Seminar

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 …