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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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:20190101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210806T130000
DTEND;TZID=Asia/Seoul:20210806T140000
DTSTAMP:20260423T100259
CREATED:20210801T140652Z
LAST-MODIFIED:20210801T140652Z
UID:4835-1628254800-1628258400@www.ibs.re.kr
SUMMARY:Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation
DESCRIPTION:We will discuss about “Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation”\, Li et. al.\, Cell Systems\, 2018 \nAbstract \nGene 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 theory makes two non-intuitive predictions. First\, for transcription factors (TFs) that regulate transcription burst frequency\, as opposed to amplitude or duration\, weak TF binding is sufficient to elicit strong transcriptional responses. Second\, refractoriness of a gene after a transcription burst enables rapid responses to stimuli. We validate both predictions experimentally by exploiting the natural\, optogenetic-like responsiveness of the Neurospora GATA-type TF White Collar Complex (WCC) to blue light. Further\, we demonstrate that differential regulation of WCC target genes is caused by different gene activation rates\, not different TF occupancy\, and that these rates are tuned by both the core promoter and the distance between TF-binding site and core promoter. In total\, our work demonstrates the relevance of a kinetic\, non-equilibrium framework for understanding transcriptional regulation.
URL:https://www.ibs.re.kr/bimag/event/2021-08-06/
LOCATION:B305 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:20210730T130000
DTEND;TZID=Asia/Seoul:20210730T140000
DTSTAMP:20260423T100259
CREATED:20210726T125859Z
LAST-MODIFIED:20210726T125859Z
UID:4785-1627650000-1627653600@www.ibs.re.kr
SUMMARY:Stochastic reaction networks in dynamic compartment populations
DESCRIPTION:We will discuss about “Stochastic reaction networks in dynamic compartment populations”\, Duso and Zechner\, PNAS\, 2020 \nAbstract: 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 have made progress toward addressing this problem in the context of specific biological systems\, but a general and sufficiently effective approach remains lacking. In this work\, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes\, including subcellular compartmentalization and tissue homeostasis.
URL:https://www.ibs.re.kr/bimag/event/2021-07-30/
LOCATION:B305 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:20210722T130000
DTEND;TZID=Asia/Seoul:20210722T140000
DTSTAMP:20260423T100259
CREATED:20210721T190000Z
LAST-MODIFIED:20210726T125353Z
UID:4754-1626958800-1626962400@www.ibs.re.kr
SUMMARY:Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter
DESCRIPTION: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 \nAbstract \nObjective: 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 on a broad scale. Humancentric lighting can bring circadian-rhythm support using biodynamic lighting solutions that sync light with the time of day. We investigate a method to improve the tracking of individual’s circadian processes. Methods: In literature\, the human circadian physiology has been mathematically modeled using ordinary differential equations\, the state of which can be tracked via the signal processing concept of a Particle Filter. We show that substantial improvements can be made if the estimator not only tracks state variables\, such as the phase and amplitude of the circadian pacemaker\, but also fits specific model parameters to the individual. In particular\, we optimize model parameter τx\, which reflects the intrinsic period of the circadian pacemaker (τ). We show that both state and model parameters can be estimated based on minimally-invasive light exposure measurements and sleep-wake state observations. We also quantify the effect of inaccuracies in sensing. Results: We demonstrate improved performance by estimating τx for every individual\, both with artificially created and human subject data. Prediction accuracy improves with every newly available observation. The estimated τx-s correlate well with the subjects’ chronotypes\, in a similar way as τ correlates. Conclusion: Our results show that individualizing the estimation of model parameters can improve circadian state estimation accuracy. Significance: These findings underscore the potential improvements in personalized models over one-size fits all approaches.
URL:https://www.ibs.re.kr/bimag/event/2021-07-22/
LOCATION:B305 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:20210715T130000
DTEND;TZID=Asia/Seoul:20210715T140000
DTSTAMP:20260423T100259
CREATED:20210713T071946Z
LAST-MODIFIED:20210715T002734Z
UID:4721-1626354000-1626357600@www.ibs.re.kr
SUMMARY:Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions
DESCRIPTION:We will discuss about “Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions”\, Thurley et al.\, Cell Systems\, 2021 \nAbstract: \nCell-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 less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here\, we make use of a framework that treats intracellular signal transduction networks as “black boxes” with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time\, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.
URL:https://www.ibs.re.kr/bimag/event/2021-07-15/
LOCATION:B305 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:20210709T130000
DTEND;TZID=Asia/Seoul:20210709T140000
DTSTAMP:20260423T100259
CREATED:20210705T061640Z
LAST-MODIFIED:20210705T131643Z
UID:4710-1625835600-1625839200@www.ibs.re.kr
SUMMARY:DeepCME: A deep learning framework for solving the Chemical Master Equation
DESCRIPTION:We will discuss about “DeepCME: A deep learning framework for solving the Chemical Master Equation\,” Gupta et al.\, bioRxiv\, 2021 \nStochastic 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 equation is called the chemical master equation (CME)\, and it is a fundamental system of linear ordinary differential equations (ODEs) that describes the evolution of the probability distribution of the random state-vector representing the copy-numbers of all the reacting species. The size of this system is given by the number of states that are accessible by the chemical system\, and for most examples of interest this number is either very large or infinite. Moreover\, approximations that reduce the size of the system by retaining only a finite number of important chemical states (e.g. those with non-negligible probability) result in high-dimensional ODE systems\, even when the number of reacting species is small. Consequently\, accurate numerical solution of the CME is very challenging\, despite the linear nature of the underlying ODEs. One often resorts to estimating the solutions via computationally intensive stochastic simulations. The goal of the present paper is to develop a novel deep-learning approach for solving high-dimensional CMEs by reformulating the stochastic dynamics using Kolmogorov’s backward equation. The proposed method leverages superior approximation properties of Deep Neural Networks (DNNs) and is algorithmically based on reinforcement learning. It only requires a moderate number of stochastic simulations (in comparison to typical simulation-based approaches) to train the “policy function”. This allows not just the numerical approximation of the CME solution but also of its sensitivities to all the reaction network parameters (e.g. rate constants). We provide four examples to illustrate our methodology and provide several directions for future research.
URL:https://www.ibs.re.kr/bimag/event/2021-07-09/
LOCATION:B305 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:20210702T120000
DTEND;TZID=Asia/Seoul:20210702T130000
DTSTAMP:20260423T100259
CREATED:20210507T124154Z
LAST-MODIFIED:20210622T234621Z
UID:4550-1625227200-1625230800@www.ibs.re.kr
SUMMARY:Collective Oscillations in coupled cell systems
DESCRIPTION:We will discuss about “Collective Oscillations in coupled cell systems”\, Chen and Sinh\, Bulletin of Mathematical Biology\, 2021 \nWe 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 an approach\, we analyze a single-cell system modeling the minimal genetic negative feedback loop and the coupled-cell system composed by these single-cell systems. We study the oscillatory properties for these systems and compare these properties between the model with Hill-type repression and the one with protein-sequestration-based repression. As the parameters move from the Hopf bifurcation value for single cells to the one for coupled cells\, we compute the eigenvalues of the linearized systems to obtain the magnitude of the collective frequency when the periodic solution of the coupled-cell system is generated. Extending from this information on the parameter values\, we further compute and compare the collective frequency for the coupled-cell system and the average frequency of the decoupled individual cells. To compare these scenarios with other biological oscillators\, we perform parallel analysis and computations on a segmentation clock model.
URL:https://www.ibs.re.kr/bimag/event/2021-07-02/
LOCATION:B305 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:20210611T123000
DTEND;TZID=Asia/Seoul:20210611T133000
DTSTAMP:20260423T100259
CREATED:20210507T123416Z
LAST-MODIFIED:20210601T035036Z
UID:4545-1623414600-1623418200@www.ibs.re.kr
SUMMARY:DNA as a universal substrate for chemical kinetics
DESCRIPTION:We will discuss about “DNA as a universal substrate for chemical kinetics “\, Soloveichik et al.\, PNAS (2009) \nMolecular 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 systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive\, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously\, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator\, a limit-cycle oscillator\, a chaotic system\, and systems implementing feedback digital logic and algorithmic behavior.
