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Results for “systems biology”

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bioRxiv2019

Time-resolved proteomic profile of Amblyomma americanum tick saliva during feeding

Amblyomma americanum ticks transmit more than a third of human tick-borne disease (TBD) agents in the United States. Tick saliva proteins are critical to success of ticks as vectors of TBD agents, and thus might serve as targets in tick antigen-based vaccines to prevent TBD infections. We describe a systems biology approach to identify, by LC-MS/MS, saliva proteins (tick=1182, rabbit=335) that A. americanum ticks likely inject into the host every 24 h during the first 8 days of feeding, and towards the end of feeding using two different sample preparation approaches (in-gel and in-solution). The in-gel approach determined molecular identification of predominant protein bands in tick saliva, and the in-solution added depth to discovery of proteins. Searching against entries in GenBank grouped tick and rabbit proteins in this study into 27 and 25 functional categories. Aside from housekeeping-like proteins, majority of tick saliva proteins belong to the tick-specific (no homology to non-tick organisms: 32%), protease inhibitors (13%), proteases (8%), glycine-rich proteins (6%) and lipocalins (4%) categories. Global secretion dynamics analysis suggests that majority (74%) of proteins in this study are associated with regulating initial tick feeding functions and transmission of pathogens as they are secreted within 24-48 h of tick attachment. Comparative analysis of the A. americanum tick saliva proteome to five other tick saliva proteomes identified 284 conserved tick saliva proteins: we speculate that these regulate critical tick feeding functions and might serve as tick vaccine antigens. We discuss our findings in the context of understanding A. americanum tick feeding physiology as a means through which we can find effective targets for a vaccine against tick feeding.\n\nAuthor SummaryThe lone star tick, Amblyomma americanum, is a medically important species in US that transmits 5 of the 16 reported tick-borne disease agents. Most recently, bites of this tick were associated with red meat allergies in humans. Vaccination of animals against tick feeding has been shown to be a sustainable and effective alternative to current acaricide based tick control method which has several limitations. The pre-requisite to tick vaccine development is to understand the molecular basis of tick feeding physiology. Toward this goal, this study has identified proteins that A. americanum ticks inject into the host at different phases of its feeding cycle. This data set has identified proteins that A. americanum inject into the host within 24-48 h of feeding before it starts to transmit pathogens. Of high importance, we identified 284 proteins that are present in saliva of other tick species, which we suspect regulate important role(s) in tick feeding success and might represent rich source target antigens for a tick vaccine. Overall, this study provides a foundation to understand the molecular mechanisms regulating tick feeding physiology.

systems biology↗

bioRxiv2019

Mutant evolution in spatially structured and fragmented populations

Understanding mutant evolution in spatially structured systems is crucially important for a range of biological systems, including bacterial populations and cancer. While previous work has shown that the mutation load is higher in spatially structured compared to well-mixed systems for neutral mutants in the absence of cell death, we demonstrate a significantly higher degree of complexity, using a comprehensive computational modeling approach that takes into account different mutant fitness, cell death, and different population structures. While an agent-based model assuming nearest neighbor interactions predicts a higher abundance of neutral or advantageous mutants compared to well-mixed systems of the same size, we show that for disadvantageous mutants, results depend on the nature of the disadvantage. In particular, if the disadvantage occurs through higher death rates, as opposed to lower reproduction rates, the result is the opposite, and a lower mutation load occurs in spatial compared to mixed systems. Interestingly, we show that in all cases, the same results are observed in fragmented patch models, where individuals can migrate to randomly chosen patches, thus lacking a strict spatial component. Hence, the results reported for spatial models are the consequence of population fragmentation, and not spatial restrictions per se. We further derive growth laws that characterize the expansion of wild type and mutant populations in different dimensionalities. For example, we find that while disadvantageous mutant abundance scales with the total population size, N, neutral mutants grow faster (as N2 for 1D, as N3/2 in 2D, and as N4/3 in 3D). Advantageous mutants scale with the cube of N in 1D and with the square of N in higher dimensions. These laws are universal (as long as mutants remain a minority) and independent of \"microscopic\" modeling details.

