| 1. |
- Alander, Jarmo, et al.
(författare)
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Near infrared wavelength relevance detection of ultraviolet radiation-induced erythema
- 2008
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Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 16:3, s. 233-241
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Tidskriftsartikel (refereegranskat)abstract
- The acute effects of sun-bathing on the near-infrared absorption spectra of human skin were studied by exposing the shoulders of a male test subject to bright Finnish high summer mid-day sun. The spectra were measured before, immediately after and for several days after exposure. Four different spectral. processing and classification methods were applied to the data set to identify differences caused by exposure to the sun. The spectrophotometer and measuring procedure were found to cause some systematic errors, calling for further development, even though they could, to a large extent, be compensated for computationally. Spectral regions indicating ultraviolet radiation-induced erythema were Located and the degree of erythema could be predicted correctly but the signal is weak. This paper discusses promising wavelength selection methods to study the dermal effects of exposure to the sun, as well as difficulties and remedies of near infrared spectroscopic measurements of the skin.
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| 2. |
- Hellgren, Mikko, 1972-, et al.
(författare)
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Multi-level modelling of the parallell metabolism of ethanol and retinol, with implications for foetal alcohol syndrome
- 2008
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Ingår i: The 9th International Conference on Systems Biology (ICSB-2008) in Gothenburg (Sweden). - 9781615673322
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Konferensbidrag (refereegranskat)abstract
- Objective: Models of the human metabolism are important for understanding diseases and could serve as a powerful tool in the drug discovery process. The complexity of even a unicellular organism is tremendous and most researchers have therefore limited their modelling efforts to bacteria, or single intracellular pathways. We studied the parallel metabolism of ethanol and retinol in humans, because of its suggested physiological importance for the development of foetal alcohol syndrome. Large ethanol intake will inhibit the conversion of retinol into retinoic acid, which is a crucial transcription factor during embryonic development. In this study the objective was to construct a quantitative model that connects phenotype observations at a population, organic and intracellular level with differences in genotype and ethanol metabolism, for further prediction of the influence on the foetus. Results: We constructed a multiple compartments model, which included a detailed desccription of the ethanol and retinol metabolism in hepatic cells for different genotypes. The model has been validated using published time-series measurements of ethanol, acetaldehyde and acetate concentrations in the blood. This model correctly accounts for differences in geno- and phenotype observed within the human population. Furthermore, the model shows that the retinol metabolism is decreased by ethanol ingestion, both via a reduced intracellular NAD+ concentration, and by an inhibition of alcohol and aldehyde dehydrogenases. Conclusions: We considered the problem of multi-level modelling with a human model for the ethanol and retinol metabolism in different compartments. This links intracellular mechanisms to macroscopic observations. The model explained the connection between geno- and phenotype differences observed at a population level. This model also shows a plausible relationship between ethanol and retinol metabolism for e.g. foetal alcohol syndrome.
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| 3. |
- Huang, Hui-Ling, et al.
(författare)
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ThermalProGAN : a sequence-based thermally stable protein generator trained using unpaired data
- 2023
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Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific. - 0219-7200 .- 1757-6334. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: The synthesis of proteins with novel desired properties is challenging but sought after by the industry and academia. The dominating approach is based on trial-and-error inducing point mutations, assisted by structural information or predictive models built with paired data that are difficult to collect. This study proposes a sequence-based unpaired-sample of novel protein inventor (SUNI) to build ThermalProGAN for generating thermally stable proteins based on sequence information.Results: The ThermalProGAN can strongly mutate the input sequence with a median number of 32 residues. A known normal protein, 1RG0, was used to generate a thermally stable form by mutating 51 residues. After superimposing the two structures, high similarity is shown, indicating that the basic function would be conserved. Eighty four molecular dynamics simulation results of 1RG0 and the COVID-19 vaccine candidates with a total simulation time of 840ns indicate that the thermal stability increased.Conclusion: This proof of concept demonstrated that transfer of a desired protein property from one set of proteins is feasible.
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| 4. |
- Ji, Zhong-Hai, et al.
(författare)
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High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes
- 2021
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Ingår i: Nano Reseach. - : Tsinghua University Press. - 1998-0124 .- 1998-0000. ; 14, s. 4610-4615
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Tidskriftsartikel (refereegranskat)abstract
- It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity (IG/ID) was extracted automatically and mapped to the growth parameters to build a database. 1,280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs. [Figure not available: see fulltext.].
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| 5. |
- Koljonen, Janne, et al.
(författare)
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A review of genetic algorithms in near infrared spectroscopy and chemometrics : past and future
- 2008
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Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Global optimisation and search problems are abundant in science and engineering, including spectroscopy and its applications. Therefore, it is hardly surprising that general optimisation and search methods such as genetic algorithms (GAs) have also found applications in the area of near infrared INIRI spectroscopy. A brief introduction to genetic algorithms, their objectives and applications in NIR spectroscopy, as well as in chemometrics, is given. The most popular application for GAs in NIR spectroscopy is wavelength, or more generally speaking, variable selection. GAs are both frequently used and convenient in multi-criteria optimisation; for example, selection of pre-processing methods, wavelength inclusion, and selection of Latent variables can be optimised simultaneously. Wavelet transform has recently been applied to pre-processing of NIR data. In particular, hybrid methods of wavelets and genetic algorithms have in a number of research papers been applied to pre-processing, wavelength selection and regression with good success. In all calibrations and, in particular, when optimising, it is essential to validate the model and to avoid over-fitting. GAs have a Large potential when addressing these two major problems and we believe that many future applications will emerge. To conclude, optimisation gives good opportunities to simultaneously develop an accurate calibration model and to regulate model complexity and prediction ability within a considered validation framework.
