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BEGIN:VEVENT
SUMMARY:Daniel Forger (University of Michigan)
DTSTART:20210325T020000Z
DTEND:20210325T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/1
DESCRIPTION:Title: The mathematics of the wearables with applications to circadi
an rhythms and sleep\nby Daniel Forger (University of Michigan) as par
t of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nMillions
of individuals track their steps\, heart rate\, and other physiological si
gnals through wearables. This data scale is unprecedented\; I will describ
e several of our apps and ongoing studies\, each of which collects wearabl
e and mobile data from thousands of users\, even in > 100 countries. This
data is so noisy that it often seems unusable and in desperate need of new
mathematical techniques to extract key signals used in the (ode) mathemat
ical modeling typically done in mathematical biology. I will describe seve
ral techniques we have developed to analyze this data and simulate models\
, including gap orthogonalized least squares\, a new ansatz for coupled os
cillators\, which is similar to the popular ansatz by Ott and Antonsen\, b
ut which gives better fits to biological data and a new level-set Kalman F
ilter that can be used to simulate population densities. My focus applicat
ions will be determining the phase of circadian rhythms\, the scoring of s
leep and the detection of COVID with wearables.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ramon Grima (University of Edinburgh)
DTSTART:20210526T080000Z
DTEND:20210526T090000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/2
DESCRIPTION:Title: Neural network aided approximation and parameter inference of
stochastic models of gene expression\nby Ramon Grima (University of E
dinburgh) as part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbs
tract\nNon-Markov models of stochastic biochemical kinetics often incorpor
ate explicit time delays to effectively model large numbers of intermediat
e biochemical processes. Analysis and simulation of these models\, as well
as the inference of their parameters from data\, are fraught with difficu
lties because the dynamics depends on the system’s history. Here we use
an artificial neural network to approximate the time-dependent distributio
ns of non-Markov models by the solutions of much simpler time-inhomogeneou
s Markov models\; the approximation does not increase the dimensionality o
f the model and simultaneously leads to inference of the kinetic parameter
s. 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 Marko
v models learnt by the neural network accurately reflect the stochastic dy
namics across parameter space.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Luonan Chen (Shanghai Institutes for Biological Sciences)
DTSTART:20210415T020000Z
DTEND:20210415T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/3
DESCRIPTION:Title: Dynamics-based data science in biology\nby Luonan Chen (S
hanghai Institutes for Biological Sciences) as part of IBS Biomedical Math
ematics Online Colloquium\n\n\nAbstract\nLife science has been a prosperou
s subject for a long time\, and is still developing with high speed now. O
ne of its major aims is to study the mechanisms of various biological proc
esses on the basis of biological big-data. Many statistics-based methods h
ave been proposed to catch the essence by mining those data\, including th
e popular category classification\, variables regression\, group clusterin
g\, statistical comparison\, dimensionality reduction\, and component anal
ysis\, which\, however\, mainly elucidate static features or steady behavi
or of living organisms due to lack of temporal data. But\, a biological sy
stem is inherently dynamic\, and with increasingly accumulated time-series
data\, dynamics-based approaches based on physical and biological laws ar
e demanded to reveal dynamic features or complex behavior of biological sy
stems.\nIn this talk\, I will present a new concept "dynamics-based data s
cience" and the approaches for studying dynamical bio-processes\, includin
g dynamical network biomarkers (DNB)\, autoreservoir neural networks (ARNN
) and partical cross-mapping. These methods are all data-driven or model-f
ree approaches but based on the theoretical frameworks of nonlinear dynami
cs. We show the principles and advantages of dynamics-based data-driven ap
proaches as explicable\, quantifiable\, and generalizable. In particular\,
dynamics-based data science approaches exploit the essential features of
dynamical systems in terms of data\, e.g. strong fluctuations near a bifur
cation point\, low-dimensionality of a center manifold or an attractor\, a
nd phase-space reconstruction from a single variable by delay embedding th
eorem\, and thus are able to provide different or additional information t
o the traditional approaches\, i.e. statistics-based data science approach
es. The dynamical-based data science approaches will further play an impor
tant role in the systematical research of biology and medicine in future.\
n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Olaf Wolkenhauer (University of Rostock)
DTSTART:20210421T080000Z
DTEND:20210421T093000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/4
DESCRIPTION:Title: Advice to my younger self\nby Olaf Wolkenhauer (Universit
y of Rostock) as part of IBS Biomedical Mathematics Online Colloquium\n\n\
nAbstract\nAge brings the benefit of experience and looking back at my job
as a professor\, there are a couple of things that fall into the category
“I wish someone had told me that earlier”. In this seminar\, I would
like to share some of the things I learned and which\, I hope\, will be us
eful for younger scientists.\n\nThe questions I will touch upon include\n\
n- What is productivity\, for a scientist?\n\n- What are qualities of succ
essful people?\n\n- How can one create motivation and success?\n\n- How to
organize myself? (project management\; getting things done)\n\n- How to c
ommunicate effectively?\n\n- Seeking fulfillment\n\nThe seminar is targete
d at PhD students\, postdocs\, and junior group leaders.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Andrew Phillpis (Monash University)
DTSTART:20210610T020000Z
DTEND:20210610T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/5
DESCRIPTION:Title: Towards individualized predictions of human sleep and circadi
an timing\nby Andrew Phillpis (Monash University) as part of IBS Biome