evolutionary biology↗

bioRxiv2023

Variability in the phytoplankton response to upwelling across an iron limitation mosaic within the California Current System

Coastal upwelling currents such as the California Current System (CCS) comprise some of the most productive biological systems on the planet. Diatoms, a distinct taxon of phytoplankton, dominate these upwelling events in part due to their rapid response to nutrient entrainment. In this region, they may also be limited by the micronutrient iron (Fe), an important trace element primarily involved in photosynthesis and nitrogen assimilation. The mechanisms behind how diatoms physiologically acclimate to the different stages of the upwelling conveyor belt cycle with respect to Fe limitation remains largely uncharacterized. Here, we explore their physiological and metatranscriptomic response to the upwelling cycle with respect to the Fe limitation mosaic that exists in the CCS. Subsurface, natural plankton assemblages that would potentially seed surface blooms were examined over wide and narrow shelf regions. The initial biomass and physiological state of the phytoplankton community had a large impact on the overall response to simulated upwelling. Following on-deck incubation under varying Fe physiological states, our results suggest that diatoms quickly dominated the blooms by "frontloading" nitrogen assimilation genes prior to upwelling. However, diatoms subjected to induced Fe limitation exhibited reductions in carbon and nitrogen uptake and decreasing biomass accumulation. Simultaneously, they exhibited a distinct gene expression response which included increased expression of Fe-starvation induced proteins and decreased expression of nitrogen assimilation and photosynthesis genes. These findings may have significant implications for upwelling events in future oceans, where changes in ocean conditions are projected to amplify the gradient of Fe limitation in coastal upwelling regions.

plant biology↗

bioRxiv2017

Coal-Miner: A Coalescent-Based Method For GWA Studies Of Quantitative Traits With Complex Evolutionary Origins

Association mapping (AM) methods are used in genome-wide association (GWA) studies to test for statistically significant associations between genotypic and phenotypic data. The genotypic and phenotypic data share common evolutionary origins - namely, the evolutionary history of sampled organisms - introducing covariance which must be distinguished from the covariance due to biological function that is of primary interest in GWA studies. A variety of methods have been introduced to perform AM while accounting for sample relatedness. However, the state of the art predominantly utilizes the simplifying assumption that sample relatedness is effectively fixed across the genome. In contrast, population genetic theory and empirical studies have shown that sample relatedness can vary greatly across different loci within a genome; this phenomena - referred to as local genealogical variation - is commonly encountered in many genomic datasets. New AM methods are needed to better account for local variation in sample relatedness within genomes.\n\nWe address this gap by introducing Coal-Miner, a new statistical AM method. The Coal-Miner algorithm takes the form of a methodological pipeline. The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively causal markers. Subsequent stages of Coal-Miner perform test for association using a linear mixed model with multiple effects which account for sample relatedness locally within candidate loci and globally across the entire genome.\n\nUsing synthetic and empirical datasets, we compare the statistical power and type I error control of Coal-Miner against state-of-theart AM methods. The simulation conditions reflect a variety of genomic architectures for complex traits and incorporate a range of evolutionary scenarios, each with different evolutionary processes that can generate local genealogical variation. The empirical benchmarks include a large-scale dataset that appeared in a recent high-profile publication. Across the datasets in our study, we find that Coal-Miner consistently offers comparable or typically better statistical power and type I error control compared to the state-of-art methods.\n\nCCS CONCEPTSApplied computing [->] Computational genomics; Computational biology; Molecular sequence analysis; Molecular evolution; Computational genomics; Systems biology; Bioinformatics; Population genetics;\n\nACM Reference formatHussein A. Hejase, Natalie Vande Pol, Gregory M. Bonito, Patrick P. Edger, and Kevin J. Liu. 2017. Coal-Miner: a coalescent-based method for GWA studies of quantitative traits with complex evolutionary origins. In Proceedings of ACM BCB, Boston, MA, 2017 (BCB), 10 pages. DOI: 10.475/123 4

bioinformatics↗

bioRxiv2022

Embodied Latch Mechanism of the Mandible to Power at an Ultrahigh Speed in the Trap-jaw Ants Odontomachus kuroiwae

Rapid movements of limbs and appendages, faster than those produced by simple muscle contraction alone, are generated through mechanical networks consisting of springs and latches. The latch plays a central role in these spring-loaded mechanisms, but the structural details of the latch are not always known. The mandibles of the trapjaw ant Odontomachus kuroiwae closes the mandible extremely quickly to capture prey or to perform mandible-powered defensive jumps to avoid potential threats. The jump is mediated by a mechanical spring and latch system embodied in the mandible. An ant can strike the tip of the mandible onto the surface of an obstacle (prey, predator, or ground) in order to bounce its body away from potential threats. The angular velocity of the closing mandible was 2.3x104 rad/s. Latching of the joint is a key mechanism to aid the storage of energy required to power the ballistic movements of the mandibles. We have identified the fine structure of two latch systems on the mandible forming a balljoint using an X-ray micro-computational tomography system (X-ray micro-CT) and X-ray live imaging with a synchrotron. Here we describe the surface of the inner section of the socket and a projection on the lip of the ball. The X-ray live imaging and movements of the 3D model show that the ball with a detent ridge slipped into a socket and over the socket ridge before snapping back at the groove edge. Our results give insight into the complex spring-latch systems that underpin ultra-fast movements in biological systems.

animal behavior and cognition↗

bioRxiv2023

Harnessing Agent-Based Modeling in CellAgentChat to Unravel Cell-Cell Interactions from Single-Cell Data

Understanding cell-cell interactions (CCIs) is essential yet challenging due to the inherent intricacy and diversity of cellular dynamics. Existing approaches often analyze global patterns of CCIs using statistical frameworks, missing the nuances of individual cell behavior due to their focus on aggregate data. This makes them insensitive in complex environments where the detailed dynamics of cell interactions matter. We introduce CellAgentChat, an agent-based model (ABM) designed to decipher CCIs from single-cell RNA sequencing and spatial transcriptomics data. This approach models biological systems as collections of autonomous agents governed by biologically inspired principles and rules. Validated against seven diverse single-cell datasets, CellAgentChat demonstrates its effectiveness in detecting intricate signaling events across different cell populations. Moreover, CellAgentChat offers the ability to generate animated visualizations of single-cell interactions and provides flexibility in modifying agent behavior rules, facilitating thorough exploration of both close and distant cellular communications. Furthermore, CellAgentChat leverages ABM features to enable intuitive in silico perturbations via agent rule modifications, pioneering new avenues for innovative intervention strategies. This ABM method empowers an in-depth understanding of cellular signaling interactions across various biological contexts, thereby enhancing in-silico studies for cellular communication-based therapies.

bioinformatics↗

bioRxiv2023

Demonstration of Bioplastic Production from CO2 and Formate using the Reductive Glycine Pathway in E. coli

There is a strong need to develop technologies that reduce anthropogenic pollution and the dependence on nonrenewable Earth resources. One way of doing so is by harnessing biological systems for replacing the production of fossil-fuel based goods with low-environmental-impact alternatives. Recently, progress was made in engineering the model organism E. coli to grow using CO2 and formate as its only carbon and energy sources using the reductive glycine pathway (rGlyP). Here, we use this engineered strain of E. coli as a host system for the production of polyhydroxybutyrate (PHB), a biologically derived and biodegradable plastic. We confirmed the production of PHB in this strain using Nile red fluorescent microscopy, transmission electron microscopy, and GC measurements. Since formate can be efficiently generated from CO2 by electrochemical reduction using renewable energy sources, this study serves as a proof of concept for the emerging field of electro-bioproduction.