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| 6. |
- Magnusson, Rasmus, 1992-, et al.
(författare)
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LASSIM-A network inference toolbox for genome-wide mechanistic modeling
- 2017
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 13:6, s. Article no. e1005608 -
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Tidskriftsartikel (refereegranskat)abstract
- Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.
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| 7. |
- Meher, Jagmohan, et al.
(författare)
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Acquisition and synchronisation of cardiography signals from a clinical patient monitor with facial video recordings
- 2024
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Ingår i: International Conference on Biomedical and Health Informatics 2022. - : Springer Nature. - 9783031592157 - 9783031592164 ; , s. 254-261
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Konferensbidrag (refereegranskat)abstract
- A far too frequent practical challenge in clinical informatics research and method development for acquiring vital signs is the extraction and synchronisation of signals from proprietary devices for the clinical monitoring of patients. In an ongoing study evaluating methods for video-based remote photoplethysmography (rPPG), we needed to extract ground truth values of electrocardiogram (ECG) and pulse oximetry (SpO2) signals from the Philips vitals monitor while recording the facial video of the subject, simultaneously. This ground truth data will be used to train the model that will perform rPPG. Various software can extract data from the Philips vitals monitor with features like data acquisition, parsing, and visualisation, but they lack synchronisation with the facial video. Therefore, we developed the Patient Monitor Data Extractor (PMDE), which collects data from the Philips IntelliVue monitors following the Data export interface programming guide provided by Philips. We set up a DHCP server on a Windows 7 computer with a webcam and interfaced with the monitor through LAN with UDP/IP. We used C++ and Windows Sockets API to develop our software and communicate over UDP. For synchronisation with the video cameras, we turned off the light in the room and used this sudden brightness drop as a trigger. The timestamp of the monitor was recorded when the webcam detected the trigger. The PMDE software records ECG at 500 Hz and SpO2 at 125 Hz with a synchronisation error of less than two sampling periods, which is about 40 ms for a 50 fps video. We conclude that PMDE is uniquely suited for recording data for rPPG evaluation because of its synchronisation feature. We have used PMDE to collect a dataset of facial videos with ground truth ECG and SpO2 signals. We intend to make PMDE available as open source to save other researchers time.
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| 8. |
- Morgan, Daniel, 1988-, et al.
(författare)
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A generalized framework for controlling FDR in gene regulatory network inference
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 35:6, s. 1026-1032
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Inference of gene regulatory networks (GRNs) from perturbation data can give detailed mechanistic insights of a biological system. Many inference methods exist, but the resulting GRN is generally sensitive to the choice of method-specific parameters. Even though the inferred GRN is optimal given the parameters, many links may be wrong or missing if the data is not informative. To make GRN inference reliable, a method is needed to estimate the support of each predicted link as the method parameters are varied.Results: To achieve this we have developed a method called nested bootstrapping, which applies a bootstrapping protocol to GRN inference, and by repeated bootstrap runs assesses the stability of the estimated support values. To translate bootstrap support values to false discovery rates we run the same pipeline with shuffled data as input. This provides a general method to control the false discovery rate of GRN inference that can be applied to any setting of inference parameters, noise level, or data properties. We evaluated nested bootstrapping on a simulated dataset spanning a range of such properties, using the LASSO, Least Squares, RNI, GENIE3 and CLR inference methods. An improved inference accuracy was observed in almost all situations. Nested bootstrapping was incorporated into the GeneSPIDER package, which was also used for generating the simulated networks and data, as well as running and analyzing the inferences.
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| 9. |
- Morgan, Daniel, et al.
(författare)
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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
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| 10. |
- Nordling, Torbjörn E. M., et al.
(författare)
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Deduction of intracellular sub-systems from a topological description of the network
- 2007
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Ingår i: Molecular BioSystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 3:8, s. 523-529
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Tidskriftsartikel (refereegranskat)abstract
- Non-linear behaviour of biochemical networks, such as intracellular gene, protein or metabolic networks, is commonly represented using graphs of the underlying topology. Nodes represent abundance of molecules and edges interactions between pairs of molecules. These graphs are linear and thus based on an implicit linearization of the kinetic reactions in one or several dynamic modes of the total system. It is common to use data from different sources - experiments conducted under different conditions or even on different species - meaning that the graph will be a superposition of linearizations made in many different modes. The mixing of different modes makes it hard to identify functional modules, that is subsystems that carry out a specific biological function, since the graph will contain many interactions that do not naturally occur at the same time. The ability to establish a boundary between the sub- system and its environment is critical in the definition of a module, contrary to a motif in which only internal interactions count. Identification of functional modules should therefore be done on graphs depicting the mode in which their function is carried out, i.e. graphs that only contain edges representing interactions active in the specific mode. In general, when an interaction between two molecules is established, one should always state the mode of the system in which it is active.
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