기초과학연구원 의생명수학그룹
This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) Abstract: Homeostasis is a recurring theme in biology that ensures that regulated variables robustly adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control, a negative feedback strategy that performs mathematical integration to achieve …
We will discuss about “Stochastic reaction networks in dynamic compartment populations”, Duso and Zechner, PNAS, 2020 Abstract: Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies …
We will discuss about “Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation”, Li et. al., Cell Systems, 2018 Abstract Gene regulation is a complex non-equilibrium process. Here, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive, active, and refractory) provides a parsimonious framework for analyzing gene regulation. Our …
We will discuss about “TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data”, Ness-Cohn and Braun, Bioinformatics, 2021 Abstract Motivation: The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implicatins of the circadian clock for …
Abstract: Most organisms exhibit various endogenous oscillating behaviors, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments, studying such periodicity of organisms often utilizes computer experiments relying on ordinary differential equations (ODE) because configuring the internal processes is difficult. Simultaneously utilizing both experiments, however, poses a …
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 …
This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) Abstract: Our current approach to cancer treatment has been largely driven by finding molecular targets, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose (MTD). These therapies generally achieve …
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 …
CANCELED due to unexpected circumstances This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) Abstract: While the presence of immune cells within solid tumours was initially viewed positively, as the host fighting to rid itself of a foreign body, we now know that the tumour can manipulate immune cells so that …
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 …
Abstract This presentation include the following two topics. First of all, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional model to confirm its convergent nature. Next, We also developed a …
This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) Abstract: Questions about the mechanistic operation of biological systems are naturally formulated as stochastic processes, but confronting such models with data can be challenging. In this talk, I describe the essence of the difficulty, highlighting both the technical issues and the importance …