synthetic biology↗

bioRxiv2025

Integrative structural interactomics reveals protein organization and structure in a giant virus

Giant viruses are large DNA viruses that infect unicellular and multicellular eukaryotes and form exceptionally large extracellular particles. (Meta)genomics and (meta)transcriptomics have provided insight into their diverse coding repertoire, but many of the proteins remain to be characterized as they lack homology with known proteins. Here, we integrated cross-linking mass spectrometry, quantitative proteomics, computational tools and cryo-EM data to characterize the protein architecture of intact melbournevirus particles. Based on this, we allocated 88 viral proteins to different virion sub-compartments and proposed topologies of 25 inner membrane proteins. We assigned eight components of the capsid in cryo-EM data, including proteins that tether the capsid shell to the membrane, reflecting key points in virion maturation. The data provide a valuable resource and demonstrate the power of an integrative approach to gain system-level structural insights into a poorly characterized biological system.

biochemistry↗

bioRxiv2025

Minute-scale single-cell transcriptomics enables dynamic modeling of cellular behavior

AbstractDynamic cellular processes such as signaling, fate decisions, and intercellular communication unfold on minute timescales, a regime inaccessible to conventional transcriptomic methods. This temporal gap has fundamentally limited the development of predictive, causal models of cell behavior. Here, we bridge this gap by introducing ChronoSeq, an automated single-cell RNA sequencing platform that achieves genome-wide profiling with a temporal resolution as brief as seven minutes. By integrating automated live-cell sampling with molecular time-barcoding, ChronoSeq captures rapid transcriptional dynamics with high fidelity. Applying ChronoSeq to TNF- stimulated cells, we discovered a rapid, heterogeneity-driven bifurcation in the NF-{kappa}B response that was previously unobservable. We further demonstrate that the high-density temporal data generated by ChronoSeq enables a new class of computational models that dramatically outperform existing methods in inferring the directionality and targets of post-translationally regulated transcription factors. Finally, in a multicellular co-culture, ChronoSeq resolved a paracrine signaling cascade in real time, identifying both the timing and molecular identity of the intercellular relay. By providing a framework to measure dynamics, infer regulation, and model communication at the true pace of biology, ChronoSeq establishes a new foundation for dynamic systems biology.

bioinformatics↗

bioRxiv2025

Maternal glucocorticoids and behavior shape offspring developmental trade-offs in wild baboons

Mammalian mothers provide behavioral and physiological signals that offspring use to calibrate development in relation to maternal resources and environmental cues. Infants respond selectively as they prioritize certain developmental systems over others, creating developmental tradeoffs between competing biological systems. Here, we investigate the influence of maternal capital ("investment capacity") on the growth and development of their infants in wild olive baboons (Papio anubis) from Laikipia, Kenya. We posit that maternal capital is influenced by a mothers own early life experiences (e.g., drought, maternal loss) and her current life experiences (e.g., dominance rank, food availability), and is signaled to offspring via maternal effort (i.e., nursing and carrying time) and glucocorticoids. We used behavioral data on 40 infants (43% female) in the first year of life to quantify maternal effort, infant play, and infant independence (i.e., frequency of infant departures from mother). We matched these behavioral data with maternal fecal glucocorticoid measures from lactating mothers, and infant growth measures assessed via photogrammetry. Signals of low maternal capital predicted lower rates of infant play, less behavioral independence, and slower growth. There was a negative relationship between the rate of social contact play and growth rate, indicating a developmental tradeoff. Males were more sensitive than females to some of the maternal signals measured in our study. These results add to a growing body of evidence demonstrating that maternal behavioral and physiological signals shape infant development.

