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| 2. |
- Mamontov, Eugen, 1955
(författare)
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Ordinary differential equation system for population of individuals and the corresponding probabilistic model
- 2008
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Ingår i: Mathl. Computer Modelling. - : Elsevier BV. - 0895-7177.
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Tidskriftsartikel (refereegranskat)abstract
- The key model for particle populations in statistical mechanics is the Bogolyubov–Born– Green–Kirkwood–Yvon (BBGKY) equation chain. It is derived mainly from the Hamilton ordinary differential equation (ODE) system for the vectors of the particle states in the particle position-momentum phase space. Many problems beyond physics or chemistry, for instance, in the living-matter sciences (biology, medicine, ecology, and scoiology) make it necessary to extend the notion of a particle to an individual, or active particle. This challenge is met by the generalized kinetic theory. It implements the extension by extending the phase space from the space of the position-momentum vectors to more rich spaces formed by the state vectors with the entries which need not be limited to the entries of the position and momentum: they include other scalar variables (e.g., those associated with modelling homeorhesis or other features inherent to the individuals). One can assume that the dynamics of the state vector in the extended space, i.e. the states of the individuals (rather than common particles) is also described by an ODE system. The latter, however, need not be the Hamilton one. The question is how one can derive the analogue of the BBGKY paradigm for the new settings. The present work proposes an answer to this question. It applies a very limited number of carefully selected tools of probability theory and common statistical mechanics. It in particular uses the well-known feature that the maximum number of the individuals which can mutually interact simultaneously is bounded by a fixed value of a few units. The present approach results in the finite system of equations for the reduced many-individual distribution functions thereby eliminating the so-called closure problem inevitable in the BBGKY theory. The thermodynamic-limit assumption is not needed either. The system includes consistently derived terms of all of the basic types known in kinetic theory, in particular, both the “mean-field” and scattering-integral terms, and admits the kinetic equation of the form allowing a direct chemical-reaction reading. The present approach can deal with Hamilton’s equation systems which are nonmonogenic and not treated in statistical mechanics. The proposed modelling suggests the basis of the generalized kinetic theory and may serve as the stochastic mechanics of population of individuals.
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| 3. |
- Johansson, Simon, 1994, et al.
(författare)
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Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
- 2022
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Ingår i: Molecular Informatics. - : Wiley. - 1868-1743 .- 1868-1751. ; 41:12
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Tidskriftsartikel (refereegranskat)abstract
- Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit how much data can be obtained from experiments. This suggests the use of schemes for data collection such as active learning, which identifies the data points of highest impact for model accuracy, and which has been used in recent studies with success. However, little has been done to explore the robustness of the methods predicting reaction yield when used together with active learning to reduce the amount of experimental data needed for training. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined AUROC faster than random sampling on both datasets. Analysis of feature importance of the trained machine learning models suggests active learning had a larger influence on the model accuracy when only a few features were important for the model prediction.
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| 4. |
- Lam, S., et al.
(författare)
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Addressing the heterogeneity in liver diseases using biological networks
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 22:2, s. 1751-1766
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Tidskriftsartikel (refereegranskat)abstract
- The abnormalities in human metabolism have been implicated in the progression of several complex human diseases, including certain cancers. Hence, deciphering the underlying molecular mechanisms associated with metabolic reprogramming in a disease state can greatly assist in elucidating the disease aetiology. An invaluable tool for establishing connections between global metabolic reprogramming and disease development is the genome-scale metabolic model (GEM). Here, we review recent work on the reconstruction of cell/tissue-type and cancer-specific GEMs and their use in identifying metabolic changes occurring in response to liver disease development, stratification of the heterogeneous disease population and discovery of novel drug targets and biomarkers. We also discuss how GEMs can be integrated with other biological networks for generating more comprehensive cell/tissue models. In addition, we review the various biological network analyses that have been employed for the development of efficient treatment strategies. Finally, we present three case studies in which independent studies converged on conclusions underlying liver disease.
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| 6. |
- Mamontov, Eugen, 1955, et al.
(författare)
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What stochastic mechanics are relevant to the study of living systems?
