Boolean models
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BACKGROUND: Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors-subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks. RESULTS: In this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method's applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus. CONCLUSIONS: The proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system's stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings.
- MeSH
- algoritmy MeSH
- aniliny MeSH
- benzamidy MeSH
- COVID-19 * MeSH
- genové regulační sítě * MeSH
- lidé MeSH
- modely genetické MeSH
- naftaleny MeSH
- SARS-CoV-2 MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
We propose a novel hybrid single-electron device for reprogrammable low-power logic operations, the magnetic single-electron transistor (MSET). The device consists of an aluminium single-electron transistor with a GaMnAs magnetic back-gate. Changing between different logic gate functions is realized by reorienting the magnetic moments of the magnetic layer, which induces a voltage shift on the Coulomb blockade oscillations of the MSET. We show that we can arbitrarily reprogram the function of the device from an n-type SET for in-plane magnetization of the GaMnAs layer to p-type SET for out-of-plane magnetization orientation. Moreover, we demonstrate a set of reprogrammable Boolean gates and its logical complement at the single device level. Finally, we propose two sets of reconfigurable binary gates using combinations of two MSETs in a pull-down network.
Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper.
- MeSH
- algoritmy MeSH
- diagnóza počítačová metody MeSH
- duktální karcinom prsu MeSH
- fraktály MeSH
- hodnocení rizik metody MeSH
- lidé MeSH
- nádory prsu diagnóza patologie MeSH
- patologie metody MeSH
- počítačová simulace MeSH
- stochastické procesy MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
Aim: The study goal consists in mapping the opportunities of use of Calista Roy adaptation model in current nursing. Methods: The submitted article was processed by survey study method. Contents analysis of studies published within scientific databases was used for data collection. The search took place through key words under use of Boolean operators „AND“ and „OR“. The following key words were chosen: adaptation; nursing; Calista Roy; Roy adaptation model. Results: Based on the analysis of the published works, it was found out that the Roy adaptation model is a broadly applicable model in the conditions of current nursing. The most published studies were focused on the model application within internal nursing. But other clinical fields, nursing research or education of non-medical health care professionals were not omitted either. Conclusion: It can be stated in summary that Calista Roy adaptation model is a flexible and broadly applicable framework that can be effectively used both for ill and for healthy individuals.
It has been known for discrete-time recurrent neural networks (NNs) that binary-state models using the Heaviside activation function (with Boolean outputs 0 or 1) are equivalent to finite automata (level 3 in the Chomsky hierarchy), while analog-state NNs with rational weights, employing the saturated-linear function (with real-number outputs in the interval [0,1]), are Turing complete (Chomsky level 0) even for three analog units. However, it is as yet unknown whether there exist subrecursive (i.e. sub-Turing) NN models which occur on Chomsky levels 1 or 2. In this paper, we provide such a model which is a binary-state NN extended with one extra analog unit (1ANN). We achieve a syntactic characterization of languages that are accepted online by 1ANNs in terms of so-called cut languages which are combined in a certain way by usual operations. We employ this characterization for proving that languages accepted by 1ANNs with rational weights are context-sensitive (Chomsky level 1) and we present explicit examples of such languages that are not context-free (i.e. are above Chomsky level 2). In addition, we formulate a sufficient condition when a 1ANN recognizes a regular language (Chomsky level 3) in terms of quasi-periodicity of parameters derived from its real weights, which is satisfied e.g. for rational weights provided that the inverse of the real self-loop weight of the analog unit is a Pisot number.
- MeSH
- jazyk (prostředek komunikace) * MeSH
- neuronové sítě * MeSH
- teoretické modely * MeSH
- Publikační typ
- časopisecké články MeSH
This article addresses the topic of extracting logical rules from data by means of artificial neural networks. The approach based on piecewise linear neural networks is revisited, which has already been used for the extraction of Boolean rules in the past, and it is shown that this approach can be important also for the extraction of fuzzy rules. Two important theoretical properties of piecewise-linear neural networks are proved, allowing an elaboration of the basic ideas of the approach into several variants of an algorithm for the extraction of Boolean rules. That algorithm has already been used in two real-world applications. Finally, a connection to the extraction of rules of the Łukasiewicz logic is established, relying on recent results about rational McNaughton functions. Based on one of the constructive proofs of the McNaughton theorem, an algorithm is formulated that in principle allows extracting a particular kind of formulas of the Łukasiewicz predicate logic from piecewise-linear neural networks trained with rational data.
- MeSH
- algoritmy MeSH
- ekologie MeSH
- financování organizované MeSH
- fuzzy logika MeSH
- interpretace statistických dat MeSH
- lidé MeSH
- lineární modely MeSH
- neuronové sítě MeSH
- rozpoznávání automatizované metody MeSH
- umělá inteligence MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- srovnávací studie MeSH
Supervised learning of perceptron networks is investigated as an optimization problem. It is shown that both the theoretical and the empirical error functionals achieve minima over sets of functions computable by networks with a given number n of perceptrons. Upper bounds on rates of convergence of these minima with n increasing are derived. The bounds depend on a certain regularity of training data expressed in terms of variational norms of functions interpolating the data (in the case of the empirical error) and the regression function (in the case of the expected error). Dependence of this type of regularity on dimensionality and on magnitudes of partial derivatives is investigated. Conditions on the data, which guarantee that a good approximation of global minima of error functionals can be achieved using networks with a limited complexity, are derived. The conditions are in terms of oscillatory behavior of the data measured by the product of a function of the number of variables d, which is decreasing exponentially fast, and the maximum of the magnitudes of the squares of the L(1)-norms of the iterated partial derivatives of the order d of the regression function or some function, which interpolates the sample of the data. The results are illustrated by examples of data with small and high regularity constructed using Boolean functions and the gaussian function.
... The Structure of Rugged Fitness Landscapes, 33 Fitness Landscapes in Sequence Space, 36 -- The NK Model ... ... Random Grammars: Models of Functional Integration and Transformation, 369 -- Jets and Autocatalytic Sets ... ... : Toward a New String Theory, 372 Infinite Boolean Networks and Random Grammars: Approaches to Studying ... ... Large-Scale Features of Cell Differentiation, 454 The Conceptual Framework: Cell Differentiation in Boolean ... ... Protein Patterns: A Bifurcation Sequence of Higher Harmonics on the Egg, 605 The Four Color Wheels Model ...
1st ed. 709 s. : il.
- Klíčová slova
- Biologie, Evoluce, Fylogeneze,
- MeSH
- biologická evoluce MeSH
- biologie MeSH
- fylogeneze MeSH
- molekulární evoluce MeSH
- původ života MeSH
Introduction: The aim of the study is to highlight the global nature of the professionalization process of lactation consulting and the significance of professional training in the field of lactation in accordance with the international certification program for lactation consultants (International Board Certified Lactation Consultant - IBCLC). In the Czech Republic, the current clinical practice in implementing internationally recognized educational standards in professional lactation counseling is lagging behind. Methodology: Document analysis was chosen for data collection. A systematic search of literary sources was conducted from 2010 to 2023. Within the search strategy, using Boolean operators "or" and "and," we defined key terms. We utilized databases such as PubMed Central, Springer, EBSCO, and Google Scholar. Results: The certified international program influences the increasing number of healthcare professionals supporting approaches and intervention strategies to meet the "Global BFHI Criteria." Consistent, not just isolated, information is provided in breastfeeding support, improving knowledge, attitudes, and practices related to breastfeeding. Women’s trust in healthcare professionals is strengthened. Conclusion: The integration of the internationally certified lactation consultant program into education and clinical practice professionalizes the assistance and support provided, increases women’s satisfaction with breastfeeding support, and enhances multidisciplinary cooperation in addressing lactation issues.
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