animal behavior and cognition↗

bioRxiv2021

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce GAMES: a workflow for Generation and Analysis of Models for Exploring Synthetic systems that includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/465216v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@b1c8feorg.highwire.dtl.DTLVardef@29d13org.highwire.dtl.DTLVardef@1967eb7org.highwire.dtl.DTLVardef@1593bdd_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2017

Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data

Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge--answering questions from high-dimensional data that we have not yet thought to ask.

systems biology↗

bioRxiv2025

Magneto-Photonic Gene Circuit for Minimally Invasive Control of Gene Expression in Mammalian Cells

Precise control of gene expression is one of the fundamental goals of synthetic biology. Whether the objective is to modify endogenous cellular function or induce the expression of molecules for diagnostic and therapeutic purposes, gene regulation remains a key aspect of biological systems. Over time, advances in protein engineering and molecular biology have led to the creation of gene circuits capable of inducing the expression of specific proteins in response to external stimulus such as light. These optogenetic, or light-activated circuits hold significant potential for gene therapy as a tool for regulating the expression of therapeutic genes within cells. However, the applications of optogenetic systems can be limited by the lack of efficient ways for light delivery inside cells or tissue. Our approach to address this challenge is to harness the power of bioluminescence to produce light directly inside cells using a luminescent enzyme. Combined with a photosensitive transcription factor, we report the development of a fully genetically encoded optogenetic circuit for control of gene expression. Furthermore, we utilized a magneto sensitive protein to engineer a split protein version of this luminescent enzyme, where its reconstitution is driven by a 50mT magnetic stimulus. Thus, resulting in a first-of-its-kind gene circuit activated by a combination of light and magnetic stimulus. We expect this work to advance the implementation of light-controlled systems without the need of external light sources, as well as serve as a basis for the development of future magneto-sensitive tools.

synthetic biology↗

bioRxiv2023

The impact of innate immunity on malaria parasite infection dynamics

1Decades of research have investigated the molecular and cellular mechanisms that control the immune response to malaria. Yet, many studies offer conflicting results on the functional impact of innate immunity for controlling parasite replication early in infection. We conduct a meta-analysis to probe for consensus on the effect of innate immunity on parasite replication, examining three different species of rodent malaria parasite. Screening published studies that span four decades of research we collate, curate and statistically analyse infection dynamics in immune deficient or augmented mice to identify and quantify consensus and identify sources of disagreement among studies. Additionally, we estimate whether host factors or experimental methodology shape the impact of immune perturbations on parasite burden. First, we detected meta-analytic mean effect sizes (absolute Cohens h) for the difference in parasite burden between treatment and control groups ranging from 0.1498 to 0.2321 across parasite species. This range is considered a small effect size and translates to a modest change in parasitaemia of roughly 6-12% on average at the peak of infection. Second, we reveal that variation across studies using P. chabaudi or P. yoelii is best explained by stochasticity (due to small sample sizes) rather than by host factors or experimental design. Third, we find that for P. berghei the impact of immune perturbation is increased when young or female mice are used and is greatest when effector molecules (as opposed to upstream signalling molecules) are disrupted (up to an 18% difference in peak parasitaemia). Finally, we find little evidence of publication bias suggesting that our results are robust. The small effects sizes we observe, across three parasite species, following experimental perturbations of the innate immune system may be explained by redundancy in a complex biological system or by incomplete (or inappropriate) data reporting for meta-analysis. Alternatively, our findings might indicate a need to re-evaluate the efficiency with which innate immunity controls parasite replication early in infection. Testing these explanations is necessary to translate understanding from model systems to human malaria infections, manage immunopathology, and facilitate realism in mathematical models.

