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About 8,989 papers for “Boolean model”

Boolean Model

MELUCCI, MASSIMO · Research Padua Archive (University of Padua) · 2009

Cited by 0

Boolean apoptosis model.

Rebekka Schlatter (224010), Kathrin Schmich (224012), Ima Avalos Vizcarra (256077) et al. · Figshare · 2015

<p>The network map as it is also used for CNA is shown. The influence of the housekeeping node is depicted in green color. In addition stimuli and nodes which have been experimentally validated to prove the coherency of the model are indicated by yellow filled background (compare <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000595#pcbi-1000595-t002" target="_blank">Table 2</a>). Logical AND connections are represented by blue spheres. Activating arcs are represented by black arrows and inhibiting arcs by red lines with a bar.</p>

Cited by 0Open access

Boolean SK Model

Linda Albanese, Andrea Alessandrelli · arXiv (Cornell University) · 2024

For over half a century, statistical mechanics of spin glasses played as a paradigm to model and interpret disparate phenomena, ranging from quantitative biology to computer science. However, despite the extensive body of research in this area, there is still a notable lack of studies addressing the replacement of Ising spins with Boolean spins: as the latter play as bits in Machine Learning, this gap to fill is now mandatory. Purpose of this paper is to address this study by focusing on the mean field assumption, providing a comprehensive description of the results pertaining to these networks, referred to as the Boolean SK model due to their close relationship with the SK one. We provide a comprehensive framework for this model by employing Guerra interpolation: the thermodynamic limit, the replica symmetric and the broken replica free energy expressions are derived. Further, we inspect the onset of the replica symmetry breaking -- i.e., the de Almeida-Thouless line -- and derive Ghirlanda-Guerra fluctuations. All theoretical findings are corroborated by numerical inspections and both highlight crucial differences in the network's behavior if compared with the Ising SK model: as the temperature is lowered, no phase transitions are evidenced and the model continuously moves from a random (ergodic) behavior to a disordered (glassy) phase.

Cited by 1Open access

Boolean model for melanogenesis

Pooja Dnyane, Chetan Gadgil · Physical Biology · 2020

Melanogenesis is a highly regulated process through which the pigment melanin is produced in skin cells. Irregularities in the molecular events that govern the process of skin pigmentation can cause disorders like vitiligo. In order to understand the biology of disease progression, it is important to have an in depth understanding of intracellular events. Mathematical models provide an integrated view of intracellular signalling. There are very few models to date that incorporate intracellular processes relevant to melanogenesis and only one to our knowledge that simulates the dynamics of response to varying levels of input. Here, we report the formulation of the largest Boolean model (265 nodes) for melanogenesis to date. The model was built on the basis of a detailed interaction network graph published by Raghunath et al. Through additional manual curation of the reported interactions, we converted the graph into a set of Boolean rules, following the procedure of the first Boolean model (62 nodes) for melanogenesis published by Lee et al. Simulations show that the predicted response to varying UV levels for most of the nodes is similar to the predictions of the existing model. The greater complexity allows investigation of the sensitivity of melanin to additional nodes. We carried out perturbation analysis of the network through node deletion and constitutive activation to identify sensitivity of outcomes, and compared the nodes identified as sensitive to previous reports.

Cited by 3

Boolean network model predictions.

Thomas H. Hraha (625798), Matthew J. Westacott (625799), Marina Pozzoli (625800) et al. · Figshare · 2015

<p>A) Schematic representation of the network model with limited connectivity. Note larger connected clusters have a higher probability of containing inexcitable cells. B) Example false-color maps displaying probability of activity, generated from a simulated network with <i>p</i> = 0.30 at P<sub>exc</sub> = 95% (top) and P<sub>exc</sub> = 60% (bottom). Note substantially increased likelihood of activity with the higher P<sub>exc</sub>. Further description for how this was generated can be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003819#pcbi.1003819.s001" target="_blank">figure S1</a>. C) Boolean network model predictions for the mean percent active cells as a function of proportion of excitable cells (P<sub>exc</sub>) for varying coupling probabilities <i>p</i>, with a threshold fraction of inexcitable cells <i>Sp</i> = 0.15. D) Boolean network model predictions for the mean percent active cells as a function of coupling probability <i>p</i>, for varying proportion of excitable cells P<sub>exc</sub>, where <i>Sp</i> = 0.15.</p>

Cited by 0Open access

Validation of Boolean model extension.

Barbara Schnitzer (6246920), Linnea Österberg (6246923), Iro Skopa (13015209) et al. · Figshare · 2022

<p>Boolean model states when turning on (0 → 1) and off (1 → 0) hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) depending on glucose and nitrogen availability and the presence of thioredoxins (Trx1/2). H<sub>2</sub>O<sub>2</sub> eventually triggers active transcription factors Skn7 and Yap1, the latter only if Trx1/2 is not active. Active Trx1/2 inhibits Yap1 activation and activates Msn2/4.</p> <p>(EPS)</p>

Cited by 0Open access

The extended Boolean SIN model.

Anastasia Chasapi (780222), Paulina Wachowicz (780223), Anne Niknejad (780224) et al. · Figshare · 2015

<p>(A) The initial, prior knowledge network, manually re-constructed from the literature. Purple nodes represent proteins and complexes that take part in the regulation of the SIN, and pink nodes represent AND gates. Blue arrows indicate activation events and orange circles inhibition events. Other logical functions, such as AND, OR and NOT regulatory gates are also encoded in the model. SAC: spindle assembly checkpoint, APC: anaphase-promoting complex, PP: protein phosphatase. (B) The final, optimized model, which uses a 3-node representation of CDK activity. Nodes in green are used as switches, and they are turned on to represent different stages of the cell cycle: interphase, early mitosis and late mitosis. Pink nodes represent AND gates. In the case of the Cdc42p regulatory link towards Byr4p, the regulatory rule can be phrased as “NOT Cdc42p activates Byr4p”.</p>

Cited by 0Open access

Simulation of a Boolean model

Christian Lantuéjoul · HAL (Le Centre pour la Communication Scientifique Directe) · 2011

International audience

Cited by 0

Boolean network model and simulations.

Núria Domedel-Puig (193365), Pau Rué (193366), Antonio J. Pons (193367) et al. · Figshare · 2015

<p>(a) The Boolean network model under investigation. The network has 9 input nodes (blue squares at the top row), which we force with periodic or random signals (compare stress to the other inputs, respectively), as pictured schematically above each node, depending on the simulation performed. The network has 130 internal nodes, six of which are considered output nodes (red circles at the bottom row, while the rest of the internal nodes are represented by black circles). (b) Response of the network to a constant chatter level. Here we show a realization of the network dynamics (cell #1) in which the states of all input nodes randomly switch between 0 and 1 at a chatter level of . For clarity, we show the evolution of only one input node (stress, in blue) and one output node (p38, in red). The population average signal for the two nodes, calculated averaging data from R = 201 realizations, is shown at the bottom. (c) Response of the network to periodic stimulation of one input node. We show one network realization in which input node stress oscillates (period ), and the other input nodes are set to chatter level .</p>

Cited by 0Open access

Refined convergence for the Boolean model

Pierre Calka, Julien Michel, Katy Paroux · Advances in Applied Probability · 2009

In Michel and Paroux (2003) the authors proposed a new proof of a well-known convergence result for the scaled elementary connected vacant component in the high intensity Boolean model towards the Crofton cell of the Poisson hyperplane process (see, e.g. Hall (1985)). In this paper we investigate the second-order term in this convergence when the two-dimensional Boolean model and the Poisson line process are coupled on the same probability space. We consider the particular case where the grains are discs with random radii. A precise coupling between the Boolean model and the Poisson line process is first established. A result of directional convergence in distribution for the difference of the two sets involved is then derived. Eventually, we show the convergence of the process, measuring the difference between the two random sets, once rescaled, as a function of the direction.

Cited by 2

Refined convergence for the Boolean model

Calka, Pierre, Michel, Julien, Paroux, Katy · arXiv (Cornell University) · 2008

In a previous work, two of the authors proposed a new proof of a well known convergence result for the scaled elementary connected vacant component in the high intensity Boolean model towards the Crofton cell of the Poisson hyperplane process. In this paper, we consider the particular case of the two-dimensional Boolean model where the grains are discs with random radii. We investigate the second-order term in this convergence when the Boolean model and the Poisson line process are coupled on the same probability space. A precise coupling between the Boolean model and the Poisson line process is first established, a result of directional convergence in distribution for the difference of the two sets involved is derived as well.

Cited by 0Open access