14 papers · ranked by Valyu relevance
Nicholas A. G. Johnson, Liezel Tamon, Xin Liu, Aleksandr B. Sahakyan
Many calculations in computational biology necessitate a use of a probabilistic optimisation protocol to determine a set of parameters that capture the system at a desired state in the configurational space. Here, we developed a flexible optimisation engine in R that can be plugged to any, simple or complex, modelling…
Máté Mohácsi, Márk Patrik Török, Sára Sáray, Luca Tar + 1 more
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Hua-Dong Xiong, Li Ji-An, Marcelo G. Mattar, Robert C. Wilson
Cognitive modeling provides a formal method to articulate and test hypotheses about cognitive processes. However, accurately and reliably estimating model parameters remains challenging due to common issues in behavioral science, such as limited data, measurement noise, experimental constraints, and model complexity.…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a…
Matt J. Owen, Gary R. Mirams
Ion channel models present many challenging optimisation problems. These include unidentifiable parame- ters, noisy data, unobserved states, and a combination of both fast and slow timescales. This can make it difficult to choose a suitable optimisation routine a priori. Nevertheless, many attempts have been made to…
Cameron Meaney, Mohammad Kohandel, Arian Novruzi
External beam radiation therapy is a key part of modern cancer treatments which uses high doses of radiation to destroy tumour cells. Despite its widespread usage and extensive study in theoretical, experimental, and clinical works, many questions still remain about how best to administer it. Many mathematical studies…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Georges Czaplicki, Serge Mazeres
Model validation depends on the agreement between the predicted and experimental data. However, finding solutions to problems, described by equations with many parameters, for which virtually nothing is known, is a difficult task. For example, the extraction of kinetic parameters from complex schemes representing the…
Nisha Ann Viswan, Alexandre Tribut, Manvel Gasparyan, Ovidiu Radulescu + 1 more
Biological signalling systems are complex, and efforts to build mechanistic models must confront a huge parameter space, indirect and incomplete data, and frequently encounter multiscale and multiphysics phenomena. We present HOSS, a framework for Hierarchical Optimization of Systems Simulations, to address such…
Sina Dabiri, Eric R. Cole, Robert E. Gross
Brain stimulation has become an important treatment option for a variety of neurological and psychiatric diseases. A key challenge in improving brain stimulation is selecting the optimal set of stimulation parameters for each patient, as parameter spaces are too large for brute-force search and their induced effects…
Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of…
Andrea Boscutti, Valeria Grasso, Tommaso Di Ianni
Low-intensity focused ultrasound (LIFU) is a promising neuromodulation modality, but challenges related to high response variability and the poorly understood parameter space undermine progress in clinical applications. To facilitate the development of therapeutic LIFU protocols, we developed an approach for…