro BBB model that replicates the cellular composition and spatial orientation of in vivo BBB observed in humans. The experiment includes comprehensive procedures and steps aimed at enhancing the integration of the four-cell model. Departing from conventional in vitro BBB models, our methodology eliminates the necessity for pre-coated plates to facilitate cell adhesion, thereby improving cell visualization throughout the procedure. An in-house coating strategy and a simple yet effective approach significantly reduce costs and provides superior imaging of cells and corresponding tight junction protein expression. Also, our BBB model includes all four primary cell types that are structural parts of the human BBB. With its innovative and user-friendly features, our in-house optimized in vitro four-cell-based BBB model showcases novel methodology and provides a promising experimental platform for drug screening processes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Coating and culture system Basic Protocol 2: Cell seeding and Transwell insert handling Basic Protocol 3: Assessment of model functionality.

The unexpected influence of legacy conspecific density dependence.

When plants die, neighbours escape competition. Living conspecifics could disproportionately benefit because they are freed from negative intraspecific processes; however, if the negative effects of past conspecific neighbours persist, other species might be advantaged, and diversity might be maintained through legacy effects. We examined legacy effects in a mapped forest by modelling the survival of 37,212 trees of 23 species using four neighbourhood properties: living conspecific, living heterospecific, legacy conspecific (dead conspecifics) and legacy heterospecific densities. Legacy conspecific effects proved nearly four times stronger than living conspecific effects; changes in annual survival associated with legacy conspecific density were 1.5% greater than living conspecific effects. Over 90% of species were negatively impacted by legacy conspecific density, compared to 47% by living conspecific density. Our results emphasize that legacies of trees alter community dynamics, revealing that prior research may have underestimated the strength of density dependent interactions by not considering legacy effects.

Intransitive Stability Collapses Under the Influence of Dominant Competitors.

AbstractIntransitive competition has received much attention over the past decade. Indeed, these cyclic arrangements of species interactions have the potential to promote and stabilize species coexistence. However, the importance of intransitive interactions in real-world species-rich communities containing a mixture of hierarchic and intransitive interactions remains unknown. Here, using simulations, we explore the behavior of intransitive loops when they interact with outer competitors, as would be expected in real-world communities. Our results show that dominant competitors often cancel the beneficial effects of intransitive loops of inferior competitors. These results call for caution when inferring beneficial effects of intransitivity on species coexistence. Although intransitive loops are a frequent motif in competition networks, their positive effects on species coexistence may be less important than previously thought. The specific properties of a subnetwork-such as stabilization by intransitive loops-should thus not be interpreted independently of the global network.

Dynamic Trait Distribution as a Source for Shifts in Interaction Strength and Population Density.

AbstractIntraspecific trait variation has been increasingly recognized as an important factor in determining species interactions and diversity. Eco-evolutionary models have studied the distribution of trait values within a population that changes over the generations as a result of selection and heritability. Nonheritable traits that can change within the lifetime, such as behavior, can cause trait-mediated indirect effects, often studied by modeling the dynamics of a homogeneous trait. Complementary to these approaches, we study the distribution of traits within a population and its dynamics on short timescales due to ecological processes. We consider several mechanisms by which the trait distribution can shift dynamically: phenotypic plasticity within each individual, differential growth among individuals, and preferential consumption by the predator. Through a simple predator-prey model that explicitly tracks the trait distribution within the prey, we identify the density and trait effects from the predator. We show that the dynamic shift of the trait distribution can lead to the modification of interaction strength between species and result in otherwise unexpected consequences. A particular example is the emergent promotion of the prey by the predator, where the introduction of the predator causes the prey population to increase rather than decrease.

Estimation of spreading speeds and travelling waves for the lattice pioneer-climax competition system.

This paper concerns the invasion dynamics of the lattice pioneer-climax competition model with parameter regions in which the system is non-monotone. We estimate the spreading speeds and establish appropriate conditions under which the spreading speeds are linearly selected. Moreover, the existence of travelling waves is determined by constructing suitable upper and lower solutions. It shows that the spreading speed coincides with the minimum wave speed of travelling waves if the diffusion rate of the invasive species is larger or equal to that of the native species. Our results are new to estimate the spreading speed of non-monotone lattice pioneer-climax systems, and the techniques developed in this work can be used to study the invasion dynamics of the pioneer-climax system with interaction delays, which could extend the results in the literature. The analysis replies on the construction of auxiliary systems, upper and lower solutions, and the monotone dynamical system approach.

Using Gas Exchange to Study CO2 Release During Photosynthesis with Steady- and Nonsteady-State Approaches.

