26 papers · ranked by Valyu relevance
Bartosz Prokop, Lendert Gelens
Dynamical systems theory describes how interacting quantities change over time and space, from molecular oscillators to large-scale biological patterns. Such systems often involve nonlinear feedbacks, delays, and interactions across scales. Classical modeling derives explicit governing equations, often systems of…
Usha Kadiyala, David Sprinzak, Nicholas A. M. Monk, Shannon E. Taylor + 6 more
'Shannon E. Taylor' 'Berta Verd' 'Katharina F. Sonnen' 'Lauren Moon' 'Adrienne H. K. Roeder' 'Ruben Perez-Carrasco' 'Pau Formosa-Jordan'] Title: ABSTRACT Developmental biology seeks to unravel the intricate regulatory mechanisms orchestrating the transformation of a single cell into a complex, multicellular organism.…
Petr Klán
Dynamical systems are used as a general label to discuss issues regarding dynamics in all environments where changes take place from the simplest particles to social system [2]. There are much effort to introduce dynamical systems and their activating and inhibiting mechanisms more formally [8]. It results in a…
Enrique Morales, Cesar Bordehore, Héctor M. Pastor, David García-García
Stella Architect (iSee Systems) offers an object-oriented environment that facilitates the development of dynamic models. Stella, however, does not support some of the advanced mathematical tools that can be found in mathematical software such as R. Moreover, R is open source, contains numerous libraries and resources…
Satinder Singh, M. R. James, Matthew Rudary
Modeling dynamical systems, both for control purposes and to make predictions about their behavior, is ubiquitous in science and engineering. Predictive state representations (PSRs) are a recently introduced class of models for discrete-time dynamical systems. The key idea behind PSRs and the closely related OOMs…
Gemma Massonis, Alejandro F. Villaverde, Julio R. Banga
Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in…
Lois A. Gelfand, Sally Engelhart
Dynamical Systems Theory (DST) has generated interest and excitement in psychological research, as demonstrated by the recent statement, “…the dynamical perspective has emerged as a primary paradigm for the investigation of psychological processes at different levels of personal and social reality” (Vallacher et al.…
Roel Dobbe, Claire J. Tomlin
The advent of biological data of increasingly higher resolution in space and time has triggered the use of dynamic models to explain and predict the evolution of biological systems over space and time. Computer-aided system modeling and analysis in biology has led to many new discoveries and explanations that would…
A. Marraffa, R. Krause, V. Mante, G. Haller
Artificial Recurrent Neural Networks (RNNs) are widely used in neuroscience to model the collective activity of neurons during behavioral tasks. The high dimensionality of their parameter and activity spaces, however, often make it challenging to infer and interpret the fundamental features of their dynamics. In this…
Stefano Giampiccolo, Giovanni Iacca, Luca Marchetti
Dynamical systems play a central role across the quantitative sciences, offering a powerful mathematical framework to describe, analyze, and predict the evolution of complex processes over time. In Systems Biology, dynamical systems provide a foundation for modeling and predicting the intricate behaviors of biological…
Scott Gigante, David van Dijk, Kevin R. Moon, Alexander Strzalkowski + 2 more
'Guy Wolf' 'Smita Krishnaswamy'] Abstract—Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, "snapshot" measurements that sample the output of the system are often available.…
Xiaojun Wu, MeiLu McDermott, Adam L MacLean, Mark Alber
Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction…
Bryan C. Daniels, William S. Ryu, Ilya Nemenman
The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process…
Niall M. Mangan, Travis Askham, Steven L. Brunton, J. Nathan Kutz + 1 more
'Joshua L. Proctor'] Abstract. Hybrid systems are traditionally difficult to identify and analyze using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we…
Bryan C. Daniels, Ilya Nemenman
Cellular regulatory dynamics is driven by large and intricate networks of interactions at the molecular scale, whose sheer size obfuscates understanding. In light of limited experimental data, many parameters of such dynamics are unknown, and thus models built on the detailed, mechanistic viewpoint overfit and are not…
Mengman Wei, Qian Peng
Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks often model these components jointly, but many treat variables independently or use static representations. This limits their ability to capture system-level dynamics and…
Yan Zhang, Xiaojie Qiu, Ke Ni, Jonathan Weissman + 2 more
Modeling cellular processes in the framework of dynamical systems theories is a focused area in systems and mathematical biology, but a bottleneck to extend the efforts to genome-wide modeling is lack of quantitative data to constrain model parameters. With advances of single cell techniques, learning dynamical…
Asmeret Naugle, Saeed P. Langarudi, Timothy Clancy
A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be…
Kishore Hari, William Duncan, Mohammed Adil Ibrahim, Mohit Kumar Jolly + 2 more
Mathematical modeling of the emergent dynamics of gene regulatory networks (GRN) faces a double challenge of (a) dependence of model dynamics on parameters, and (b) lack of reliable experimentally determined parameters. In this paper we compare two complementary approaches for describing GRN dynamics across unknown…
Tatiana Baumuratova, Simona Dobre, Thierry Bastogne, Thomas Sauter + 1 more
'Gennady Cymbalyuk'] Systems with bifurcations may experience abrupt irreversible and often unwanted shifts in their performance, called critical transitions. For many systems like climate, economy, ecosystems it is highly desirable to identify indicators serving as early warnings of such regime shifts. Several…
Ezio Bartocci, Pietro Lió, Marta Zofia Kwiatkowska
As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable…
Authors not listed
Accurately modeling the dynamics of open quantum systems is critical for advancing quantum technologies, yet traditional methods often struggle with balancing accuracy and efficiency. Machine learning (ML) offers a promising alternative, particularly through recursive models that predict system evolution based on the…
Authors not listed
Molecular dynamics (MD) and hybrid quantum–classical (QM/MM) methods provide powerful tools for simulating chemical and biological systems, yet their application to long-timescale, strongly coupled processes remains limited by non-integrability, chaotic sensitivity, and extreme time-scale separation. These limitations…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
Carley V. Cook, Ariel M. Lighty, Brenda J. Smith, Ashlee N. Ford Versypt
Bone remodeling is an essential physiological process in the adult skeleton. Due to the complex nature of this process, many mathematical models of bone remodeling have been developed. Each of these models has unique features, but they have underlying patterns. In this review, the authors highlight the important…
Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei Lapkin
A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor…