Highly accurate fluorogenic DNA sequencing with information theory–based error correction

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about "Highly accurate fluorogenic DNA sequencing with information theory–based error correction", Chen et al., Nature Biotechnology (2017) Eliminating errors in next-generation DNA sequencing has proved challenging. Here we present error-correction code (ECC) sequencing, a method to greatly improve sequencing accuracy by combining fluorogenic sequencing-by-synthesis (SBS) with an information theory–based error-correction algorithm. ECC

Synthetic multistability in mammalian cells

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about "Synthetic multistability in mammalian cells", Zhu et al., bioRxiv (2021) In multicellular organisms, gene regulatory circuits generate thousands of molecularly distinct, mitotically heritable states, through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and allow engineering of multicellular programs that require

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about "A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics", Dharmarajan et al., Cell Systems (2019) Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a

Introduction to Bayesian ML/DL, with Application to Parameter Inference of Coupled Non-linear ODEs – Part 1

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

In this talk, the speaker will present introductory materials about Bayesian Machine Learning. Abstract Gaussian process(GP) is a stochastic process such that the joint distribution of an arbitrary finite subset of the random variables is a multivariate normal. It plays a fundamental role in Bayesian machine learning as it can be interpreted as a prior

Introduction to Bayesian ML/DL, with Application to Parameter Inference of Coupled Non-linear ODEs – Part 2

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

In this talk, the speaker will present introductory materials about Bayesian Machine Learning. Abstract The problem of approximating the posterior distribution (or density estimation in general) is a crucial problem in Bayesian statistics, in which intractable integrals often become the computational bottleneck. MCMC sampling is the most widely used family of algorithms for approximating posteriors.

Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about "Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model", Ito et. al., PloS ONE, 2011 Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is

Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about "Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes", Hempel et. al., bioRxiv, 2021 In order to advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of

DNA as a universal substrate for chemical kinetics

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about "DNA as a universal substrate for chemical kinetics ", Soloveichik et al., PNAS (2009) Molecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that

Collective Oscillations in coupled cell systems

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about "Collective Oscillations in coupled cell systems", Chen and Sinh, Bulletin of Mathematical Biology, 2021 We investigate oscillations in coupled systems. The methodology is based on the Hopf bifurcation theorem and a condition extended from the Routh–Hurwitz criterion. Such a condition leads to locating the bifurcation values of the parameters. With such

DeepCME: A deep learning framework for solving the Chemical Master Equation

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about “DeepCME: A deep learning framework for solving the Chemical Master Equation,” Gupta et al., bioRxiv, 2021 Stochastic models of biomolecular reaction networks are commonly employed in systems and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. For such models, the Kolmogorov’s forward

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about “Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions”, Thurley et al., Cell Systems, 2021 Abstract: Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far

Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter

B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

We will discuss about “Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter”, Bonarius et. al., IEEE Trans. Biomed. Eng., 2021 Abstract Objective: In the near future, real-time estimation of peoples unique, precise circadian clock state has the potential to improve the efficacy of medical treatments and improve human performance

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IBS Biomedical Mathematics Group (BIMAG)
Institute for Basic Science (IBS)
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