Measures of respiration in the light and Ci* are crucial to the modeling of photorespiration and photosynthesis. This chapter provides background on the equations used to model C3 photosynthesis and the history of the incorporation of the effects of rubisco oxygenation into these models. It then describes three methods used to determine two key parameters necessary to incorporate photorespiratory effects into C3 photosynthesis models: respiration in the light (RL) and Ci*. These methods include the Laisk, Yin, and isotopic methods. For the Laisk method, we also introduce a new rapid measurement technique.

Reaction-Kinetic Modeling of Photorespiration Using Modelbase.

Plant science has become more and more complex. With the introduction of new experimental techniques and technologies, it is now possible to explore the fine details of plant metabolism. Besides steady-state measurements often applied in gas-exchange or metabolomic analyses, new approaches, e.g., based on 13C labeling, are now available to understand the changes in metabolic concentrations under fluctuating environmental conditions in the field or laboratory. To explore those transient phenomena of metabolite concentrations, kinetic models are a valuable tool. In this chapter, we describe ways to implement and build kinetic models of plant metabolism with the Python software package modelbase. As an example, we use a part of the photorespiratory pathway. Moreover, we show additional functionalities of modelbase that help to explore kinetic models and thus can reveal information about a biological system that is not easily accessible to experiments. In addition, we will point to extra information on the mathematical background of kinetic models to give an impetus for further self-study.

Cell Division and Motility Enable Hexatic Order in Biological Tissues.

Biological tissues transform between solid- and liquidlike states in many fundamental physiological events. Recent experimental observations further suggest that in two-dimensional epithelial tissues these solid-liquid transformations can happen via intermediate states akin to the intermediate hexatic phases observed in equilibrium two-dimensional melting. The hexatic phase is characterized by quasi-long-range (power-law) orientational order but no translational order, thus endowing some structure to an otherwise structureless fluid. While it has been shown that hexatic order in tissue models can be induced by motility and thermal fluctuations, the role of cell division and apoptosis (birth and death) has remained poorly understood, despite its fundamental biological role. Here we study the effect of cell division and apoptosis on global hexatic order within the framework of the self-propelled Voronoi model of tissue. Although cell division naively destroys order and active motility facilitates deformations, we show that their combined action drives a liquid-hexatic-liquid transformation as the motility increases. The hexatic phase is accessed by the delicate balance of dislocation defect generation from cell division and the active binding of disclination-antidisclination pairs from motility. We formulate a mean-field model to elucidate this competition between cell division and motility and the consequent development of hexatic order.

A microscopic model of the dose distribution in hepatocellular carcinoma after selective internal radiation therapy.

The dosimetry evaluation for the selective internal radiation therapy is currently performed assuming a uniform activity distribution, which is in contrast with literature findings. A 2D microscopic model of the perfused liver was developed to evaluate the effect of two different 90Y microspheres distributions: i) homogeneous partitioning with the microspheres equally distributed in the perfused liver, and ii) tumor-clustered partitioning where the microspheres distribution is inferred from the patient specific images. METHODS: Two subjects diagnosed with liver cancer were included in this study. For each subject, abdominal CT scans acquired prior to the SIRT and post-treatment 90Y positron emission tomography were considered. Two microspheres partitionings were simulated namely homogeneous and tumor-clustered partitioning. The homogeneous and tumor-clustered partitionings were derived starting from CT images. The microspheres radiation is simulated by means of Russell's law. RESULTS: In homogenous simulations, the dose delivery is uniform in the whole liver while in the tumor-clustered simulations a heterogeneous distribution of the delivered dose is visible with higher values in the tumor regions. In addition, in the tumor-clustered simulation, the delivered dose is higher in the viable tumor than in the necrotic tumor, for all patients. In the tumor-clustered case, the dose delivered in the non-tumoral tissue (NTT) was considerably lower than in the perfused liver. CONCLUSIONS: The model proposed here represents a proof-of-concept for personalized dosimetry assessment based on preoperative CT images.

Considering Joule heating in coupled electroporation and electrodeformation modeling of glioblastoma cells.

Cells exposed to a pulsed electric field undergo electroporation(EP) and electrodeformation(ED) under electric field stress, and a coupled model of EP and ED of glioblastoma(GBM) taking into account Joule heating is proposed. The model geometry is extracted from real cell boundaries, and the effects of Joule heating-induced temperature rise on the EP and ED processes are considered. The results show that the temperature rise will increase the cell's local conductivity, leading to a decrease in the transmembrane potential(TMP). The temperature rise also causes a decrease in the dynamic Young's modulus of the cell membrane, making the cell less resistant to deformation. In addition, GBM cells are more susceptible to EP in the middle portion of the cell and ED in the three tentacle portions under pulsed electric fields, and the cells undergo significant positional shifts. The ED of the nucleus is similar to spherical cells, but the degree of ED is smaller.