This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies... Show moreThis thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to find relations in protein-ligand bioactivity data and then predict using a virtual screen whether compounds that had never been tested on a particular protein, or set of proteins. With this, sets of compounds were suggested for experimental validation that were significant in a myriad of ways. Another study investigated the mutational patterns in cancer, applying a large dataset of mutation data and identifying several motifs in G protein-coupled receptors. The thesis also contains the work done on the Papyrus dataset, a large scale bioactivity dataset that focuses on standardising data for computational drug discovery and providing an out-of-the-box set that can be used in a variety of settings. Show less
There is a need for alternative methods to replace, reduce and refine (3R) animal experimentation. Combining experimental data from high-throughput in vitro studies with in silico modeling is a... Show moreThere is a need for alternative methods to replace, reduce and refine (3R) animal experimentation. Combining experimental data from high-throughput in vitro studies with in silico modeling is a promising approach to unravel the effect of chemicals on living cells and to gain a better understanding of the processes leading to adverse effects. Exposure to chemicals can activate various stress response pathways that limit the amount of cellular damage, help cells to recover or orchestrate irreversible cell fates such as apoptosis. In this thesis, we use experimental data and current knowledge on stress pathway activation and cell fate to create different types of computational models. With these models, we mathematically describe intracellular protein signaling cascades activated upon exposure to various compounds and their link to cell fate. In this way, we integrate molecular-level biological processes to cell-level phenomena such as cell cycle progression, senescence and necrosis, and generate new hypotheses about the mechanisms underlying adversity. Show less
Herrmann, D.P.; Kalkman, R.K.; Frijns, J.H.M.; Bahmer, A. 2023
Triphasic pulse stimulation can prevent unpleasant facial nerve stimulation in cochlear implant users. Using electromyographic measurements on facial nerve effector muscles, previous studies have... Show moreTriphasic pulse stimulation can prevent unpleasant facial nerve stimulation in cochlear implant users. Using electromyographic measurements on facial nerve effector muscles, previous studies have shown that biphasic and triphasic pulse stimulations produce different input-output functions. However, little is known about the intracochlear effects of triphasic stimulation and how these may contribute to the amelioration of facial nerve stimulation.The present study used a computational model of implanted human cochleae to investigate the effect of pulse shape on the intracochlear spread of excitation. Biphasic and triphasic pulse stimulations were simulated from three different cochlear implant electrode contact positions. To validate the model results, experimental spread of excitation measurements were conducted with biphasic and triphasic pulse stimulation from three different electrode contact positions in 13 cochlear implant users.The model results depict differences between biphasic and triphasic pulse stimulations depending on the position of the stimulating electrode contact. While biphasic and triphasic pulse stimulations from a medial or basal electrode contact caused similar extents of neural excitation, differences between the pulse shapes were observed when the stimulating contact was located in the cochlear apex. In contrast, the experimental results showed no difference between the biphasic and triphasic initiated spread of excitation for any of the tested contact positions. The model was also used to study responses of neurons without peripheral processes to mimic the effect of neural degeneration. For all three contact positions, simulated degeneration shifted the neural responses towards the apex. Biphasic pulse stimulation showed a stronger response with neural degeneration compared to without degeneration, while triphasic pulse stimulation showed no difference.As previous measurements have demonstrated an ameliorative effect of triphasic pulse stimulation on facial nerve stimulation from medial electrode contact positions, the results imply that a complementary effect located at the facial nerve level must be responsible for reducing facial nerve stimulation. Show less
Epithelial-mesenchymal plasticity (EMP) and tumor cell migration play an important role in cancer progression, and an improved understanding of the mechanisms underlying these concepts is essential... Show moreEpithelial-mesenchymal plasticity (EMP) and tumor cell migration play an important role in cancer progression, and an improved understanding of the mechanisms underlying these concepts is essential for developing new targeted approaches. In this thesis, we studied these mechanisms using mathematical and computational approaches.First, we summarized and reviewed previous computational approaches that have been used to decipher EMP regulation. We then created mathematical models to explore (1) how different regulatory networks can explain epithelial-mesenchymal transition (EMT) in different cell contexts, and (2) how EMP and immune regulation can interact to cause tumor immunoevasion.Next, we studied the role of cell density in migration characteristics of triple-negative breast cancer cell lines by using a combined experimental and computational approach. We show how clustering and pseudopodial dynamics, potentially influenced by EMT-related factors, can alter the migratory behavior of these cell lines.Jointly, our work has shown that computational modeling can be used to test hypotheses based on experimental data, and generate testable hypotheses, making it a valuable addition to wet-lab experiments. Importantly, we identified mechanisms related to potential therapeutic targets, hopefully leading to improved targeted therapies and reduced cancer mortality. Show less
The overarching goal of this thesis was to examine the behavioral, computational, and neural mechanisms underlying social learning in adolescence. The first aim was to examine developmental... Show moreThe overarching goal of this thesis was to examine the behavioral, computational, and neural mechanisms underlying social learning in adolescence. The first aim was to examine developmental patterns across adolescence of two forms of social learning: (1) learning about other people, specifically, whether they are (un)cooperative and (un)trustworthy, and (2) learning for other people (prosocial learning) to know what actions may benefit or help others. I made use of multiple experimental paradigms based on well-known economic games and/or probabilistic reinforcement learning paradigms to assess these forms of social learning. Secondly, I aimed to examine underlying mechanisms and factors that account for age-related and individual differences in social learning. Applying computational modeling and functional neuroimaging as additional tools contributed to a better understanding of the underlying mechanisms and how these develop across adolescence. The findings in this thesis converge to early-to-mid adolescence as a key developmental period for developing well-adjusted social behaviors, and especially in the cooperative domain there are pronounced improvements. These studies make an important contribution to the literature on social development and learning, and may eventually contribute to interventions targeted at promoting well-adjusted behavior in typically developing adolescents, as well as youth with maladaptive social tendencies. Show less
