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Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore the spatiotemporal evolution of the tumor and its microenvironment. However, these models face serious drawbacks in the realm of parameterization – ABM parameters are typically set individually based on various data and literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit to simple time-course data (such as tumor volume), that type of data loses the spatial information that is a defining feature of ABMs. While tumor images provide spatial information, it is exceedingly difficult to compare tumor images to ABM simulations beyond a qualitative visual comparison. Without a quantitative method of comparing the similarity of tumor images to ABM simulations, a rigorous parameter fitting is not possible. Here, we present a novel approach that applies neural networks to represent both tumor images and ABM simulations as low dimensional points, with the distance between points acting as a quantitative measure of difference between the two. This enables a quantitative comparison of tumor images and ABM simulations, where the distance between simulated and experimental images can be minimized using standard parameter-fitting algorithms. Here, we describe this method and present two examples to demonstrate the application of the approach to estimate parameters for two distinct ABMs. Overall, we provide a novel method to robustly estimate ABM parameters.

80 I. 80 II. 81 III. 83 IV. 83 84 References 84 SUMMARY: Floral traits often show correlated variation, both within and across species. One explanation for this pattern of floral integration is that different elements of floral phenotypes are controlled by the same genes, that is, that the genetic architecture is pleiotropic. Recent studies from a range of model systems suggest that the pleiotropy is common among the loci responsible for floral divergence. Moreover, the effects of allelic substitutions at these loci are overwhelmingly aligned with direction of divergence, suggesting that the nature of the pleiotropic effects was adaptive. Molecular genetic studies have revealed the functional basis for some of these effects, although much remains to be discovered with respect to the molecular, biochemical and developmental mechanisms underlying most quantitative trait loci (QTL) responsible for floral differences. Developing a detailed understanding of the nature of pleiotropic mutations and their phenotypic consequences is crucial for modeling how the genetic architecture of trait variation influences the tempo and trajectory of floral evolution.

Within the tumor microenvironment, macrophages exist in an immunosuppressive state, preventing T cells from eliminating the tumor. Due to this, research is focusing on immunotherapies that specifically target macrophages in order to reduce their immunosuppressive capabilities and promote T cell function. In this study, we develop an agent-based model consisting of the interactions between macrophages, T cells, and tumor cells to determine how the immune response changes due to three macrophage-based immunotherapeutic strategies: macrophage depletion, recruitment inhibition, and macrophage reeducation. We find that reeducation, which converts the macrophages into an immune-promoting phenotype, is the most effective strategy and that the macrophage recruitment rate and tumor proliferation rate (tumor-specific properties) have large impacts on therapy efficacy. We also employ a novel method of using a neural network to reduce the computational complexity of an intracellular signaling mechanistic model.

Flower form is one of many floral features thought to be shaped by pollinator-mediated selection. Although the drivers of variation in flower shape have often been examined in microevolutionary studies, relatively few have tested the relationship between shape evolution and shifts in pollination system across clades. In the present study, we use morphometric approaches to quantify shape variation across the Andean clade Iochrominae and estimate the relationship between changes in shape and shifts in pollination system using phylogenetic comparative methods. We infer multiple shifts from an ancestral state of narrow, tubular flowers toward open, bowl-shaped, or campanulate flowers as well as one reversal to the tubular form. These transitions in flower shape are significantly correlated with changes in pollination system. Specifically, tubular forms tend to be hummingbird-pollinated and the open forms tend to be insect-pollinated, a pattern consistent with experimental work as well as classical floral syndromes. Nonetheless, our study provides one of the few empirical demonstrations of the relationship between flower shape and pollination system at a macroevolutionary scale.