evolutionary biology↗

bioRxiv2022

Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system

As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop a fast and memory-efficient open-source GPU-based hybrid computational modeling platform Gell (GPU Cell), for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement. Author SummaryNumerical cell simulations provide indispensable insight into the cell-to-tumor tissue transition and help reduce biological experimental variables. However, the availability and practicality of large-scale cell simulation tools have been limited by high computational cost, slow performance, or proprietary. Recent developments in open-source simulation codes and GPU implementation have partially addressed the challenge. We further optimized the cell simulation platform for GPU implementation in this work. As a result, benchmark cell simulation experiments can be performed efficiently on a personal computer with a modern GPU. We made the platform open source to encourage community adoption and collective development.

systems biology↗

bioRxiv2023

A dicer-related helicase opposes the age-related pathology from SKN-1 activation in ASI neurons

Coordination of cellular responses to stress are essential for health across the lifespan. The transcription factor SKN-1 is an essential homeostat that mediates survival in stress-inducing environments and cellular dysfunction, but constitutive activation of SKN-1 drives premature aging thus revealing the importance of turning off cytoprotective pathways. Here we identify how SKN-1 activation in two ciliated ASI neurons in C. elegans results in an increase in organismal transcriptional capacity that drives pleiotropic outcomes in peripheral tissues. An increase in the expression of established SKN-1 stress response and lipid metabolism gene classes of RNA in the ASI neurons, in addition to the increased expression of several classes of non-coding RNA, define a molecular signature of animals with constitutive SKN-1 activation and diminished healthspan. We reveal neddylation as a novel regulator of the SKN-1 homeostat that mediates SKN-1 abundance within intestinal cells. Moreover, RNAi-independent activity of the dicer-related DExD/H-box helicase, drh-1, in the intestine, can oppose the e2ffects of aberrant SKN-1 transcriptional activation and delays age-dependent decline in health. Taken together, our results uncover a cell non-autonomous circuit to maintain organism-level homeostasis in response to excessive SKN-1 transcriptional activity in the sensory nervous system. SIGNIFICANCE STATEMENTUnlike activation, an understudied fundamental question across biological systems is how to deactivate a pathway, process, or enzyme after it has been turned on. The irony that the activation of a transcription factor that is meant to be protective can diminish health was first documented by us at the organismal level over a decade ago, but it has long been appreciated that chronic activation of the human ortholog of SKN-1, NRF2, could lead to chemo- and radiation resistance in cancer cells. A colloquial analogy to this biological idea is a sink faucet that has an on valve without a mechanism to shut the water off, which will cause the sink to overflow. Here, we define this off valve.

genetics↗

bioRxiv2023

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

systems biology↗

bioRxiv2023

Turing pattern formation in reaction-cross-diffusion systems with a bilayer geometry

Conditions for self-organisation via Turings mechanism in biological systems represented by reaction-diffusion or reaction-cross-diffusion models have been extensively studied. Nonetheless, the impact of tissue stratification in such systems is under-explored, despite its ubiquity in the context of a thin epithelium overlying connective tissue, for instance the epidermis and underlying dermal mesenchyme of embryonic skin. In particular, each layer can be subject to extensively different biochemical reactions and transport processes, with chemotaxis - a special case of cross- diffusion - often present in the mesenchyme, contrasting the solely molecular transport typically found in the epidermal layer. We study Turing patterning conditions for a class of reaction-cross- diffusion systems in bilayered regions, with a thin upper layer and coupled by a linear transport law. In particular, the role of differential transport through the interface is explored together with the presence of asymmetry between the homogeneous equilibria of the two layers. A linear stability analysis is carried out around a spatially homogeneous equilibrium state in the asymptotic limit of weak and strong coupling strengths, where quantitative approximations of the bifurcation curve can be computed. Our theoretical findings, for an arbitrary number of reacting species, reveal quantitative Turing conditions, highlighting when the coupling mechanism between the layered regions can either trigger patterning or stabilize a homogeneous equilibrium regardless of the inde- pendent patterning state of each layer. We support our theoretical results through direct numerical simulations, and provide an open source code to explore such systems further.

developmental biology↗