Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types.

The Lotka-Volterra model is widely used to model interactions between two species. Here, we generate synthetic data mimicking competitive, mutualistic and antagonistic interactions between two tumor cell lines, and then use the Lotka-Volterra model to infer the interaction type. Structural identifiability of the Lotka-Volterra model is confirmed, and practical identifiability is assessed for three experimental designs: (a) use of a single data set, with a mixture of both cell lines observed over time, (b) a sequential design where growth rates and carrying capacities are estimated using data from experiments in which each cell line is grown in isolation, and then interaction parameters are estimated from an experiment involving a mixture of both cell lines, and (c) a parallel experimental design where all model parameters are fitted to data from two mixtures (containing both cell lines but with different initial ratios) simultaneously. Each design is tested on data generated from the Lotka-Volterra model with noise added, to determine efficacy in an ideal sense. In addition to assessing each design for practical identifiability, we investigate how the predictive power of the model - i.e., its ability to fit data for initial ratios other than those to which it was calibrated - is affected by the choice of experimental design. The parallel calibration procedure is found to be optimal and is further tested on in silico data generated from a spatially-resolved cellular automaton model, which accounts for oxygen consumption and allows for variation in the intensity level of the interaction between the two cell lines. We use this study to highlight the care that must be taken when interpreting parameter estimates for the spatially-averaged Lotka-Volterra model when it is calibrated against data produced by the spatially-resolved cellular automaton model, since baseline competition for space and resources in the CA model may contribute to a discrepancy between the type of interaction used to generate the CA data and the type of interaction inferred by the LV model.

Analyzing collective motion with machine learning and topology.

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D'Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.

Fetal iron deficiency alters the proteome of adult rat hippocampal synaptosomes.

Fetal and neonatal iron deficiency results in cognitive impairments in adulthood despite prompt postnatal iron replenishment. To systematically determine whether abnormal expression and localization of proteins that regulate adult synaptic efficacy are involved, we used a quantitative proteomic approach (isobaric tags for relative and absolute quantitation, iTRAQ) and pathway analysis to identify dysregulated proteins in hippocampal synapses of fetal iron deficiency model. Rat pups were made iron deficient (ID) from gestational day 2 through postnatal day (P) 7 by providing pregnant and nursing dams an ID diet (4 ppm Fe) after which they were rescued with an iron-sufficient diet (200 ppm Fe). This paradigm resulted in a 40% loss of brain iron at P15 with complete recovery by P56. Synaptosomes were prepared from hippocampi of the formerly iron-deficient (FID) and always iron-sufficient controls rats at P65 using a sucrose gradient method. Six replicates per group that underwent iTRAQ labeling and LC-MS/MS analysis for protein identification and comparison elucidated 331 differentially expressed proteins. Western analysis was used to confirm findings for selected proteins in the glutamate receptor signaling pathway, which regulates hippocampal synaptic plasticity, a cellular process critical for learning and memory. Bioinformatics were performed using knowledge-based Interactive Pathway Analysis. FID synaptosomes show altered expression of synaptic proteins-mediated cellular signalings, supporting persistent impacts of fetal iron deficiency on synaptic efficacy, which likely cause the cognitive dysfunction and neurobehavioral abnormalities. Importantly, the findings uncover previously unsuspected pathways, including neuronal nitric oxide synthase signaling, identifying additional mechanisms that may contribute to the long-term biobehavioral deficits.

Genomics data analysis via spectral shape and topology.

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper, differential gene expression, and spectral shape analysis. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-distributed stochastic neighbor embedding (t-SNE). Although Mapper shows promise in analyzing high-dimensional data, tools to statistically analyze Mapper graphical structures are limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis.

An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models.

