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Relevant bibliographies by topics / Complex systems, networks, dynamical models on networks, stochastic models

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Journal articles

Academic literature on the topic 'Complex systems, networks, dynamical models on networks, stochastic models'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Complex systems, networks, dynamical models on networks, stochastic models"

1

Rozum, Jordan C., Jorge Gómez Tejeda Zañudo, Xiao Gan, Dávid Deritei, and Réka Albert. "Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks." Science Advances 7, no. 29 (2021): eabf8124. http://dx.doi.org/10.1126/sciadv.abf8124.

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We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-stan

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Morrison, Megan, and Lai-Sang Young. "Chaotic heteroclinic networks as models of switching behavior in biological systems." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 12 (2022): 123102. http://dx.doi.org/10.1063/5.0122184.

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Key features of biological activity can often be captured by transitions between a finite number of semi-stable states that correspond to behaviors or decisions. We present here a broad class of dynamical systems that are ideal for modeling such activity. The models we propose are chaotic heteroclinic networks with nontrivial intersections of stable and unstable manifolds. Due to the sensitive dependence on initial conditions, transitions between states are seemingly random. Dwell times, exit distributions, and other transition statistics can be built into the model through geometric design an

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Safdari, Hadiseh, Martina Contisciani, and Caterina De Bacco. "Reciprocity, community detection, and link prediction in dynamic networks." Journal of Physics: Complexity 3, no. 1 (2022): 015010. http://dx.doi.org/10.1088/2632-072x/ac52e6.

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Abstract Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference methods have been generalized to the dynamic case with the aim to model dynamic interactions. Particular interest has been devoted to extend the stochastic block model and its variant, to capture community structure as the network changes in time. While these models assume that edge formation depends only on the community memberships, recent work for static networks show the importance to include additional param

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KNOPOFF, D. "ON A MATHEMATICAL THEORY OF COMPLEX SYSTEMS ON NETWORKS WITH APPLICATION TO OPINION FORMATION." Mathematical Models and Methods in Applied Sciences 24, no. 02 (2013): 405–26. http://dx.doi.org/10.1142/s0218202513400137.

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This paper presents a development of the so-called kinetic theory for active particles to the modeling of living, hence complex, systems localized in networks. The overall system is viewed as a network of interacting nodes, mathematical equations are required to describe the dynamics in each node and in the whole network. These interactions, which are nonlinearly additive, are modeled by evolutive stochastic games. The first conceptual part derives a general mathematical structure, to be regarded as a candidate towards the derivation of models, suitable to capture the main features of the said

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Jirsa, Viktor, and Hiba Sheheitli. "Entropy, free energy, symmetry and dynamics in the brain." Journal of Physics: Complexity 3, no. 1 (2022): 015007. http://dx.doi.org/10.1088/2632-072x/ac4bec.

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Abstract Neuroscience is home to concepts and theories with roots in a variety of domains including information theory, dynamical systems theory, and cognitive psychology. Not all of those can be coherently linked, some concepts are incommensurable, and domain-specific language poses an obstacle to integration. Still, conceptual integration is a form of understanding that provides intuition and consolidation, without which progress remains unguided. This paper is concerned with the integration of deterministic and stochastic processes within an information theoretic framework, linking informat

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Penfold, Christopher A., and David L. Wild. "How to infer gene networks from expression profiles, revisited." Interface Focus 1, no. 6 (2011): 857–70. http://dx.doi.org/10.1098/rsfs.2011.0053.

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Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance of these competing approaches have previously been assessed using a variety of in silico and in vivo dat

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Parham, Paul E., and Neil M. Ferguson. "Space and contact networks: capturing the locality of disease transmission." Journal of The Royal Society Interface 3, no. 9 (2005): 483–93. http://dx.doi.org/10.1098/rsif.2005.0105.

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While an arbitrary level of complexity may be included in simulations of spatial epidemics, computational intensity and analytical intractability mean that such models often lack transparency into the determinants of epidemiological dynamics. Although numerous approaches attempt to resolve this complexity–tractability trade-off, moment closure methods arguably offer the most promising and robust frameworks for capturing the role of the locality of contact processes on global disease dynamics. While a close analogy may be made between full stochastic spatial transmission models and dynamic netw

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Warne, David J., Ruth E. Baker, and Matthew J. Simpson. "Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art." Journal of The Royal Society Interface 16, no. 151 (2019): 20180943. http://dx.doi.org/10.1098/rsif.2018.0943.

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Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour

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Cardelli, Luca, Isabel Cristina Perez-Verona, Mirco Tribastone, Max Tschaikowski, Andrea Vandin, and Tabea Waizmann. "Exact maximal reduction of stochastic reaction networks by species lumping." Bioinformatics 37, no. 15 (2021): 2175–82. http://dx.doi.org/10.1093/bioinformatics/btab081.

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Abstrtact Motivation Stochastic reaction networks are a widespread model to describe biological systems where the presence of noise is relevant, such as in cell regulatory processes. Unfortunately, in all but simplest models the resulting discrete state-space representation hinders analytical tractability and makes numerical simulations expensive. Reduction methods can lower complexity by computing model projections that preserve dynamics of interest to the user. Results We present an exact lumping method for stochastic reaction networks with mass-action kinetics. It hinges on an equivalence r

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10

Bombieri, Nicola, Silvia Scaffeo, Antonio Mastrandrea, et al. "SystemC Implementation of Stochastic Petri Nets for Simulation and Parameterization of Biological Networks." ACM Transactions on Embedded Computing Systems 20, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3427091.

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Model development and simulation of biological networks is recognized as a key task in Systems Biology. Integrated with in vitro and in vivo experimental data, network simulation allows for the discovery of the dynamics that regulate biological systems. Stochastic Petri Nets (SPNs) have become a widespread and reference formalism to model metabolic networks thanks to their natural expressiveness to represent metabolites, reactions, molecule interactions, and simulation randomness due to system fluctuations and environmental noise. In the literature, starting from the network model and the comp

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