22 papers · ranked by Valyu relevance
Authors not listed
This paper presents the Multi Cell-line Kinetic Model (MCKM), a novel generalised kinetic mechanistic model specifically tailored for Ambr15™ fed-batch cultivations of multiple Chinese Hamster Ovary (CHO) cell lines producing different recombinant monoclonal antibodies (mAbs). Unlike traditional models that requires…
Martina Cantone, Guido Santos, Pia Wentker, Xin Lai + 1 more
Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The…
Xiaohan Kang, Bruce Hajek, Yoshie Hanzawa
A gene regulatory network can be described at a high level by a directed graph with signed edges, and at a more detailed level by a system of ordinary differential equations (ODEs). The former qualitatively models the causal regulatory interactions between ordered pairs of genes, while the latter quantitatively models…
Claudia Stötzel, Susanna Röblitz, Heike Siebert, Attila Csikász-Nagy
In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs) into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which…
Ben Noordijk, Monica L. Garcia Gomez, Kirsten H. W. J. ten Tusscher, Dick de Ridder + 2 more
'Dick de Ridder' 'Aalt D. J. van Dijk' 'Robert W. Smith'] Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a…
T.J. Sego, Josua O. Aponte-Serrano, Juliano F. Gianlupi, James A. Glazier
The biophysics of an organism span scales from subcellular to organismal and include spatial processes like diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales.…
Eva-Maria Kapfer, Paul Stapor, Jan Hasenauer
Mathematical models based on ordinary differential equations have been employed with great success to study complex biological systems. With soaring data availability, more and more models of increasing size are being developed. When working with these large-scale models, several challenges arise, such as high…
Mansura Habiba, Barak A. Pearlmutter
Neural differential equations are a promising new member in the neural network family. They show the potential of differential equations for time series data analysis. In this paper, the strength of the ordinary differential equation (ODE) is explored with a new extension. The main goal of this work is to answer the…
Christopher Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt + 2 more
'Lyndon White' 'Vaibhav Dixit'] DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the complementary nature between machine learning models and differential equations. We demonstrate the ability…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
Chandra Kanth Nagesh, Sriram Sankaranarayanan, Ramneet Kaur, Tuhin Sahai + 1 more
We study the problem of learning neural network models for Ordinary Differential Equations (ODEs) with parametric uncertainties. Such neural network models capture the solution to the ODE over a given set of parameters, initial conditions, and range of times. Physics-Informed Neural Networks (PINNs) have emerged as a…
Philipp Städter, Yannik Schälte, Leonard Schmiester, Jan Hasenauer + 1 more
'Paul L. Stapor'] Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often…
Aaron Tuor, Ján Drgoňa, Draguna Vrabie
Differential equations are frequently used in engineering domains, such as modeling and control of industrial systems, where safety and performance guarantees are of paramount importance. Traditional physics-based modeling approaches require domain expertise and are often difficult to tune or adapt to new systems. In…
Philipp Städter, Yannik Schälte, Leonard Schmiester, Jan Hasenauer + 1 more
Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when…
Florencio Rusty Punzalan, Yoshiharu Yamashita, Naoki Soejima, Masanari Kawabata + 4 more
'Masanari Kawabata' 'Takao Shimayoshi' 'Hiroaki Kuwabara' 'Yoshitoshi Kunieda' 'Akira Amano'] Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model…
Aleksandr Fedorov, Anna Perechodjuk, David Linke
Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in fundamental and applied science. However, training and building reliable ANN models requires a lot of data which so far hinders their wider application in kinetic modelling where typically only small (experimental) datasets are…
Simon Witzke, Julian Zabbarov, Maximilian Kleissl, Pascal Iversen + 2 more
Accurate prediction of the temporal dynamics of biological systems is crucial for informing timely and effective interventions, e.g., in ecological or epidemiological contexts, or for treatment adjustments in therapy. While machine learning has proven its capabilities in generalizing the underlying non-linear dynamics…
Mascha Berg, Julia Plöntzke, Heike Siebert, Susanna Röblitz
Boolean delay equations (BDEs), with their relatively simple and intuitive mode of modelling, have been used in many research areas including, for example, climate dynamics and earthquake propagation. Their application to biological systems has been scarce and limited to the molecular level. Here, we derive and present…
Colby Fronk, Linda Petzold
Exponential Integration Methods Authors: ['Colby Fronk' 'Linda Petzold'] Stiff ordinary differential equations (ODEs) are common in many science and engineering fields, but standard neural ODE approaches struggle to accurately learn these stiff systems, posing a significant barrier to widespread adoption of neural…
Yakov Golovanev, Alexander Hvatov
Most machine learning methods are used as a black box for modelling. We may try to extract some knowledge from physics-based training methods, such as neural ODE (ordinary differential equation). Neural ODE has advantages like a possibly higher class of represented functions, the extended interpretability compared to…
L. E. Wadkin, I. Makarenko, N. G. Parker, A. Shukurov + 2 more
'F. C. Figueiredo' 'M. Lako'] Purpose of Review To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications. Recent Findings hSCs hold great potential in ocular regenerative medicine due to their application in…
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…