The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models requires detailed clinical data. This raises questions about the type and quantity of data that should be collected and when, in order to maximize the information gain about the model behavior while still minimizing the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types. The novel score function introduced in this work eliminates the need for a penalization parameter used in a previous study, while yielding model predictions that are superior to those obtained using two potential pre-determined data collection protocols for two different prostate cancer model scenarios: one in which we fit a simple ODE system to synthetic data generated from a cellular automaton model using radiotherapy as the imposed treatment, and a second scenario in which a more complex ODE system is fit to clinical patient data for patients undergoing intermittent androgen suppression therapy. We also conduct a robust analysis of the calibration results, using both error and uncertainty metrics in combination to determine when additional data acquisition may be terminated.

Candida species differ in their interactions with immature human gastrointestinal epithelial cells.

Life-threatening gastrointestinal (GI) diseases of prematurity are highly associated with systemic candidiasis. This implicates the premature GI tract as an important site for invasion by Candida. Invasive interactions of Candida spp. with immature enterocytes have heretofore not been analyzed. Using a primary immature human enterocyte line, we compared the ability of multiple isolates of different Candida spp. to penetrate, injure, and induce a cytokine response from host cells. Of all the Candida spp. analyzed, C. albicans had the greatest ability to penetrate and injure immature enterocytes and to elicit IL-8 release (p < 0.01). In addition, C. albicans was the only Candida spp. to form filamentous hyphae when in contact with immature enterocytes. Similarly, a C. albicans mutant with defective hyphal morphogenesis and invasiveness had attenuated cytotoxicity for immature enterocytes (p < 0.003). Thus, hyphal morphogenesis correlates with immature enterocyte penetration, injury, and inflammatory responses. Furthermore, variability in enterocyte injury was observed among hyphal-producing C. albicans strains, suggesting that individual organism genotypes also influence host-pathogen interactions. Overall, the finding that Candida spp. differed in their interactions with immature enterocytes implicates that individual spp. may use different pathogenesis mechanisms.

An Agent-Based Model of Combination Oncolytic Viral Therapy and Anti-PD-1 Immunotherapy Reveals the Importance of Spatial Location When Treating Glioblastoma.

Oncolytic viral therapies and immunotherapies are of growing clinical interest due to their selectivity for tumor cells over healthy cells and their immunostimulatory properties. These treatment modalities provide promising alternatives to the standard of care, particularly for cancers with poor prognoses, such as the lethal brain tumor glioblastoma (GBM). However, uncertainty remains regarding optimal dosing strategies, including how the spatial location of viral doses impacts therapeutic efficacy and tumor landscape characteristics that are most conducive to producing an effective immune response. We develop a three-dimensional agent-based model (ABM) of GBM undergoing treatment with a combination of an oncolytic Herpes Simplex Virus and an anti-PD-1 immunotherapy. We use a mechanistic approach to model the interactions between distinct populations of immune cells, incorporating both innate and adaptive immune responses to oncolytic viral therapy and including a mechanism of adaptive immune suppression via the PD-1/PD-L1 checkpoint pathway. We utilize the spatially explicit nature of the ABM to determine optimal viral dosing in both the temporal and spatial contexts. After proposing an adaptive viral dosing strategy that chooses to dose sites at the location of highest tumor cell density, we find that, in most cases, this adaptive strategy produces a more effective treatment outcome than repeatedly dosing in the center of the tumor.

Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer.

With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among individual patients' parameter values, particularly those relating to treatment response; thus, exploiting the use of these various metrics for model calibration can be helpful to infer such patient-specific parameters both accurately and early, so that treatment protocols can be adjusted mid-course for maximum efficacy. However, taking measurements can be costly and invasive, limiting clinicians to a sparse collection schedule. As such, the determination of optimal times and metrics for which to collect data in order to best inform proper treatment protocols could be of great assistance to clinicians. In this investigation, we employ a Bayesian information-theoretic calibration protocol for experimental design in order to identify the optimal times at which to collect data for informing treatment parameters. Within this procedure, data collection times are chosen sequentially to maximize the reduction in parameter uncertainty with each added measurement, ensuring that a budget of n high-fidelity experimental measurements results in maximum information gain about the low-fidelity model parameter values. In addition to investigating the optimal temporal pattern for data collection, we also develop a framework for deciding which metrics should be utilized at each data collection point. We illustrate this framework with a variety of toy examples, each utilizing a radiotherapy treatment regimen. For each scenario, we analyze the dependence of the predictive power of the low-fidelity model upon the measurement budget.

