16 papers · ranked by Valyu relevance
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…
Chay Paterson, Miaomiao Gao, Joshua Hellier, Georg Luebeck + 3 more
Compound birth-death processes are widely used to model the age-incidence curves of many cancers. There are efficient schemes for directly computing the relevant probability distributions in the context of linear multi-stage clonal expansion (MSCE) models. However, these schemes have not been generalised to models on…
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…
Solmaz Golmohammadi, Mina Zarei, Jacopo Grilli
We investigate stochastic predator-prey dynamics and their spatial phase synchronization using the Rosenzweig-MacArthur model coupled across multiple patches. Combining stochastic simulations based on the Gillespie algorithm with analytical methods inspired by the XY model, we uncover fundamental mechanisms through…
Stephen Zhewen Lu, Aakarsh Vermani, Kohei Sanno, Jiarui Lu + 3 more
Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these methods overlook affinity maturation as a rich and largely untapped source of information about the evolutionary process by which antibodies…
Tom Kimpson, Domenic P. J. Germano, Jennifer A. Flegg, Mark B. Flegg
Many biological systems exhibit multiscale dynamics, where some species occur in high copy numbers while others remain rare. This heterogeneity necessitates hybrid modelling approaches: deterministic models are computationally efficient but inaccurate for low-count species, while fully stochastic simulations are…
Jia Le Tan, Nicola D. Walker, Richard G. Everitt
A popular technique for reducing the variance of importance sampling (IS) estimators is to modify the weights of some importance points. One approach is to truncate the largest weights, which reduces variance but can introduce substantial bias. Pareto smoothed importance sampling (PSIS), by contrast, reduces the…
Domenic P. J. Germano, Alexander E. Zarebski, Sophie Hautphenne, Robert Moss + 2 more
Multi-scale systems often exhibit a combination of stochastic and deterministic dynamics. In compartmental models, low occupancy compartments tend to exhibit stochastic dynamics while high occupancy compartments tend to follow deterministic dynamics. Representing both dynamics with existing methods is challenging.…
Arunendra Kumar Verma, Hillol Kumar Barman, Krishna Rijal, Dibyendu Das
Within the studies of stochastic gene expression, apart from the variability of copy number of gene products, the problems of threshold crossing of those products are biologically important as they often lead to terminal cellular events. Here, we study the threshold crossing problem of the messenger ribonucleic acid…
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Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Pankaj Sharma, Rohit Salgotra, Saravanakumar Raju, Szymon Łukasik + 1 more
The identification of unknown parameters for proton exchange memberane fuel cells (PEMFCs) using nature-inspired optimization algorithms has emerged as a significant field of research in recent years. In the present study, a novel approach is presented, namely the hybrid Gray Particle Cuckoo (GPC) algorithm based on…
Alex N Popinga, Jack Forman, Dmitri Svetlov, Huy Vo + 1 more
Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and…
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Exploring the potential energy surface to sample transition state regions is crucial to understand the atomic processes that govern chemical reactivity. Ideally, the exploration does not require any collective variables that are based on prior chemical domain knowledge. With this in mind, we adapt the stochastic saddle…
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Atomistic simulations provide essential mechanistic insights into chemical processes, yet many important phenomena in chemistry and materials science occur on timescales that are inaccessible to molecular dynamics. Existing computational approaches force a choice between atomic resolution on relatively short timescales…
Sartori, Camilo Chacón, Blum, Christian
Automatic algorithm configuration tools such as irace efficiently tune parameter values but leave algorithmic code unchanged. This paper introduces a first version of irace-evo, an extension of irace that integrates code evolution through large language models (LLMs) to jointly explore parameter and code spaces. The…
João Sartori, Eduardo Krempser, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado
The optimization of protein sequences for enhanced binding and stability remains a formidable challenge in bioengineering due to the vastness of sequence space. Existing state-of-the-art methods, including traditional structure-based design and protein language models, use fitness estimators as objective functions to…