Computational models of melanoma.

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research

Recipes for calibration and validation of agent-based models in cancer biomedicine

Computational models and simulations are not just appealing because of their intrinsic characteristics across spatiotemporal scales, scalability, and predictive power, but also because the set of problems in cancer biomedicine that can be addressed computationally exceeds the set of those amenable to analytical solutions. Agent-based models and simulations are especially interesting candidates among computational modelling strategies in cancer research due to their capabilities to replicate realistic local and global interaction dynamics at a convenient and relevant scale. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature to explore strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on validation approached as simulation calibration. We argue that simulation calibration goes beyond parameter optimization by embedding informative priors to generate plausible parameter configurations across multiple dimensions

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

TIME AND CAUSALITY IN GENOMICS DATA

The ability to sequence the genomic information that describes individual cell states has provided enormous insight into biological systems. However, to sequence the genomic information within a cell, the cell must be killed, preventing measurements from the future states that cell would have occupied had it been allowed to survive. Thus, sequencing measurements only provide a single snapshot in time of cellular genomic states. Often the ultimate goal of an analysis is to derive mechanistic insight into the biology of a system or process from the data. However, such mechanistic, causal inference is almost impossible without temporal information because causality in standard formulations is based on the concept of connected causes and effects through time. This thesis has interacted with time in genomics data in several ways. The first contribution of this thesis is a neural network-based model that attempts to predict future single-cell transcriptomic states from single-cell transcriptomics data sets. This work demonstrates that using metabolic labeling data sets, future RNA states are estimable within the same cell in the short term, providing a proof of principle that can be expanded as genomics data sets with a temporal dimension become more common. The second contribution of this thesis is a simulation of molecular cell states over time, which is able to demonstrate how single time points from cells do not allow for robust mechanistic inference. Further, the simulation conforms to observations that mRNA expression and expression of the corresponding protein are often poorly correlated and provides mechanistic explanations for how this occurs. The final contribution relates to time in a different sense, analyzing the impact of human age on biomarkers used for cancer immunotherapy. We found that older individuals possessed a number of favorable biomarkers at higher levels than their younger counterparts, possibly explaining clinical observations that older individuals do no worse than younger individuals on immune checkpoint therapies despite the usual anticorrelation between patient age and effective immune responses

Development and analysis of Tinker-OpenMM as a GPU-based free energy perturbation engine

The utilization of computational technologies for the lead optimization process is one of the biggest challenges in the computational chemistry field. In this dissertation, I describe the addition of GPU-based absolute and relative free energy calculation methods using polarizable force field AMOEBA to Tinker-OpenMM. I then proceed to test the capabilities of this platform by studying the binding free energy and binding structures of derivatives of the MELK inhibitor IN17. Also, I present the implementation of virial-based pressure control to the Tinker-OpenMM platform that is needed for performing isobaric simulations.Cellular and Molecular Biolog

Multispectral imaging for preclinical assessment of rheumatoid arthritis models

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune condition affecting multiple body systems. Murine models of RA are vital in progressing understanding of the disease. The severity of arthritis symptoms is currently assessed in vivo by observations and subjective scoring which are time-consuming and prone to bias and inaccuracy. The main aim of this thesis is to determine whether multispectral imaging of murine arthritis models has the potential to assess the severity of arthritis symptoms in vivo in an objective manner. Given that pathology can influence the optical properties of a tissue, changes may be detectable in the spectral response. Monte Carlo modelling of reflectance and transmittance for varying levels of blood volume fraction, blood oxygen saturation, and water percentage in the mouse paw tissue demonstrated spectral changes consistent with the reported/published physiological markers of arthritis. Subsequent reflectance and transmittance in vivo spectroscopy of the hind paw successfully detected significant spectral differences between normal and arthritic mice. Using a novel non-contact imaging system, multispectral reflectance and transmittance images were simultaneously collected, enabling investigation of arthritis symptoms at different anatomical paw locations. In a blind experiment, Principal Component (PC) analysis of four regions of the paw was successful in identifying all 6 arthritic mice in a total sample of 10. The first PC scores for the TNF dARE arthritis model were found to correlate significantly with bone erosion ratio results from microCT, histology scoring, and the manual scoring method. In a longitudinal study at 5, 7 and 9 weeks the PC scores identified changes in spectral responses at an early stage in arthritis development for the TNF dARE model, before clinical signs were manifest. Comparison of the multispectral image data with the Monte Carlo simulations suggest that in this study decreased oxygen saturation is likely to be the most significant factor differentiating arthritic mice from their normal littermates. The results of the experiments are indicative that multispectral imaging performs well as an assessor of arthritis for RA models and may outperform existing techniques. This has implications for better assessment of preclinical arthritis and hence for better experimental outcomes and improvement of animal welfare

