Journal Club

We will discuss about "Cellular signaling beyond the Wiener-Kolmogorov limit", Weisenberger et al., bioRxiv, 2021 Abstract: Accurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory, originally developed for engineering problems, has …

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 …

We will discuss about “Detecting and quantifying causal associations in large nonlinear time series datasets”, Runge et al., Science Advances, 2019 Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference …

We will discuss about “Solving Singular Control Problems in Mathematical Biology, Using PASA”, Atkins et al., arXiv, 2020 In this paper, we will demonstrate how to use a nonlinear polyhedral constrained optimization solver called the Polyhedral Active Set Algorithm (PASA) for solving a general singular control problem. We present methods of discretizing a general optimal …

We will discuss about “A Random Matrix Theory Approach to Denoise Single-Cell Data”, Aparicio et al., Patterns, 2020 Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method …

We will discuss about “Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics”, Ji et al., The Journal of Physical Chemistry A, 2020 The recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network such that the network not …

We will discuss about "Information Integration and Energy Expenditure in Gene Regulation", Estrada et al., Cell, 2016 Abstract: The quantitative concepts used to reason about gene regulation largely derive from bacterial studies. We show that this bacterial paradigm cannot explain the sharp expression of a canonical developmental gene in response to a regulating transcription factor …