This thesis describes the importance of being able to control the selectivity of potential drug candidates. It explains how computational models are employed to predict and rationalize compound... Show moreThis thesis describes the importance of being able to control the selectivity of potential drug candidates. It explains how computational models are employed to predict and rationalize compound-protein binding (affinity) and therewith, selectivity of compounds. Moreover, it shows that selectivity can purposely be tuned to target either a single protein or an entire panel of proteins. The challenges of selectivity modeling are addressed based on case studies in the sodium-dependent glucose co-transporters, G protein-coupled receptors, and kinases. Show less
Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated... Show moreQuantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid (i.e. lookup table) as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects (T-1, T-2), proton density (PD), receiver phase ( phi(0)), transmit field inhomogeneity (B-1(+)), and slice profile. Parametermapswere comparedwith those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension (T-1, T-2, B-1(+)) by - on average - an order of magnitude. In effect, the applied dictionary was reduced from 1.47GB to 464KB. Furthermore, the proposed method was equally robust against undersampling artifacts as dictionarymatching. Dictionary fittingwith B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi- parametric mapping. Show less
Background Psychopathy has repeatedly been linked to disturbed associative learning from aversive events (i.e., threat conditioning). Optimal threat conditioning requires the generation of internal... Show moreBackground Psychopathy has repeatedly been linked to disturbed associative learning from aversive events (i.e., threat conditioning). Optimal threat conditioning requires the generation of internal representations of stimulus–outcome contingencies and the rate with which these may change. Because mental representations are imperfect, there will always be uncertainty about the accuracy of representations in the brain (i.e., representational uncertainty). However, it remains unclear 1) to what extent threat conditioning is susceptible to different types of uncertainty in representations about contingencies during the acquisition phase and 2) how representational uncertainty relates to psychopathic features. Methods A computational model was applied to functional neuroimaging data to estimate uncertainty in representations of contingencies (CoUn) and the rate of change of contingencies (RUn), respectively, from brain activation during the acquisition phase of threat conditioning in 132 adolescents at risk of developing antisocial personality profiles. Next, the associations between these two types of representational uncertainty and psychopathy-related dimensions were examined. Results The left and right amygdala activations were associated with CoUn, while the bilateral insula and the right amygdala were associated with RUn. Different patterns of relationships were found between psychopathic features and each type of uncertainty. Callous-unemotional traits and impulsive-irresponsible traits uniquely predicted increased CoUn, while only impulsive-irresponsible traits predicted increased RUn. Conclusions The findings suggest that 1) the insula and amygdala differ in how these regions are affected by different types of representational uncertainty during threat conditioning and 2) CoUn and RUn have different patterns of relationships with psychopathy-related dimensions. Show less
In this thesis computational modeling is used to help unravel the mechanisms of key steps in angiogenesis, the formation of new capillaries from existing blood vessels. The first step in... Show moreIn this thesis computational modeling is used to help unravel the mechanisms of key steps in angiogenesis, the formation of new capillaries from existing blood vessels. The first step in angiogenesis is the invasion of new branches into the surrounding tissue by degradation of extracellular matrix proteins, e.g. fibrin. A first model describes how invading sprouts use the so called plasminogen system, which dissolves fibrin matrices. A next model asks how endothelial cells can dynamically switch position during angiogenesis. Based on experimental observations, several authors suggest that dynamic cell shuffling is under strict, genetic control. Our simulations show, however, that shuffling can emerge as a side effect of sprouting. Once a sprout is formed, it needs to hollow to allow blood flow. The mechanisms responsible for this hollowing, or lumen formation, are debated: vacuoles may punch a hole through the cell, or cells might repulse one another. In our simulations, both these hypotheses can work synergistically in lumen formation, suggesting that both hypotheses might work together. In a final chapter, we introduce a workflow to simultaneously test the impact of changes in the value of multiple parameters on the outcome of the type of models used in this thesis. Show less
Streptomycetes are Gram-positive multicellular soil-dwelling bacteria which are commercially used as natural producers of antibiotics, anticancer agents and immunosuppressants, as well as many... Show moreStreptomycetes are Gram-positive multicellular soil-dwelling bacteria which are commercially used as natural producers of antibiotics, anticancer agents and immunosuppressants, as well as many industrial enzymes (Hopwood 2007). Similarly to fungi, they carry out a complex developmental life cycle, forming highly structured multicellular colonies composed of physiologically distinct hyphae (Miguelez et al. 2000). A major scientific challenge lies in understanding how growth parameters are controlled in response to nutritional conditions, and how this affects the efficiency of production and secretion of proteins and antibiotics. The work presented in this thesis therefore aims to arrive at a better understanding of Streptomyces morphogenesis and development, and how these processes link to productivity. Studies performed include the analysis of several novel coiled-coil proteins and their effect on morphogenesis an d division, as well as fluorescent live imaging to better understand and localize dynamic secretion systems. Cryo-correlative light and electron microscopy, and specifically tomography, was performed to image vegetative cell division and its effect on development within a pellet. Combining the multi-scale information gained, a structured morphological model was created to provide a framework for rational design of Streptomyces sp. and provide a test drive for the fermentation process. Show less