URL:https://www.ibs.re.kr/bimag/event/2021-05-27/
LOCATION:B305 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:20210520T123000
DTEND;TZID=Asia/Seoul:20210520T133000
DTSTAMP:20260423T100259
CREATED:20210507T123654Z
LAST-MODIFIED:20210507T123746Z
UID:4547-1621513800-1621517400@www.ibs.re.kr
SUMMARY:Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes
DESCRIPTION:We will discuss about “Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes”\, Hempel et. al.\, bioRxiv\, 2021 \nIn 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 simplified models of molecular kinetics on long timescales. Despite its success\, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes\, the number of independent or weakly coupled subsystems increases\, and the number of global system states increase exponentially\, making the sampling of all distinct global states unfeasible. In this work\, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology\, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.
URL:https://www.ibs.re.kr/bimag/event/2021-05-20/
LOCATION:B305 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:20210514T110000
DTEND;TZID=Asia/Seoul:20210514T120000
DTSTAMP:20260423T100259
CREATED:20210507T124508Z
LAST-MODIFIED:20210507T124508Z
UID:4555-1620990000-1620993600@www.ibs.re.kr
SUMMARY:Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model
DESCRIPTION:We will discuss about “Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model”\, Ito et. al.\, PloS ONE\, 2011 \nTransfer 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 computationally intensive\, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic\, as synaptic delays between cortical neurons\, for example\, range from one to tens of milliseconds. In addition\, neurons produce bursts of spikes spanning multiple time bins. To address these issues\, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance\, we applied these extensions of TE to a spiking cortical network model (Izhikevich\, 2006) with known connectivity and a range of synaptic delays. For comparison\, we also investigated single-delay TE\, at a message length of one bin (D1TE)\, and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE\, this dramatically improved to 73% of true connections. In addition\, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE\, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE\, when used on currently available desktop computers\, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
URL:https://www.ibs.re.kr/bimag/event/2021-05-14/
LOCATION:B305 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:20210507T123000
DTEND;TZID=Asia/Seoul:20210507T133000
DTSTAMP:20260423T100259
CREATED:20210503T075749Z
LAST-MODIFIED:20210503T075749Z
UID:4525-1620390600-1620394200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 2
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nThe 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. However\, if the underlying graphical model is too complex or the data is in very high dimensions\, then such sampling-based methodologies run into several problems. Variational inference (Jordan et al.\, 1999; Wainwright and Jordan\, 2008) is a family of machine learning methodologies that transforms the problem of approximating posterior densities to an optimization\, which lets us circumvent all such problems. In the first part\, I will introduce the general framework of variational inference and some underlying theory\, accompanied by an illustrative example of LDA (Blei et al.\, 2003). In the second part\, I will introduce some recent works on applying variational inference to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-2/
LOCATION:B305 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:20210429T120000
DTEND;TZID=Asia/Seoul:20210429T130000
DTSTAMP:20260423T100259
CREATED:20210425T180554Z
LAST-MODIFIED:20210425T180554Z
UID:4499-1619697600-1619701200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 1
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nGaussian 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 over functions (Rasmussen and Williams\, 2006)\, hence providing a nonparametric approach to various tasks. In the first part\, I will introduce the general framework of GP and some underlying theory\, accompanied by an illustrative example of GP regression\, also known as Kringing. In the second part\, I will introduce some recent works on applying GP to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-1/
LOCATION:B305 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:20210422T120000
DTEND;TZID=Asia/Seoul:20210422T130000
DTSTAMP:20260423T100259
CREATED:20210417T101617Z
LAST-MODIFIED:20210419T021327Z