Advances in quantitative microscopy

Microscopy allows us to peer into the complex deeply shrouded world that the cells of our body grow and thrive in. With the emergence of automated digital microscopes and software for anlysing and processing the large numbers of image that they produce; quantitative microscopy approaches are now allowing us to answer ever larger and more complex biological questions. In this thesis I explore two trends. Firstly, that of using quantitative microscopy for performing unbiased screens, the advances made here include developing strategies to handle imaging data captured from physiological models, and unsupervised analysis screening data to derive unbiased biological insights. Secondly, I develop software for analysing live cell imaging data, that can now be captured at greater rates than ever before and use this to help answer key questions covering the biology of how cells make the decision to arrest or proliferate in response to DNA damage. Together this thesis represents a view of the current state of the art in high-throughput quantitative microscopy and details where the field is heading as machine learning approaches become ever more sophisticated.Open Acces

Ultrasound Imaging

In this book, we present a dozen state of the art developments for ultrasound imaging, for example, hardware implementation, transducer, beamforming, signal processing, measurement of elasticity and diagnosis. The editors would like to thank all the chapter authors, who focused on the publication of this book

Unveiling the Molecular Mechanisms Regulating the Activation of the ErbB Family Receptors at Atomic Resolution through Molecular Modeling and Simulations

The EGFR/ErbB/HER family of kinases contains four homologous receptor tyrosine kinases that are important regulatory elements in key signaling pathways. To elucidate the atomistic mechanisms of dimerization-dependent activation in the ErbB family, we have performed molecular dynamics simulations of the intracellular kinase domains of the four members of the ErbB family (those with known kinase activity), namely EGFR, ErbB2 (HER2) and ErbB4 (HER4) as well as ErbB3 (HER3), an assumed pseudokinase, in different molecular contexts: monomer vs. dimer, wildtype vs. mutant. Using bioinformatics and fluctuation analyses of the molecular dynamics trajectories, we relate sequence similarities to correspondence of specific bond-interaction networks and collective dynamical modes. We find that in the active conformation of the ErbB kinases (except ErbB3), key subdomain motions are coordinated through conserved hydrophilic interactions: activating bond-networks consisting of hydrogen bonds and salt bridges. The inactive conformations also demonstrate conserved bonding patterns (albeit less extensive) that sequester key residues and disrupt the activating bond network. Both conformational states have distinct hydrophobic advantages through context-specific hydrophobic interactions. The inactive ErbB3 kinase domain also shows coordinated motions similar to the active conformations, in line with recent evidence that ErbB3 is a weakly active kinase, though the coordination seems to arise from hydrophobic interactions rather than hydrophilic ones. We show that the functional (activating) asymmetric kinase dimer interface forces a corresponding change in the hydrophobic and hydrophilic interactions that characterize the inactivating interaction network, resulting in motion of the αC-helix through allostery. Several of the clinically identified activating kinase mutations of EGFR act in a similar fashion to disrupt the inactivating interaction network. Our molecular dynamics study reveals the asymmetric dimer interface helps progress the ErbB family through the activation pathway using both hydrophilic and hydrophobic interaction. There is a fundamental difference in the sequence of events in EGFR activation compared with that described for the Src kinase Hck