14 papers · ranked by Valyu relevance
Pariksheet Nanda, Maral Budak, Christian T. Michael, Kathryn Krupinsky + 1 more
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex…
Lionel Kusch, Sandra Diaz, Wouter Klijn, Kim Sontheimer + 3 more
Integration of information across heterogeneous sources creates added scientific value. It is, however, a challenge to progress, often a barrier, to interoperate data, tools and models across spatial and temporal scales. Here we present a design template for coupling simulators operating at different scales and…
Parima Ahmadipour, Omid G. Sani, Bijan Pesaran, Maryam M. Shanechi
Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important…
Jean-Louis Palgen, Angélique Perrillat-Mercerot, Nicoletta Ceres, Emmanuel Peyronnet + 6 more
Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data.…
M. Deepa Maheshvare, Soumyendu Raha, Debnath Pal
Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability…
Miguel Ponce-de-Leon, Arnau Montagud, Charilaos Akasiadis, Janina Schreiber + 2 more
The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell-cell variability have been described as playing a key role in the…
T.J. Sego, James P. Sluka, Herbert M. Sauro, James A. Glazier
Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular…
Barak Raveh, Liping Sun, Kate L. White, Tanmoy Sanyal + 11 more
Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each…
Christopher Schölzel, Valeria Blesius, Gernot Ernst, Andreas Dominik
Reproducible, understandable models that can be reused and combined to true multi-scale systems are required to solve the present and future challenges of systems biology. However, many mathematical models are still built for a single purpose and reusing them in a different context can be challenging due to an…
Ranjan Anantharaman, Anas Abdelrehim, Anand Jain, Avik Pal + 3 more
Quantitative systems pharmacology (QsP) may need to change in order to accommodate machine learning (ML), but ML may need to change to work for QsP. Here we investigate the use of neural network surrogates of stiff QsP models. This technique reduces and accelerates QsP models by training ML approximations on…
Niloofar Shahidi, Michael Pan, Soroush Safaei, Kenneth Tran + 2 more
Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in…
Jessica S. Yu, Blair Lyons, Susanne Rafelski, Julie A. Theriot + 2 more
Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate…
Christopher T. Lee, Justin G. Laughlin, Nils Angliviel de La Beaumelle, Rommie E. Amaro + 4 more
Recent advances in electron microscopy have, for the first time, enabled imaging of single cells in 3D at a nanometer length scale resolution. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations will require a system for…
Attila Gabor, Marco Tognetti, Alice Driessen, Jovan Tanevski + 7 more
Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies…