Major computational challenges exist in relation to the collection, curation, processing and
analysis of large genomic and imaging datasets, as well as the simulation of larger and …

Mathematical modelling of whole biological cells opens up new opportunities for
fundamental and applied biology. In particular in the context of synthetic biology, it opens up …

The modelling of many real-world problems relies on computationally heavy simulations of
randomly interacting individuals or agents. However, the values of the parameters that …

The Python package pyABC provides a framework for approximate Bayesian computation
(ABC), a likelihood-free parameter inference method popular in many research areas. At its …

Motivation Existing methods for simulating synthetic genotype and phenotype datasets have
limited scalability, constraining their usability for large-scale analyses. Moreover, a …

The predictive power of machine learning models often exceeds that of mechanistic
modeling approaches. However, the interpretability of purely data-driven models, without …

The abnormal activation of the mTOR pathway is closely related to the occurrence and
progression of cancer, especially colorectal cancer. In this study, a rational virtual screening …

Gene expression inherently gives rise to stochastic variation (“noise”) in the production of
gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However …

Abstract Motivation Approximate Bayesian computation (ABC) is an increasingly popular
method for likelihood-free parameter inference in systems biology and other fields of …

A Julia language (Bezanson et al., 2017) package providing practical and modular
implementation of “Calibrate, Emulate, Sample”(Cleary et al., 2021), hereafter CES, an …