UID:4477-1619092800-1619096400@www.ibs.re.kr
SUMMARY:A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics
DESCRIPTION:We will discuss about “A Simple and Flexible Computational Framework for\nInferring Sources of Heterogeneity from Single-Cell\nDynamics”\, Dharmarajan et al.\, Cell Systems (2019) \nSingle-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 challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple\, flexible\, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks\, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics
URL:https://www.ibs.re.kr/bimag/event/2021-04-22/
LOCATION:B305 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:20210416T120000
DTEND;TZID=Asia/Seoul:20210416T130000
DTSTAMP:20260423T100259
CREATED:20210412T110458Z
LAST-MODIFIED:20210412T110458Z
UID:4423-1618574400-1618578000@www.ibs.re.kr
SUMMARY:Synthetic multistability in mammalian cells
DESCRIPTION:We will discuss about “Synthetic multistability in mammalian cells”\, Zhu et al.\, bioRxiv (2021) \nIn 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 interactions among cells in distinct states. Here we introduce MultiFate\, a naturally-inspired\, synthetic circuit that supports long-term\, controllable\, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using model-based design\, we engineered MultiFate circuits that generate up to seven states\, each stable for at least 18 days. MultiFate permits controlled state-switching and modulation of state stability through external inputs\, and can be easily expanded with additional transcription factors. Together\, these results provide a foundation for engineering multicellular behaviors in mammalian cells. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-16/
LOCATION:B305 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:20210409T120000
DTEND;TZID=Asia/Seoul:20210409T130000
DTSTAMP:20260423T100259
CREATED:20210323T105030Z
LAST-MODIFIED:20210407T041048Z
UID:4304-1617969600-1617973200@www.ibs.re.kr
SUMMARY:Highly accurate fluorogenic DNA sequencing with information theory–based error correction
DESCRIPTION:We will discuss about “Highly accurate fluorogenic DNA sequencing with information theory–based error correction”\, Chen et al.\, Nature Biotechnology (2017) \nEliminating 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 embeds redundancy in sequencing reads by creating three orthogonal degenerate sequences\, generated by alternate dual-base reactions. This is similar to encoding and decoding strategies that have proved effective in detecting and correcting errors in information communication and storage. We show that\, when combined with a fluorogenic SBS chemistry with raw accuracy of 98.1%\, ECC sequencing provides single-end\, error-free sequences up to 200 bp. ECC approaches should enable accurate identification of extremely rare genomic variations in various applications in biology and medicine. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-09/
LOCATION:B305 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:20210401T120000
DTEND;TZID=Asia/Seoul:20210401T130000
DTSTAMP:20260423T100259
CREATED:20210331T003338Z
LAST-MODIFIED:20210406T075108Z
UID:4352-1617278400-1617282000@www.ibs.re.kr
SUMMARY:Yun Min Song\, A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light
DESCRIPTION:We will discuss about “A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light”\, Kumpost et al.\, bioRxiv (2021) \nThe circadian clock is a cellular mechanism that synchronizes various biological processes with respect to the time of the day. While much progress has been made characterizing the molecular mechanisms underlying this clock\, it is less clear how external light cues influence the dynamics of the core clock mechanism and thereby entrain it with the light-dark cycle. Zebrafish-derived cell cultures possess clocks that are directly light-entrainable\, thus providing an attractive laboratory model for circadian entrainment. Here\, we have developed a stochastic oscillator model of the zebrafish circadian clock\, which accounts for the core clock negative feedback loop\, light input\, and the proliferation of single-cell oscillator noise into population-level luminescence recordings. The model accurately predicts the entrainment dynamics observed in bioluminescent clock reporter assays upon exposure to a wide range of lighting conditions. Furthermore\, we have applied the model to obtain refitted parameter sets for cell cultures exposed to a variety of pharmacological treatments and predict changes in single-cell oscillator parameters. Our work paves the way for model-based\, large-scale screens for genetic or pharmacologically-induced modifications to the entrainment of circadian clock function.
