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Eduard Dumitrescu, Kenneth Wallace, Silvana Andreescu
The emergence of nanomaterials in industrial processing and consumer products has generated an increased presence of nano-enabled products in the environment and now pose an increased risk of exposure to living organisms. However, assessing the risks of nanomaterials is a challenging task because of a large variety and great variability in their properties. Here, we describe a methodology for assessing toxicity and evaluate potential risks posed by nanomaterials using zebrafish embryos as a model organism. Zebrafish are a well-established organism that has a number of advantages over other biological models...
2019: Methods in Molecular Biology
Sameera Nallanthighal, Ramune Reliene
Owing to new and unique properties, engineered nanoparticles (NPs) likely pose different risks than their constituent chemicals and these risks need to be understood. In particular, it is important to assess genotoxicity, since genotoxicity is a precursor to carcinogenicity. Here we describe a battery of tests for the assessment of genotoxicity of NPs in vivo in mice. Mice can be exposed to NPs for various exposure durations and by any route of exposure, provided NPs are absorbed into the systemic blood circulation...
2019: Methods in Molecular Biology
Ali Kermanizadeh
The liver is the principal detoxification center of the body, removing xenobiotics and waste products which could potentially include some nanomaterials (NM). With the ever increasing public and occupational exposure associated with accumulative production of nanomaterials, there is an urgent need to consider the possibility of harmful health consequences of engineered NM exposure. It is understood that following exposure via inhalation, ingestion, or direct intravenous injection a fraction of NMs reach the liver...
2019: Methods in Molecular Biology
Maryam Lotfi Shahreza, Nasser Ghadiri, James R Green
Using existing drugs for diseases which are not developed for their treating (drug repositioning) provides a new approach to developing drugs at a lower cost, faster, and more secured. We proposed a method for drug repositioning which can predict simple and complex relationships between drugs, drug targets, and diseases. Since biological networks typically present a suitable model for relationships between different biological concepts, our primary approach is to analyze graphs and complex networks in the study of drugs and their therapeutic effects...
2019: Methods in Molecular Biology
Zhaobin Kuang, Yujia Bao, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, Rebecca Willett, David Page
We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In the same time, numeric physical measurements (e.g., fasting blood glucose level) are also recorded. Baseline regularization uses statistical relationships between the occurrences of prescriptions of some particular drugs and the increase or the decrease in the values of some particular numeric physical measurements to identify potential repurposing opportunities...
2019: Methods in Molecular Biology
Kai Zhao, Hon-Cheong So
The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML) algorithms to learn patterns in biological data related to drugs and then link them up to the potential of treating specific diseases. Here we give an overview of the general principles and different types of ML algorithms, as well as common approaches to evaluating predictive performances, with reference to the application of ML algorithms to predict repurposing opportunities using drug expression data as features...
2019: Methods in Molecular Biology
Xiao Ji, Johannes M Freudenberg, Pankaj Agarwal
Computational prediction of the clinical success or failure of a potential drug target for therapeutic use is a challenging problem. Novel network propagation algorithms that integrate heterogeneous biological networks are proving useful for drug target identification and prioritization. These approaches typically utilize a network describing relationships between targets, a method to disseminate the relevant information through the network, and a method to elucidate new associations between targets and diseases...
2019: Methods in Molecular Biology
Mihai Udrescu, Lucreţia Udrescu
Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network visualization and analysis) to represent a drug-drug interaction network with drug interaction information from DrugBank 4.1. Both modularity class- and force-directed layout ForceAtlas2 are employed to generate drug clusters which correspond to nine specific drug properties...
2019: Methods in Molecular Biology
Si Zheng, Hetong Ma, Jiayang Wang, Jiao Li
We present a bipartite graph-based approach to calculate drug pairwise similarity for identifying potential new indications of approved drugs. Both chemical and molecular features were used in drug similarity calculation. In this paper, we first extracted drug chemical structures and drug-target interactions. Second, we computed chemical structure similarity and drug- target profile similarity. Further, we constructed a bipartite graph model with known relationships between drugs and their target proteins. Finally, we weighted summing drug structure similarity with target profile similarity to derive drug pairwise similarity, so that we can predict potential indication of a drug from its similar drugs...
