Journal Club

Abstract: Consider a biomolecular reaction network that exhibits robust perfect adaptation to disturbances from several parallel sources. The well-known Internal Model Principle of control theory suggests that such systems must include a subsystem (called the “internal model”) that is able to recreate the dynamic structure of the disturbances. This requirement poses certain structural constraints on the network …

We will discuss about "Physics-informed learning of governing equations from scarce data", Chen et al., Nature Communications, 2021 Abstract: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name …

We will discuss about "RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy", Behrendt et al., SoftwareX, 2019 Abstract: This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Rényi transfer entropy using the R package RTransferEntropy. We discuss the methodology, the bias …

We will discuss about "A topological data analysis based classifier", Kindelan et al., arXiv, 2022 Abstract: Topological Data Analysis is an emergent field that aims to discover the underlying dataset’s topological information. Topological Data Analysis tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes …

We will discuss about "An Efficient Characterization of Complex-Balanced, Detailed-Balanced, and Weakly Reversible Systems", Craciun et al., SIAM Journal on Applied Mathematics, 2020 Abstract: Very often, models in biology, chemistry, physics, and engineering are systems of polynomial or power-law ordinary differential equations, arising from a reaction network. Such dynamical systems can be generated by many …

We will discuss about "Toroidal topology of population activity in grid cells", Gardner et al., Nature, 2021. Abstract: The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations, …

We will discuss about "The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes", Katori et al., PNAS, 2022. Abstract: Human sleep phenotypes can be defined and diversified by both genetic and environmental factors. However, some sleep phenotypes are difficult to evaluate without long-term, precise sleep monitoring, for which simple …

We will discuss about "Optimizing Oscillators for Specific Tasks Predicts Preferred Biochemical Implementations", Agrahar and  Rust., bioRxiv, 2022. Abstract: Oscillatory processes are used throughout cell biology to control time-varying physiology including the cell cycle, circadian rhythms, and developmental patterning. It has long been understood that free-running oscillations require feedback loops where the activity of one …

We will discuss about "Approximating Solutions of the Chemical Master Equation using Neural Networks", Sukys et al., bioRxiv, 2022 Abstract: The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest. While Monte Carlo methods provide a …

We will discuss about "Identifying the critical states of complex diseases by the dynamic change of multivariate distribution", Peng, Hao, et al., Briefings in Bioinformatics, 2022. Abstract: The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes …

We will discuss about "Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization", Wang, Yingfan, et al., J. Mach. Learn. Res., 2021. Abstract: Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMAP have demonstrated impressive visualization performance on many real world datasets. One tension …

We will discuss about "AI Pontryagin or how artificial neural networks learn to control dynamical systems", Böttcher, L., Antulov-Fantulin, N. & Asikis, T., Nat Commun 13, 333 (2022). Abstract: The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems, both control energy and cost …