BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220705T100000
DTEND;TZID=Asia/Seoul:20220705T110000
DTSTAMP:20260425T012929
CREATED:20220704T160000Z
LAST-MODIFIED:20220704T035619Z
UID:6122-1657015200-1657018800@www.ibs.re.kr
SUMMARY:AI Pontryagin or how artificial neural networks learn to control dynamical systems
DESCRIPTION: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). \nAbstract: 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 constraints play a significant role. Although such optimal control problems can be formulated within the framework of variational calculus\, their solution for complex systems is often analytically and computationally intractable. To overcome this outstanding challenge\, we present AI Pontryagin\, a versatile control framework based on neural ordinary differential equations that automatically learns control signals that steer high-dimensional dynamical systems towards a desired target state within a specified time interval. We demonstrate the ability of AI Pontryagin to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. Our results suggest that AI Pontryagin is capable of solving a wide range of control and optimization problems\, including those that are analytically intractable
URL:https://www.ibs.re.kr/bimag/event/2022-07-05-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220705T160000
DTEND;TZID=Asia/Seoul:20220705T170000
DTSTAMP:20260425T012929
CREATED:20220704T220000Z
LAST-MODIFIED:20220625T051518Z
UID:6240-1657036800-1657040400@www.ibs.re.kr
SUMMARY:TENET+: a tool for reconstructing gene networks by integrating single cell expression and chromatin accessibility data
DESCRIPTION:Reconstruction of gene regulatory networks (GRNs) is a powerful approach to capture a prioritized gene set controlling cellular processes. In our previous study\, we developed TENET a GRN reconstructor from single cell RNA sequencing (scRNAseq). TENET has a superior capability to identify key regulators compared with other algorithms. However\, accurate inference of gene regulation is still challenging. Here\, we suggest an integrative strategy called TENET+ by combining single cell transcriptome and chromatin accessibility data. By applying TENET+ to a paired scRNAseq and scATACseq dataset of human peripheral blood mononuclear cells\, we found critical regulators and their epigenetic regulations for the differentiations of CD4 T cells\, CD8 T cells\, B cells and monocytes. Interestingly\, TENET+ predicted LRRFIP1 and ZBTB16 as top regulators of CD4 and CD8 T cells which were not predicted in a motif-based tool SCENIC. In sum\, TENET+ is a tool predicting epigenetic gene regulatory programs in unbiased way\, suggesting that novel epigenetic regulations can be identified by TENET+.
URL:https://www.ibs.re.kr/bimag/event/2022-07-05-seminar/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220708T130000
DTEND;TZID=Asia/Seoul:20220708T140000
DTSTAMP:20260425T012929
CREATED:20220707T190000Z
LAST-MODIFIED:20220629T005506Z
UID:6246-1657285200-1657288800@www.ibs.re.kr
SUMMARY:Chemical Organisation Theory
DESCRIPTION:We will discuss about “Chemical Organisation Theory\n“\, Dittrich\, Peter\, and Pietro Speroni Di Fenizio\, Bulletin of mathematical biology 69.4 (2007): 1199-1231. \nAbstract: Complex dynamical reaction networks consisting of many components that interact and produce each other are difficult to understand\, especially\, when new component types may appear and present component types may vanish completely. Inspired by Fontana and Buss (Bull. Math. Biol.\, 56\, 1–64) we outline a theory to deal with such systems. The theory consists of two parts. The first part introduces the concept of a chemical organisation as a closed and self-maintaining set of components. This concept allows to map a complex (reaction) network to the set of organisations\, providing a new view on the system’s structure. The second part connects dynamics with the set of organisations\, which allows to map a movement of the system in state space to a movement in the set of organisations. The relevancy of our theory is underlined by a theorem that says that given a differential equation describing the chemical dynamics of the network\, then every stationary state is an instance of an organisation. For demonstration\, the theory is applied to a small model of HIV-immune system interaction by Wodarz and Nowak (Proc. Natl. Acad. USA\, 96\, 14464–14469) and to a large model of the sugar metabolism of E. Coli by Puchalka and Kierzek (Biophys. J.\, 86\, 1357–1372). In both cases organisations where uncovered\, which could be related to functions.
URL:https://www.ibs.re.kr/bimag/event/2022-07-08-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220722T130000
DTEND;TZID=Asia/Seoul:20220722T140000
DTSTAMP:20260425T012929
CREATED:20220629T010032Z
LAST-MODIFIED:20220629T010032Z
UID:6248-1658494800-1658498400@www.ibs.re.kr
SUMMARY:Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells
DESCRIPTION:We will discuss about “Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells”\, Jia\, Chen\, and Ramon Grima\, bioRxiv (2022). \nAbstract: The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA\, and switching of the gene between active and inactive states. While commonly used\, this model does not describe how fluctuations are influenced by the cell cycle phase\, cellular growth and division\, and other crucial aspects of cellular biology. Here we derive the analytical time-dependent solution of a stochastic model that explicitly considers various sources of intrinsic and extrinsic noise: switching between inactive and active states\, doubling of gene copy numbers upon DNA replication\, dependence of the mRNA synthesis rate on cellular volume\, gene dosage compensation\, partitioning of molecules during cell division\, cell-cycle duration variability\, and cell-size control strategies. We show that generally the analytical distribution of transcript numbers in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models\, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak\, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.
URL:https://www.ibs.re.kr/bimag/event/2022-07-22-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220729T130000
DTEND;TZID=Asia/Seoul:20220729T140000
DTSTAMP:20260425T012929
CREATED:20220728T190000Z
LAST-MODIFIED:20220728T085252Z
UID:6250-1659099600-1659103200@www.ibs.re.kr
SUMMARY:Learning stable and predictive structures in kinetic systems
DESCRIPTION:We will discuss about “Learning stable and predictive structures in kinetic systems”\, Niklas Pfister \, Stefan Bauer\, and Jonas Peters. PNAS\, 2019 \nAbstract: Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework\, called CausalKinetiX\, that identifies structure from discrete time\, noisy observations\, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying\, invariant kinetic model\, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well-established approaches focusing solely on predictive performance\, especially for out-of-sample generalization.
URL:https://www.ibs.re.kr/bimag/event/2022-07-29-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR