Journal Club

We will discuss about "Network design principle for robust oscillatory behaviors with respect to biological noise", Qiao et al, bioRxiv, 2021 Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of …

We will discuss about "Mechanisms for the generation of robust circadian oscillations through ultrasensitivity and differential binding affinity", Behera, Junco, and Vaikuntanathan, The Journal of Physical Chemistry B, 2021 Biochemical circadian rhythm oscillations play an important role in many signaling mechanisms. In this work, we explore some of the biophysical mechanisms responsible for sustaining robust …

We will discuss about "Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations", Mircea et al., 2022, Genome Biology The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be …

We will discuss about "A Deficiency-Based Approach to Parametrizing Positive Equilibria of Biochemical Reaction Systems", Johnston, Müller, and Pantea, Bulletin of Mathematical Biology, 2019 We present conditions which guarantee a parametrization of the set of positive equilibria of a generalized mass-action system. Our main results state that (1) if the underlying generalized chemical reaction network …

We will discuss about "Modeling polypharmacy side effects with graph convolutional networks", Zitnik, Agrawal, and Leskovec, Bioinformatics, 2018 Motivation The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. …

Abstract: Genetic feedback loops can be used by cells as a means to regulate internal processes or keep track of time. It is often thought that, for a genetic circuit to display self-sustained oscillations, a degree of cooperativity is needed in the binding and unbinding of actor species. This cooperativity is usually modeled using a …

Abstract: The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the …

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 …