- 2005
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Ingår i: Proceedings of the Latvian Academy of Sciences. Section B: Natural, Exact and Applied Sciences. - Riga, Latvia : Latvian Academy of Sciences. - 1407-009X. ; 59:6, s. 255-262
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Tidskriftsartikel (refereegranskat)abstract
- Biologists have identified many features of living systems which cannot be studied by application of fundamental statistical mechanics (FSM). The present work focuses on some of these features. By discussing all the basic approaches of FSM, the work formulates the extension of the kinetic-theory paradigm (based on the reduced one-particle distribution function) that possesses all the considered properties of the living systems. This extension appears to be a model within the generalized-kinetic theory developed by N. Bellomo and his co-authors. In connection with this model, the work also stresses some other features necessary for making the model relevant to living systems. An example is discussed, which is a generalized kinetic equation coupled with the probability-density equation which represents the varying component content of a living system. The work also suggests directions for future research.
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| 7. |
- Grüning, Björn, et al.
(författare)
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Bioconda: A sustainable and comprehensive software distribution for the life sciences
- 2017
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
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| 8. |
- Brunius, Carl, 1974, et al.
(författare)
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Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059 .- 1367-4811. ; 33:22, s. 3567-3574
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Tidskriftsartikel (refereegranskat)abstract
- Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples. This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1-36 h pre-centrifugation delay time at 4 A degrees C and 22 A degrees C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 A degrees C. Moreover, pre-centrifugation delay at 4 A degrees C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 A degrees C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.
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| 9. |
- Pettersson, Fredrik, 1974-
(författare)
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A multivariate approach to computational molecular biology
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis describes the application of multivariate methods in analyses of genomic DNA sequences, gene expression and protein synthesis, which represent each of the steps in the central dogma of biology. The recent finalisation of large sequencing projects has given us a definable core of genetic data and large-scale methods for the dynamic quantification of gene expression and protein synthesis. However, in order to gain meaningful knowledge from such data, appropriate data analysis methods must be applied. The multivariate projection methods, principal component analysis (PCA) and partial least squares projection to latent structures (PLS), were used for clustering and multivariate calibration of data. By combining results from these and other statistical methods with interactive visualisation, valuable information was extracted and further interpreted. We analysed genomic sequences by combining multivariate statistics with cytological observations and full genome annotations. All oligomers of di- (16), tri- (64), tetra- (256), penta- (1024) and hexa-mers (4096) of DNA were separately counted and normalised and their distributions in the chromosomes of three Drosophila genomes were studied by using PCA. Using this strategy sequence signatures responsible for the differentiation of chromosomal elements were identified and related to previously defined biological features. We also developed a tool, which has been made publicly available, to interactively analyse single nucleotide polymorphism data and to visualise annotations and linkage disequilibrium. PLS was used to investigate the relationships between weather factors and gene expression in field-grown aspen leaves. By interpreting PLS models it was possible to predict if genes were mainly environmentally or developmentally regulated. Based on a PCA model calculated from seasonal gene expression profiles, different phases of the growing season were identified as different clusters. In addition, a publicly available dataset with gene expression values for 7070 genes was analysed by PLS to classify tumour types. All samples in a training set and an external test set were correctly classified. For the interpretation of these results a method was applied to obtain a cut-off value for deciding which genes could be of interest for further studies. Potential biomarkers for the efficacy of radiation treatment of brain tumours were identified by combining quantification of protein profiles by SELDI-MS-TOF with multivariate analysis using PCA and PLS. We were also able to differentiate brain tumours from normal brain tissue based on protein profiles, and observed that radiation treatment slows down the development of tumours at a molecular level. By applying a multivariate approach for the analysis of biological data information was extracted that would be impossible or very difficult to acquire with traditional methods. The next step in a systems biology approach will be to perform a combined analysis in order to elucidate how the different levels of information are linked together to form a regulatory network.
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| 10. |
- Lang, Victoria Ashley, 1994, et al.
(författare)
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Mathematical and Computational Models for Pain: A Systematic Review
- 2021
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Ingår i: Pain Medicine. - : Oxford University Press (OUP). - 1526-2375 .- 1526-4637. ; 22:12, s. 2806-2817
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Tidskriftsartikel (refereegranskat)abstract
- Objective. There is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain. Methods. The databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant. Results. Most of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment. Conclusions. Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment.
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