Background Angiogenesis plays an important role in the survival of tissues, as blood vessels provide oxygen and nutrients required by the resident cells. Thus, targeting angiogenesis is a prominent strategy in many different settings, including both tissue engineering and cancer treatment. However, not all of the approaches that modulate angiogenesis lead to successful outcomes. Angiogenesis-based therapies primarily target pro-angiogenic factors such as vascular endothelial growth factor-A (VEGF) or fibroblast growth factor (FGF) in isolation, and there is a limited understanding of how these promoters combine together to stimulate angiogenesis. Targeting one pathway could be insufficient, as alternative pathways may compensate, diminishing the overall effect of the treatment strategy. Methods To gain mechanistic insight and identify novel therapeutic strategies, we have developed a detailed mathematical model to quantitatively characterize the crosstalk of FGF and VEGF intracellular signaling. The model focuses on FGF- and VEGF-induced mitogen-activated protein kinase (MAPK) signaling to promote cell proliferation and the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) pathway, which promotes cell survival and migration. We fit the model to published experimental datasets that measure phosphorylated extracellular regulated kinase (pERK) and Akt (pAkt) upon FGF or VEGF stimulation. We validate the model with separate sets of data. Results We apply the trained and validated mathematical model to characterize the dynamics of pERK and pAkt in response to the mono- and co-stimulation by FGF and VEGF. The model predicts that for certain ranges of ligand concentrations, the maximum pERK level is more responsive to changes in ligand concentration compared to the maximum pAkt level. Also, the combination of FGF and VEGF indicates a greater effect in increasing the maximum pERK compared to the summation of individual effects, which is not seen for maximum pAkt levels. In addition, our model identifies the influential species and kinetic parameters that specifically modulate the pERK and pAkt responses, which represent potential targets for angiogenesis-based therapies. Conclusions Overall, the model predicts the combination effects of FGF and VEGF stimulation on ERK and Akt quantitatively and provides a framework to mechanistically explain experimental results and guide experimental design. Thus, this model can be utilized to study the effects of pro- and anti-angiogenic therapies that particularly target ERK and/or Akt activation upon stimulation with FGF and VEGF. 2Uswabfw1pT6JLX3vZ683i Video Abstract

Mathematical modeling provides the predictive ability to understand the metabolic reprogramming and complex pathways that mediate cancer cells' proliferation. We present a mathematical model using a multiscale, multicellular approach to simulate avascular tumor growth, applied to pancreatic cancer. The model spans three distinct spatial and temporal scales. At the extracellular level, reaction diffusion equations describe nutrient concentrations over a span of seconds. At the cellular level, a lattice-based energy driven stochastic approach describes cellular phenomena including adhesion, proliferation, viability and cell state transitions, occurring on the timescale of hours. At the sub-cellular level, we incorporate a detailed kinetic model of intracellular metabolite dynamics on the timescale of minutes, which enables the cells to uptake and excrete metabolites and use the metabolites to generate energy and building blocks for cell growth. This is a particularly novel aspect of the model. Certain defined criteria for the concentrations of intracellular metabolites lead to cancer cell growth, proliferation or death. Overall, we model the evolution of the tumor in both time and space. Starting with a cluster of tumor cells, the model produces an avascular tumor that quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids. Through our model simulations, we can investigate the response of individual intracellular species under a metabolic perturbation and investigate how that response contributes to the response of the tumor as a whole. The predicted response of intracellular metabolites under various targeted strategies are difficult to resolve with experimental techniques. Thus, the model can give novel predictions as to the response of the tumor as a whole, identifies potential therapies to impede tumor growth, and predicts the effects of those therapeutic strategies. In particular, the model provides quantitative insight into the dynamic reprogramming of tumor cells at the intracellular level in response to specific metabolic perturbations. Overall, the model is a useful framework to study targeted metabolic strategies for inhibiting tumor growth.

Aim The tomato family Solanaceae is distributed on all major continents except Antarctica and has its centre of diversity in South America. Its worldwide distribution suggests multiple long-distance dispersals within and between the New and Old Worlds. Here, we apply maximum likelihood (ML) methods and newly developed biogeographical stochastic mapping (BSM) to infer the ancestral range of the family and to estimate the frequency of dispersal and vicariance events resulting in its present-day distribution. Location Worldwide. Methods Building on a recently inferred megaphylogeny of Solanaceae, we conducted ML model fitting of a range of biogeographical models with the program 'BioGeoBEARS'. We used the parameters from the best fitting model to estimate ancestral range probabilities and conduct stochastic mapping, from which we estimated the number and type of biogeographical events. Results Our best model supported South America as the ancestral area for the Solanaceae and its major clades. The BSM analyses showed that dispersal events, particularly range expansions, are the principal mode by which members of the family have spread beyond South America. Main conclusions For Solanaceae, South America is not only the family's current centre of diversity but also its ancestral range, and dispersal was the principal driver of range evolution. The most common dispersal patterns involved range expansions from South America into North and Central America, while dispersal in the reverse direction was less common. This directionality may be due to the early build-up of species richness in South America, resulting in large pool of potential migrants. These results demonstrate the utility of BSM not only for estimating ancestral ranges but also in inferring the frequency, direction and timing of biogeographical events in a statistically rigorous framework.

Background Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates. Results In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux. Conclusions Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.