Modeling Oncolytic Viral Therapy, Immune Checkpoint Inhibition, and the Complex Dynamics of Innate and Adaptive Immunity in Glioblastoma Treatment.

Oncolytic viruses are of growing interest to cancer researchers and clinicians, due to their selectivity for tumor cells over healthy cells and their immunostimulatory properties. The immune response to an oncolytic virus plays a critical role in treatment efficacy. However, uncertainty remains regarding the circumstances under which the immune system either assists in eliminating tumor cells or inhibits treatment via rapid viral clearance, leading to the cessation of the immune response. In this work, we develop an ordinary differential equation model of treatment for a lethal brain tumor, glioblastoma, using an oncolytic Herpes Simplex Virus. We use a mechanistic approach to model the interactions between distinct populations of immune cells, incorporating both innate and adaptive immune responses to oncolytic viral therapy (OVT), and including a mechanism of adaptive immune suppression via the PD-1/PD-L1 checkpoint pathway. We focus on the tradeoff between viral clearance by innate immune cells and the innate immune cell-mediated recruitment of antiviral and antitumor adaptive immune cells. Our model suggests that when a tumor is treated with OVT alone, the innate immune cells' ability to clear the virus quickly after administration has a much larger impact on the treatment outcome than the adaptive immune cells' antitumor activity. Even in a highly antigenic tumor with a strong innate immune response, the faster recruitment of antitumor adaptive immune cells is not sufficient to offset the rapid viral clearance. This motivates our subsequent incorporation of an immunotherapy that inhibits the PD-1/PD-L1 checkpoint pathway by blocking PD-1, which we combine with OVT within the model. The combination therapy is most effective for a highly antigenic tumor or for intermediate levels of innate immune localization. Extreme levels of innate immune cell activity either clear the virus too quickly or fail to activate a sufficiently strong adaptive response, yielding ineffective combination therapy of GBM. Hence, we show that the innate and adaptive immune interactions significantly influence treatment response and that combining OVT with an immune checkpoint inhibitor expands the range of immune conditions that allow for tumor size reduction or clearance.

UDP-sugar pyrophosphorylase is essential for pollen development in Arabidopsis.

Arabidopsis UDP-sugar pyrophosphorylase (AtUSP) is a broad substrate enzyme that synthesizes nucleotide sugars. The products of the AtUSP reaction can act as precursors for the synthesis of glycolipids, glycoproteins, and cell wall components including pectin and hemicellulose. AtUSP has no close homologs in Arabidopsis and its biological function has not been clearly defined. We identified two T-DNA insertional mutant lines for AtUSP, usp-1 and usp-2. No homozygous individuals were identified and progeny from plants heterozygous for usp-1 or usp-2 showed a 1:1 segregation ratio under selection. Despite decreased levels of both AtUSP transcript and USP activity (UDP-GlcA-->GlcA-1-P), heterozygous plants were indistinguishable from wild type at all stages of development. Reciprocal test crosses indicated the source of the segregation distortion was lack of transmission through the male gametophyte. Analysis of pollen tetrads from usp-1 in the quartet background revealed a 2:2 ratio of normal:collapsed pollen grains. The collapsed pollen grains were not viable as determined by Alexander's viability and DAPI staining, and pollen germination tests. The pollen phenotype of usp-1 was complemented by transformation of usp-1 with the AtUSP cDNA sequence. Surface and ultrastructural analyses of pollen from wild-type and usp mutants demonstrated that the mutation had no apparent effect on the outer wall (exine) but prevented the synthesis of the pectocellulosic inner wall (intine). Evidence presented here shows that AtUSP has a critical role in pollen development.