URL:https://www.ibs.re.kr/bimag/event/2021-04-02/
LOCATION:B305 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:20210319T113000
DTEND;TZID=Asia/Seoul:20210319T130000
DTSTAMP:20260423T100259
CREATED:20210312T062049Z
LAST-MODIFIED:20210406T075219Z
UID:4254-1616153400-1616158800@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Unified rational protein engineering with sequence-based deep representation learning
DESCRIPTION:In this presentation\, we are going to discuss the paper\, “Unified rational protein engineering with sequence-based deep representation learning” \nAbstract\nRational protein engineering requires a holistic understanding of protein function. Here\, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally\, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins\, and the quantitative function of molecularly diverse mutants\, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
URL:https://www.ibs.re.kr/bimag/event/2021-03-19/
LOCATION:Tea Room\, IBS\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210312T113000
DTEND;TZID=Asia/Seoul:20210312T130000
DTSTAMP:20260423T100259
CREATED:20210305T084406Z
LAST-MODIFIED:20210406T075224Z
UID:4227-1615548600-1615554000@www.ibs.re.kr
SUMMARY:Dae Wook Kim\, Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks
DESCRIPTION:We will discuss about “Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks”\, Dixit et al.\, Cell Systems (2020) \nPredictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However\, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here\, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-03-12/
LOCATION:Tea Room\, IBS\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210305T130000
DTEND;TZID=Asia/Seoul:20210305T140000
DTSTAMP:20260423T100259
CREATED:20210228T074756Z
LAST-MODIFIED:20210406T075234Z
UID:4157-1614949200-1614952800@www.ibs.re.kr
SUMMARY:Eui Min Jeong\, Pairing of segmentation clock genes drives robust pattern formation
DESCRIPTION:We will discuss about “Pairing of segmentation clock genes drives robust pattern formation”\, Zinani et al.\, Nature (2021) \nGene expression is an inherently stochastic process; however\, organismal development and homeostasis require cells to coordinate the spatiotemporal expression of large sets of genes. In metazoans\, pairs of co-expressed genes often reside in the same chromosomal neighbourhood\, with gene pairs representing 10 to 50% of all genes\, depending on the species. Because shared upstream regulators can ensure correlated gene expression\, the selective advantage of maintaining adjacent gene pairs remains unknown6. Here\, using two linked zebrafish segmentation clock genes\, her1 and her7\, and combining single-cell transcript counting\, genetic engineering\, real-time imaging and computational modelling\, we show that gene pairing boosts correlated transcription and provides phenotypic robustness for the formation of developmental patterns. Our results demonstrate that the prevention of gene pairing disrupts oscillations and segmentation\, and the linkage of her1 and her7 is essential for the development of the body axis in zebrafish embryos. We predict that gene pairing may be similarly advantageous in other organisms\, and our findings could lead to the engineering of precise synthetic clocks in embryos and organoids \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-03-05/
LOCATION:Tea Room\, IBS\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210204T130000
DTEND;TZID=Asia/Seoul:20210204T150000
DTSTAMP:20260423T100259
CREATED:20210223T091012Z
LAST-MODIFIED:20210228T073227Z
UID:3968-1612443600-1612450800@www.ibs.re.kr
SUMMARY:Hyukpyo Hong\, Frequency Spectra and the Color of Cellular Noise
DESCRIPTION:We will discuss about “Frequency Spectra and the Color of Cellular Noise”\,  bioRxiv (2020). \nThe invention of the Fourier integral in the 19th century laid the foundation for modern spectral analysis methods. By decomposing a (time) signal into its essential frequency components\, these methods uncovered deep insights into the signal and its generating process\, precipitating tremendous inventions and discoveries in many fields of engineering\, technology\, and physical science. In systems and synthetic biology\, however\, the impact of frequency methods has been far more limited despite their huge promise. This is in large part due to the difficulty of gleaning spectral information from single-cell trajectories\, owing to their distinctive noisy character forged by the underlying discrete stochastic dynamics of the living cell\, typically modelled as a continuous-time Markov chain (CTMC). Here we draw on stochastic process theory to develop a spectral theory and computational methodologies tailored specifically to the computation and analysis of frequency spectra of noisy cellular networks. For linear networks we present exact expressions for the frequency spectrum and use them to decompose the variability of a signal into its sources. For nonlinear networks\, we develop methods to obtain accurate Padé approximants of the spectrum from a single Monte Carlo trajectory simulation. Our results provide new conceptual and practical methods for the analysis and design of noisy cellular networks based on their output frequency spectra. We illustrate this through diverse case studies in which we show that the single-cell frequency spectrum enables topology discrimination\, synthetic oscillator optimization\, cybergenetic controller design\, and systematic investigation of stochastic entrainment. \n 
