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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220304T130000
DTEND;TZID=Asia/Seoul:20220304T140000
DTSTAMP:20260427T001038
CREATED:20220224T190000Z
LAST-MODIFIED:20220224T015333Z
UID:5556-1646398800-1646402400@www.ibs.re.kr
SUMMARY:Modeling polypharmacy side effects with graph convolutional networks
DESCRIPTION:We will discuss about “Modeling polypharmacy side effects with graph convolutional networks”\, Zitnik\, Agrawal\, and Leskovec\, Bioinformatics\, 2018 \nMotivation\nThe 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. Polypharmacy side effects emerge because of drug-drug interactions\, in which activity of one drug may change\, favorably or unfavorably\, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare\, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity. \nResults\nHere\, we present Decagon\, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions\, drug-protein target interactions and the polypharmacy side effects\, which are represented as drug-drug interactions\, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values\, Decagon can predict the exact side effect\, if any\, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects\, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore\, Decagon models particularly well polypharmacy side effects that have a strong molecular basis\, while on predominantly non-molecular side effects\, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.
URL:https://www.ibs.re.kr/bimag/event/2022-02-25/
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220311T130000
DTEND;TZID=Asia/Seoul:20220311T140000
DTSTAMP:20260427T001038
CREATED:20220303T190000Z
LAST-MODIFIED:20220224T015356Z
UID:5558-1647003600-1647007200@www.ibs.re.kr
SUMMARY:Transcription factor competition facilitates self-sustained oscillations in single gene genetic circuits
DESCRIPTION: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 Hill function\, regardless of the actual promoter architecture. Moreover\, genetic circuits do not operate in isolation and often transcription factors are shared between different promoters. In this work we show how mathematical modelling of genetic feedback loops can be facilitated with a mechanistic fold-change function that takes into account the titration effect caused by competing binding sites for transcription factors. The model shows how the titration effect aids self-sustained oscillations in a minimal genetic feedback loop: a gene that produces its own repressor directly — without cooperative transcription factor binding. The use of delay differential equations leads to a stability contour that predicts whether a genetic feedback loop will show self-sustained oscillations\, even when taking the bursty nature of transcription into account. \n 
URL:https://www.ibs.re.kr/bimag/event/2022-03-04/
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:20220318T130000
DTEND;TZID=Asia/Seoul:20220318T140000
DTSTAMP:20260427T001038
CREATED:20220310T190000Z
LAST-MODIFIED:20220224T015419Z
UID:5560-1647608400-1647612000@www.ibs.re.kr
SUMMARY:Data-driven discovery of coordinates and governing equations
DESCRIPTION: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 dynamics\, balancing model complexity with descriptive ability\, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam’s razor for model discovery. However\, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work\, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus\, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.
URL:https://www.ibs.re.kr/bimag/event/2022-03-11/
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:20220325T130000
DTEND;TZID=Asia/Seoul:20220325T140000
DTSTAMP:20260427T001038
CREATED:20220317T190000Z
LAST-MODIFIED:20220224T015444Z
UID:5562-1648213200-1648216800@www.ibs.re.kr
SUMMARY:Universal structural requirements for maximal robust perfect adaptation in biomolecular networks
DESCRIPTION: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 which we elaborate in this paper for the scenario where constant-in-time disturbances maximally affect network interactions and there is model uncertainty and possible stochasticity in the dynamics. We prove that these structural constraints are primarily characterized by a simple linear-algebraic stoichiometric condition which remains the same for both deterministic and stochastic descriptions of the dynamics. Our results reveal the essential requirements for maximal robust perfect adaptation in biology\, with important implications for both systems and synthetic biology. We exemplify our results through many known examples of robustly adapting networks and we construct new examples of such networks with the aid of our linear-algebraic characterization.
URL:https://www.ibs.re.kr/bimag/event/2022-03-18/
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
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