25 papers · ranked by Valyu relevance
Amin Ghaffari, Yufei Zhao, Xu Chen, Jason Langley + 1 more
Resting-state functional connectivity (FC) have distinct, personalized patterns that could serve as a unique fingerprint of each individual’s brain. While previous brain fingerprinting methods have used functional connectivity maps over a scanning session (static method), it has been shown that the brain is a dynamic…
Doris Voina, Steven L. Brunton, J. Nathan Kutz
Dynamical Systems Authors: ['Doris Voina' 'Steven L. Brunton' 'J. Nathan Kutz'] A significant challenge in many fields of science and engineering is making sense of time-dependent measurement data by recovering governing equations in the form of differential equations. We focus on finding parsimonious ordinary…
Ron Teichner, Naama Brenner, Ron Meir, Jordi Garcia-Ojalvo
Biological systems maintain stability of their function in spite of external and internal perturbations. An important challenge in studying biological regulation is to identify the control objectives based on empirical data. Very often these objectives are time-varying, and require the regulation system to follow a…
Oana-Teodora Chis, Julio R. Banga, Eva Balsa-Canto, Johannes Jaeger
Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be…
Nart Gashi, Panagiotis Kakosimos, Papafotiou, George
— Kolmogorov-Arnold Networks (KANs) are emerging as a powerful framework for interpretable and efficient system identification in dynamic systems. By leveraging the Kolmogorov-Arnold representation theorem, KANs enable function approximation through learnable activation functions, offering improved scalability…
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…
Alejandro F. Villaverde, Antonio Barreiro, Antonis Papachristodoulou, Satoru Miyano
'Satoru Miyano'] A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model…
Attila Gábor, Alejandro F. Villaverde, Julio R. Banga
Background Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the…
Wenjie Mei, Muhammad Nadeem, MirSaleh Bahavarnia, Ahmad F. Taha
System identification through learning approaches is emerging as a promising strategy for understanding and simulating dynamical systems, which nevertheless faces considerable difficulty when confronted with power systems modeled by differential-algebraic equations (DAEs). This paper introduces a neural network (NN)…
Georgios Makrygiorgos, Aaron J. Berliner, Fengzhe Shi, Douglas S. Clark + 2 more
Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed…
Ana Paredes-Vázquez, Eva Balsa-Canto, Julio R. Banga
Microbial communities, complex ecological networks crucial for human and planetary health, remain poorly understood in terms of the quantitative principles governing their composition, assembly, and function. Dynamic modeling using ordinary differential equations (ODEs) is a powerful framework for understanding and…
Omar Rodríguez-Abreo, J. L. Aragón, Mario A. Quiroz‐Juárez
search limits Authors: ['Omar Rodríguez-Abreo' 'J. L. Aragón' 'Mario A. Quiroz‐Juárez'] Mathematical modeling is a powerful tool for describing, predicting, and understanding complex phenomena exhibited by real-world systems. However, identifying the equations that govern a system's dynamics from experimental data…
Jake P. Taylor-King, Asbjørn N. Riseth, Manfred Claassen
Recent high-dimensional single-cell technologies such as mass cytometry are enabling time series experiments to monitor the temporal evolution of cell state distributions and to identify dynamically important cell states, such as fate decision states in differentiation. However, these technologies are destructive, and…
Filippo Menolascina, Domenico Bellomo, Thomas Maiwald, Vitoantonio Bevilacqua + 3 more
'Vitoantonio Bevilacqua' 'Caterina Ciminelli' 'Angelo Paradiso' 'Stefania Tommasi'] Background Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce…
Saeideh Khatiry Goharoodi, Kevin Dekemele, Luc Dupré, Mia Loccufier + 1 more
'Guillaume Crevecoeur'] Abstract: In this paper we aim to apply an adaptation of the recently developed technique of sparse identification of nonlinear dynamical systems on a Duffing experimental setup with cubic feedback of the output. The Duffing oscillator described by nonlinear differential equation which…
Yanbing Wang, Maria Laura Delle Monache, Daniel B. Work
The advancement of in-vehicle sensors provides abundant datasets to estimate parameters of car-following models that describe driver behaviors. The question of parameter identifiability of such models (i.e., whether it is possible to infer its unknown parameters from the experimental data) is a central system analysis…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Shun Wang, Wenrui Hao
Practical identifiability is a fundamental challenge in the data-driven modeling of biological systems, as many model parameters cannot be directly measured and must be estimated from experimental data. Without confirming the identifiability of these parameters, model predictions may be unreliable, limiting their…
Samuel Pastva, Kyu Hyong Park, Jordan C. Rozum, Van-Giang Trinh + 1 more
Connecting the dynamics of biomolecular networks to experimentally measurable cell phenotypes remains a central challenge in systems biology. Here we introduce a model-based definition of phenotype as a partial steady state that is committed to a certain dynamical outcome while otherwise being minimally constrained. We…
Shun Wang, Wenrui Hao
Practical identifiability is a fundamental challenge in the data-driven modeling of biological systems, as many model parameters cannot be directly measured and must be estimated from experimental data. Without confirming the identifiability of these parameters, model predictions may be unreliable, limiting their…
Shara Balakrishnan, Aqib Hasnain, Nibodh Boddupalli, Dennis M. Joshy + 2 more
'Robert G. Egbert' 'Enoch Yeung'] Abstract— In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then…
Marina Gorostiola González, Remco L. van den Broek, Thomas G.M. Braun, Magdalini Chatzopoulou + 4 more
Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive…
Claudia Diehl, Alessandra Salerno, Alessio Ciulli
Dynamic combinatorial chemistry (DCC) leverages a reversible reaction to generate compound libraries from constituting building blocks under thermodynamic control. The position of this equilibrium can be biased by addition of a target macromolecule towards enrichment of bound ligands. While DCC has been applied to…
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…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…