URL:https://www.ibs.re.kr/bimag/event/2021-02-04/
LOCATION:KAIST E2-1 room 3221\, E2-1 building\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210129T140000
DTEND;TZID=Asia/Seoul:20210129T160000
DTSTAMP:20260423T100259
CREATED:20210223T092935Z
LAST-MODIFIED:20210406T075248Z
UID:3978-1611928800-1611936000@www.ibs.re.kr
SUMMARY:Yun Min Song\, On the quasi-steady-state approximation in an open Michaelis-Menten reaction mechanism
DESCRIPTION:We will discuss about “On the quasi-steady-state approximation in an open Michaelis-Menten reaction mechanism”\, bioRxiv (2021). \nThe conditions for the validity of the standard quasi-steady-state approximation in the Michaelis–Menten mechanism in a closed reaction vessel have been well studied\, but much less so the conditions for the validity of this approximation for the system with substrate inflow. We analyze quasi-steady-state scenarios for the open system attributable to singular perturbations\, as well as less restrictive conditions. For both settings we obtain distinguished invariant slow manifolds and time scale estimates\, and we highlight the special role of singular perturbation parameters in higher order approximations of slow manifolds. We close the paper with a discussion of distinguished invariant manifolds in the global phase portrait. \n 
URL:https://www.ibs.re.kr/bimag/event/2021-01-29/
LOCATION:KAIST E2-1 room 3221\, E2-1 building\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210121T140000
DTEND;TZID=Asia/Seoul:20210121T160000
DTSTAMP:20260423T100259
CREATED:20210223T094006Z
LAST-MODIFIED:20210406T075136Z
UID:3980-1611237600-1611244800@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression
DESCRIPTION:We will discuss about “Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression”\, Benzinger et al.\, bioRxiv (2021) \nCells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However\, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly understood. Here\, we devise networked optogenetic pathways that achieve novel dynamic signal processing functions that recapitulate cellular information processing. Exploiting light-responsive transcriptional regulators with differing response kinetics\, we build a falling-edge pulse-detector and show that this circuit can be employed to demultiplex dynamically encoded signals. We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders. Applying information theory\, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state. Finally\, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway. Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications. \n 
URL:https://www.ibs.re.kr/bimag/event/2021-01-21_1/
LOCATION:KAIST E2-1 room 3221\, E2-1 building\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20201224T130000
DTEND;TZID=Asia/Seoul:20201224T140000
DTSTAMP:20260423T100259
CREATED:20210223T094304Z
LAST-MODIFIED:20210406T075331Z
UID:3983-1608814800-1608818400@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Ligand-receptor promiscuity enables cellular addressing
DESCRIPTION:We will discuss about “Ligand-receptor promiscuity enables cellular addressing”\, Su et al.\, bioRxiv (2021) \nIn multicellular organisms\, secreted ligands selectively activate\, or “address\,” specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors\, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue\, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands\, acting in combinations\, to address a larger number of individual cell types\, each defined by its receptor expression profile. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capacity. Combinatorial addressing extends to groups of cell types\, is robust to receptor expression noise\, grows more powerful with increasing receptor multiplicity\, and is maximized by specific biochemical parameter relationships. Together\, these results identify fundamental design principles governing cell addressing by ligand combinations.
URL:https://www.ibs.re.kr/bimag/event/2020-12-24_1/
LOCATION:KAIST E2-1 room 3221\, E2-1 building\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20201224T020000
DTEND;TZID=Asia/Seoul:20201224T150000
DTSTAMP:20260423T100259
CREATED:20210223T094556Z
LAST-MODIFIED:20210406T075337Z
UID:3985-1608775200-1608822000@www.ibs.re.kr
SUMMARY:Dae Wook Kim\, Neural network aided approximation and parameter inference of stochastic models of gene expression
DESCRIPTION:We will discuss about “Neural network aided approximation and parameter inference of stochastic models of gene expression”\, Jian et al.\, bioRxiv (2020). \nNon-Markov models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models\, as well as the inference of their parameters from data\, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markov models by the solutions of much simpler time-inhomogeneous Markov models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markov model. We show using a variety of models\, where the delays stem from transcriptional processes and feedback control\, that the Markov models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.
URL:https://www.ibs.re.kr/bimag/event/2020-12-24_2/
LOCATION:KAIST E2-1 room 3221\, E2-1 building\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR