16 papers · ranked by Valyu relevance
David Alonso, Steffen Bauer, Markus Kirkilionis, Lisa Maria Kreuser + 1 more
'Luca Sbano'] 1Theoretical and Computational Ecology, Center for Advanced Studies of Blanes (CEAB-CSIC), Spanish Council for Scientific Research, Acces Cala St. Francesc 14, Blanes, E-17300, Spain. 2Mathematisches Institut, Mathematikon, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 205, 69120 Heidelberg…
Alexander Temerev, Liudmila Rozanova, Olivia Keiser, Janne Estill
We have developed a mathematical model and stochastic numerical simulation for the transmission of COVID-19 and other similar infectious diseases that accounts for the geographic distribution of population density, detailed down to the level of location of individuals, and age-structured contact rates. Our analytical…
Krishna Rijal, Pankaj Mehta
The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the…
Krishna Rijal, Pankaj Mehta
The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the…
Krishna Rijal, Pankaj Mehta
The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the…
Krishna Rijal, Pankaj Mehta, Anne-Florence Bitbol, Aleksandra M Walczak
'Aleksandra M Walczak'] The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates…
David Lacoste, Michele Castellana
We present an improvement of the Gillespie Exact Stochastic Simulation Algorithm, which leverages a bitwise representation of variables to perform independent simulations in parallel. We show that the subsequent gain in computational yield is significant, and it may allow to perform simulations of non-well mixed…
Dipanjana Dalui, John P DeLong
Eco-evolutionary processes drive patterns in species’ abundances and traits. However, modeling complex ecological systems is challenging because of the many species and traits involved and because processes unfold in stochastic and non-equilibrium conditions. Gillespie Eco-evolutionary Models (GEMs) were created to…
Krishna Rijal, Pankaj Mehta
parameter estimation, and designing synthetic biological circuits Authors: ['Krishna Rijal' 'Pankaj Mehta'] The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the…
Naoki Masuda, Christian L. Vestergaard
Many multiagent dynamics, including various collective dynamics occurring on networks, can be modeled as a stochastic process in which the agents in the system change their state over time in interaction with each other. The Gillespie algorithms are popular algorithms that exactly simulate such stochastic multiagent…
Ron Solan, Gad Getz
The Gillespie algorithm and its extensions are commonly used for the simulation of chemical reaction networks. A limitation of these algorithms is that they have to process and update the system after every reaction, requiring significant computation. Another class of algorithms, based on the τ -leaping method, is able…
Andrew J. Loza, Marc S. Sherman
Biological systems frequently contain biochemical species present as small numbers of slowly diffusing molecules, leading to fluctuations that invalidate deterministic analyses of system dynamics. The development of mathematical tools that account for the spatial distribution and discrete number of reacting molecules…
S. Mukundan, Girish Deshpande, M.S. Madhusudhan
Molecular interactions play a central role in all biological processes. The strength of these interactions is often characterized by their dissociation constants (K_D_). The high affinity interactions (K_D_ ≤ 10^-8^ M) are crucial for the proper execution of cellular processes and are thus extensively investigated.…
Douglas Lin, Michael Martin
Introduction Bioreporters are genetically engineered cells that produce detectable responses in the presence of specific analytes, providing a cheap, mass-producible, and accurate method of analyte detection. Most research focuses on the single cell-level, where all engineering is concentrated on the interactions…
Authors not listed
Stochastic Simulation Algorithms (SSA) are a cornerstone in simulating Free Radical Polymerization (FRP) due to their accuracy and reliability. However, computational inefficiency remains a challenge for large-scale and complex polymerization systems. This work introduces a novel stochastic simulation algorithm…
Elena D’Ambrosio, Zhou Fang, Ankit Gupta, Sant Kumar + 1 more
Time-lapse microscopy has become increasingly prevalent in biological experimentation, as it provides single-cell trajectories that unveil valuable insights into underlying networks and their stochastic dynamics. However, the limited availability of fluorescent reporters typically constrains tracking to only a few…