We will discuss about "Machine learning of stochastic gene network phenotypes", Park et al., bioRxiv, 2019 Abstract: A recurrent challenge in biology is the development of predictive quantitative models because most molecular and cellular parameters have unknown values and realistic models are analytically intractable. While the dynamics of the system can be analyzed via computer …

We will discuss about “Nonlinear delay differential equations and their application to modeling biological network motifs”, Glass et al., Nature Communications, 2021 Abstract: Biological regulatory systems, such as cell signaling networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight …

We will discuss about “The Oscillation Amplitude, Not the Frequency of Cytosolic Calcium, Regulates Apoptosis Induction ”, Qi et al., iScience, 2020 Abstract: Although a rising concentration of cytosolic Ca2+ has long been recognized as an essential signal for apoptosis, the dynamical mechanisms by which Ca2+ regulates apoptosis are not clear yet. To address this, we constructed …

We will discuss about “A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells”, Unosson et al., bioRxiv, 2021 We propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse spatiotemporal gene expression in intact …

We will discuss about “Balanced truncation for model reduction of biological oscillators”, Padoan et al., Biological Cybernetics, 2021 Model reduction is a central problem in mathematical biology. Reduced order models enable modeling of a biological system at different levels of complexity and the quantitative analysis of its properties, like sensitivity to parameter variations and resilience …

We will discuss about “Filtering and inference for stochastic oscillators with distributed delays”, Calderazzo et al., Bioinformatics, 2018 at the Journal Club Motivation The time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the …