2019: Methods in Molecular Biology
Salvatore Alaimo, Alfredo Pulvirenti
The wealth of knowledge and omic data available in drug research allowed the rising of several computational methods in drug discovery field yielding a novel and exciting application called drug repositioning. Several computational methods try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter we present an in-depth review of data resources and computational models for drug repositioning.
2019: Methods in Molecular Biology
Asghar Talebian, Kim Robinson-Brookes, Susan O Meakin
Brain-derived neurotrophic factor (BDNF) facilitates multiple aspects of neuronal differentiation and cellular physiology by activating the high-affinity receptor tyrosine kinase, TrkB. While it is known that both BDNF and TrkB modulate cellular processes involved in learning and memory, exactly how TrkB cross-talks and modulates signaling downstream of excitatory ionotropic receptors, such as the NMDA receptor (NMDAR), are not well understood. A model that we have investigated involves the signaling molecule RasGrf1, a guanine nucleotide exchange factor for both Ras and Rac...
December 13, 2018: Journal of Molecular Neuroscience: MN
Miriam Scheld, Athanassios Fragoulis, Stella Nyamoya, Adib Zendedel, Bernd Denecke, Barbara Krauspe, Nico Teske, Markus Kipp, Cordian Beyer, Tim Clarner
Widespread inflammatory lesions within the central nervous system grey and white matter are major hallmarks of multiple sclerosis. The development of full-blown demyelinating multiple sclerosis lesions might be preceded by preactive lesions which are characterized by focal microglia activation in close spatial relation to apoptotic oligodendrocytes. In this study, we investigated the expression of signaling molecules of oligodendrocytes that might be involved in initial microglia activation during preactive lesion formation...
December 13, 2018: Journal of Molecular Neuroscience: MN
Mika E Mononen, Mimmi K Liukkonen, Rami K Korhonen
Currently, there are no clinically available tools or applications which could predict osteoarthritis development. Some computational models have been presented to simulate cartilage degeneration, but they are not clinically feasible due to time required to build subject-specific knee models. Therefore, the objective of this study was to develop a template-based modeling method for rapid prediction of knee joint cartilage degeneration. Knee joint models for 21 subjects were constructed with two different template approaches (multiple templates and one template) based on the MRI data...
December 13, 2018: Annals of Biomedical Engineering
Fabian Fröhlich, Carolin Loos, Jan Hasenauer
Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box...
2019: Methods in Molecular Biology
Olivia Angelin-Bonnet, Patrick J Biggs, Matthieu Vignes
Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models...
2019: Methods in Molecular Biology
Pau Erola, Eric Bonnet, Tom Michoel
Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions...
2019: Methods in Molecular Biology
Christopher A Penfold, Iulia Gherman, Anastasiya Sybirna, David L Wild
Gaussian process dynamical systems (GPDS) represent Bayesian nonparametric approaches to inference of nonlinear dynamical systems, and provide a principled framework for the learning of biological networks from multiple perturbed time series measurements of gene or protein expression. Such approaches are able to capture the full richness of complex ODE models, and can be scaled for inference in moderately large systems containing hundreds of genes. Related hierarchical approaches allow for inference from multiple datasets in which the underlying generative networks are assumed to have been rewired, either by context-dependent changes in network structure, evolutionary processes, or synthetic manipulation...
2019: Methods in Molecular Biology
Vân Anh Huynh-Thu, Guido Sanguinetti
Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability...
2019: Methods in Molecular Biology
Julien Chiquet, Guillem Rigaill, Martina Sundqvist
This chapter addresses the problem of reconstructing regulatory networks in molecular biology by integrating multiple sources of data. We consider data sets measured from diverse technologies all related to the same set of variables and individuals. This situation is becoming more and more common in molecular biology, for instance, when both proteomic and transcriptomic data related to the same set of "genes" are available on a given cohort of patients.To infer a consensus network that integrates both proteomic and transcriptomic data, we introduce a multivariate extension of Gaussian graphical models (GGM), which we refer to as multiattribute GGM...
2019: Methods in Molecular Biology
Alex White, Matthieu Vignes
Biological networks are a very convenient modeling and visualization tool to discover knowledge from modern high-throughput genomics and post-genomics data sets. Indeed, biological entities are not isolated but are components of complex multilevel systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression...
2019: Methods in Molecular Biology
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