25 papers · ranked by Valyu relevance
Tim Maiwald, Helge Hass, Bernhard Steiert, Joep Vanlier + 8 more
'Raphael Engesser' 'Andreas Raue' 'Friederike Kipkeew' 'Hans H. Bock' 'Daniel Kaschek' 'Clemens Kreutz' 'Jens Timmer' 'Byung-Jun Yoon'] In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined…
Thomas J. Snowden, Piet H. van der Graaf, Marcus J. Tindall
Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed…
Ayush Pandey, Richard M. Murray
We present an automated model reduction algorithm that uses quasi-steady state approximation based reduction to minimize the error between the desired outputs. Additionally, the algorithm minimizes the sensitivity of the error with respect to parameters to ensure robust performance of the reduced model in the presence…
Thomas J. Snowden, Piet H. van der Graaf, Marcus J. Tindall
Background Systems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approaches, or real-time simulation, this growing model complexity can present a significant hurdle. Often…
Markus Koschorreck, Holger Conzelmann, Sybille Ebert, Michael Ederer + 1 more
'Ernst Dieter Gilles'] Background Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows…
Mochamad Apri, Maarten de Gee, Simon van Mourik, Jaap Molenaar + 1 more
'Marie-Joelle Virolle'] Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding…
Punit Tulpule, Umesh Vaidya
This paper presents an information-theoretic approach for model reduction for finite time simulation. Although system models are typically used for simulation over a finite time, most of the metrics (and pseudo-metrics) used for model accuracy assessment consider asymptotic behavior e.g., Hankel singular values and…
Sanjana Gupta, Robin E.C. Lee, James R. Faeder
Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso…
Elena Kutumova, Andreï Zinovyev, Ruslan Sharipov, Fedor Kolpakov
1 Institute of Systems Biology, Ltd, 15 Detskiy proezd, Novosibirsk 630090, Russia 2Design Technological Institute of Digital Techniques, The Siberian Branch of The Russian Academy of Sciences, 6 Acad. Rzhanov Str., Novosibirsk 630090, Russia 3 Institute Curie, 26 rue d'Ulm, Paris 75248, France 3 Institut Curie, 26 rue…
Ayush Pandey, Richard M. Murray
We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic biological circuits that guide the design using chemical reaction-level descriptive models. From the parts and mechanism description of a synthetic biological circuit, it is easy to obtain a chemical…
Thomas J. Snowden, Piet H. van der Graaf, Marcus J. Tindall
In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model…
Kate E. Dray, Joseph J. Muldoon, Niall M. Mangan, Neda Bagheri + 1 more
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier…
Elena Kutumova, Andrei Zinovyev, Ruslan Sharipov, Fedor Kolpakov
Background Many mathematical models characterizing mechanisms of cell fate decisions have been constructed recently. Their further study may be impossible without development of methods of model composition, which is complicated by the fact that several models describing the same processes could use different reaction…
Luuk Poort, Lars A. L. Janssen, Bart Besselink, R.H.B. Fey + 1 more
Interconnected System Reduction Authors: ['Luuk Poort' 'Lars A. L. Janssen' 'Bart Besselink' 'R.H.B. Fey' 'Nathan van de Wouw'] Abstract—This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnected…
Anika Küken, Philipp Wendering, Damoun Langary, Zoran Nikoloski
Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of…
Karl Friston, Thomas Parr, Peter Zeidman
This paper reviews recent developments in statistical structure learning; namely, Bayesian model reduction. Bayesian model reduction is a method for rapidly computing the evidence and parameters of probabilistic models that differ only in their priors. In the setting of variational Bayes this has an analytical…
Mahsa Sajjadi, Kaiyang Huang, Kai Sun
—This paper describes an adaptive method to reduce a nonlinear power system model for fast and accurate transient stability simulation. It presents an approach to analyze and rank participation factors of each system state variable into dominant system modes excited by a disturbance so as to determine which regions or…
Mohammad Khatibi, Fatemeh Rahmani, Tanushree Agarwal
Computational Research Progress in Applied Science & Engineering CRPASE Vol. 06(01), 46-51, March 2020 Mohammad Khatibia , Fatemeh Rahmanib , Tanushree Agarwalc a Department of Electrical Engineering, K.N.Toosi University of Technology, Tehran, Iran b Department of Electronics, Central Tehran Branch, Islamic Azad…
Suryoday Prodhan, Rex Manurung, Alessandro Troisi
We present a model reduction scheme for polymer semiconductors which can be utilized to compute intrachain charge-carrier mobility from the monomer sequence. The reduced model can be used in conjunction with any quantum dynamics approach, but it is explored here assuming that transport takes place through incoherent…
Authors not listed
We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into…
Conrad Hübler
A novel application to determine stability constants from supramolecular titration experiments is presented. The focus lies on NMR titration and ITC experiments for pure 1:1 systems, as well as mixed 2:1/1:1, 1:1/1:2 and 2:1/1:1/1:2 systems. SupraFit provides global and local fitting and a global search tool.…
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…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Authors not listed
The recent release of Meta's Open Molecules 2025 dataset (OMol25) has enabled the creation of pretrained NNPs that can predict the energy of unseen molecules in a variety of charge and spin states. However, these models do not explicitly consider charge- or spin-based physics, potentially impacting the accuracy of…
Authors not listed
Manganese (oxyhydr)oxides are abundant redox-active minerals that influence diverse biogeochemical processes, yet their redox reactivity remains poorly understood due to variations in mineral structure and manganese oxidation state. We quantified the reduction kinetics of three manganese oxides—birnessite, manganite…