Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics

Bianconi, Ginestra; Arenas, Àlex; Biamonte, Jacob; Carr, Lincoln D.; Kahng, B.; Kertész, János; Kurths, Jürgen; Lü, Linyuan; Masoller, Cristina; Motter, Adilson E.; Perc, Matjaž; Radicchi, Filippo; Ramaswamy, Ramakrishna; Rodrigues, Francisco A.; Sales‐Pardo, Marta; Miguel, M. San; Thurner, Stefan; Yasseri, Taha

doi:10.1088/2632-072x/ac7f75

Cited by 36 publications

(28 citation statements)

References 56 publications

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“…While data availability percolates those three groups of answers posing new fundamental questions, opening new fields of research and having a large social impact, the societal impact of present research is an important challenge in itself. Similar, complementary or different ideas about these challenges by different representative groups of researchers in Complex Systems have been collected elsewhere (San Miguel et al, 2012;Thurner, 2017;Bianconi et al, 2023).…”

Section: Introductionmentioning

confidence: 76%

Frontiers in Complex Systems

Miguel

2023

Front. Complex Syst.

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Section: Introductionmentioning

confidence: 76%

Frontiers in Complex Systems

Miguel

2023

Front. Complex Syst.

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“…Usually, the dynamics of such a system can be described by a set of nonlinear ordinary differential equations: where x = ( x 1 ,…, x n ) T represents the state vector of the network, R = ( R 1 ,… R n ) T is the nonlinear regulatory function, F = F 0 + η F ( t ) is the noisy upstream signal with a fluctuatio η F , F 0 = ⟨ F ⟩ is the average signal, ⟨…⟩ denotes temporal average, and η x = ( η x 1, …, η xn ) T is the downstream fluctuation originating from the stochastic reaction of the network. As a first step toward understanding the noise properties following the spirit of perturbation theory [28,31], we assume that the signaling system operates at a steady state and consider its dynamics near a stable fixed point x 0 [28]. Approximately, Eq.…”

Section: Resultsmentioning

confidence: 99%

A trade-off in controlling upstream and downstream noise in signaling networks

Kong,

Luo,

Liu

2023

Preprint

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Signal transduction, underpinning the function of a variety of biological systems, is inevitably affected by fluctuations. It remains intriguing how the timescale of a signaling network relates to its capability of noise control, specifically, whether long timescale can average out fluctuation or accumulate fluctuation. Here, we consider two noise components of the signaling system: the upstream noise from the fluctuation of the input signal and the downstream noise from the stochastic fluctuations of the network. We discover a fundamental trade-off in controlling the upstream and downstream noise: a longer timescale of the signaling network can buffer upstream noise, while accumulate downstream noise. Moreover, we confirm that this trade-off relation exists in real biological signaling networks such as a fold-change detection circuit and the p53 activation signaling system.Author SummaryInformation transmission is vital in biological systems, such as decoding the information regarding nutrient levels during chemotaxis or morphogen concentrations in tissue development. While fluctuations arising from the stochastic nature of biological processes inevitably affect information transmission, noise control mechanisms have been studied for decades. However, it remains controversial what the role of the slow dynamics (long timescale) is in noise control. On the one hand, it has been reported to attenuate noise by averaging out fluctuations. On the other hand, it is also proposed to amplify noise by accumulating fluctuations. Here we dissect the noise in signaling systems into two components: upstream noise originating from signal fluctuation, and downstream noise from the network stochasticity. Our analysis reveals that upstream noise negatively correlates with timescale, while downstream noise exhibits a positive correlation, indicating a fundamental trade-off in controlling the two noise components. Moreover, we provide an intuitive illustration to understand this phenomenon using the concept of landscape representation. Mathematically, we analytically derive a trade-off relation that agrees well with simulations. Our results uncover a new property of noise in signaling processes, deepening our understanding of noise control and proposing a new perspective in designing signaling network.

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“…In recent decades, various techniques of probability and statistical physics have been employed to measure and explain social phenomena [1][2][3]. A variety of social collective phenomena can be well understood through stochastic binary-state models of interacting agents.…”

Section: Introductionmentioning

confidence: 99%

Ordering dynamics and aging in the symmetrical threshold model

Abella,

González-Avella,

San Miguel

et al. 2024

New J. Phys.

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The so-called Granovetter-Watts model was introduced to capture a situation in which the adoption of new ideas or technologies requires a certain redundancy in the social environment of each agent to take effect. This model has become a paradigm for complex contagion. Here we investigate a symmetric version of the model: agents may be in two states that can spread equally through the system via complex contagion. We find three possible phases: a mixed one (dynamically active disordered state), an ordered one, and a heterogeneous frozen phase. These phases exist for several configurations of the contact network. Then, we consider the effect of introducing aging as a non-Markovian mechanism in the model, where agents become increasingly resistant to change their state the longer they remain in it. We show that when aging is present, the mixed phase is replaced, for sparse networks, by a new phase with different dynamical properties. This new phase is characterized by an initial disordering stage followed by a slow ordering process towards a fully ordered absorbing state. In the ordered phase, aging modifies the dynamical properties. For random contact networks, we develop a theoretical description based on an Approximate Master Equation that describes with good accuracy the results of numerical simulations for the model with and without aging.

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Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics

Cited by 36 publications

References 56 publications

Frontiers in Complex Systems

Frontiers in Complex Systems

A trade-off in controlling upstream and downstream noise in signaling networks

Ordering dynamics and aging in the symmetrical threshold model

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