dical Mathematics Online Colloquium\n\n\nAbstract\nAccurate assessment of
circadian timing is critical to many applications\, including timing of dr
ug delivery\, prediction of neurobehavioral performance\, and optimized sc
heduling of sleep. Current methods for measuring circadian timing are oner
ous and do not produce results in real time. Mathematical models have been
developed for predicting circadian timing from an individual’s light ex
posure patterns\, which can be applied to passively collected data. These
models are now well validated in the field at the group-average level\, bu
t tend to perform poorly at the individual level. One potential solution t
o this problem is the estimation of model parameters at an individual leve
l. We explored whether this approach could be applied to parameters relati
ng to an individual’s light sensitivity. We found that these parameters
can account for inter-individual and intra-individual variation in circadi
an timing. These findings demonstrate that model parametrization based on
physiological measurements of light sensitivity could lead to more accurat
e individual-level circadian phase prediction.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bärbel Finkenstädt Rand (University of Warwick)
DTSTART:20210714T080000Z
DTEND:20210714T090000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/6
DESCRIPTION:Title: Inference for Circadian Pacemaking\nby Bärbel Finkenstä
dt Rand (University of Warwick) as part of IBS Biomedical Mathematics Onli
ne Colloquium\n\n\nAbstract\nOrganisms have evolved an internal biological
clock which allows them to temporally regulate and organize their physiol
ogical and behavioral responses to cope in an optimal way with the fundame
ntally periodic nature of the environment. It is now well established that
the molecular genetics of such rhythms within the cell consist of interwo
ven transcriptional-translational feedback loops involving about 15 clock
genes\, which generate circa 24-h oscillations in many cellular functions
at cell population or whole organism levels. We will present statistical m
ethods and modelling approaches that address newly emerging large circadia
n data sets\, namely spatio-temporal gene expression in SCN neurons and re
st-activity actigraph data obtained from non-invasive e-monitoring\, both
of which provide unique opportunities for furthering progress in understan
ding the synchronicity of circadian pacemaking and address implications fo
r monitoring patients in chronotherapeutic healthcare.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mustafa Khammash (ETH Zürich)
DTSTART:20210728T080000Z
DTEND:20210728T090000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/7
DESCRIPTION:Title: Theory and design of molecular integral feedback controllers<
/a>\nby Mustafa Khammash (ETH Zürich) as part of IBS Biomedical Mathemati
cs Online Colloquium\n\n\nAbstract\nHomeostasis is a recurring theme in bi
ology that ensures that regulated variables robustly adapt to environmenta
l perturbations. This robust perfect adaptation feature is achieved in nat
ural circuits by using integral control\, a negative feedback strategy tha
t performs mathematical integration to achieve structurally robust regulat
ion. Despite its benefits\, the synthetic realization of integral feedback
in living cells has remained elusive owing to the complexity of the requi
red biological computations. In this talk I will show that there is a sing
le fundamental biomolecular controller topology that realizes integral fe
edback and achieves robust perfect adaptation in arbitrary intracellular n
etworks with noisy dynamics. This adaptation property is guaranteed both f
or the population-average and for the time-average of single cells. On th
e basis of this concept\, I will describe a genetically engineered synthet
ic integral feedback controller in living cells and demonstrate its tunab
ility and adaptation properties. A growth-rate control application in Esc
herichia coli shows the intrinsic capacity of our integral controller to
deliver robustness and highlights its potential use as a versatile control
ler for regulation of biological variables in uncertain networks. These re
sults provide conceptual and practical tools in the area of cybergenetics\
, for engineering synthetic controllers that steer the dynamics of living
systems.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Aaron A. King (University of Michigan)
DTSTART:20210916T020000Z
DTEND:20210916T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/8
DESCRIPTION:Title: Stochastic processes as scientific instruments: efficient inf
erence based on stochastic dynamical systems\nby Aaron A. King (Univer
sity of Michigan) as part of IBS Biomedical Mathematics Online Colloquium\
n\n\nAbstract\nQuestions about the mechanistic operation of biological sys
tems are naturally formulated as stochastic processes\, but confronting su
ch models with data can be challenging. In this talk\, I describe the ess
ence of the difficulty\, highlighting both the technical issues and the im
portance of the “plug-and-play property”. I then illustrate some effe
ctive approaches to efficient inference based on such models. I conclude
by sketching promising new developments and describing some open problems.
\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Helen Byrne (University of Oxford)
DTSTART:20210908T080000Z
DTEND:20210908T090000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/9
DESCRIPTION:Title: [CANCELED] Approaches to understanding tumour-immune interact
ions\nby Helen Byrne (University of Oxford) as part of IBS Biomedical
Mathematics Online Colloquium\n\n\nAbstract\nWhile the presence of immune
cells within solid tumours was initially viewed positively\, as the host f
ighting to rid itself of a foreign body\, we now know that the tumour can
manipulate immune cells so that they promote\, rather than inhibit\, tumou
r growth. Immunotherapy aims to correct for this by boosting and/or restor
ing the normal function of the immune system. Immunotherapy has delivered
some extremely promising results. However\, the complexity of the tumour-i
mmune interactions means that it can be difficult to understand why one pa
tient responds well to immunotherapy while another does not. In this talk\
, we will show how mathematical\, statistical and topological methods can
contribute to resolving this issue and present recent results which illust
rate the complementary insight that different approaches can deliver.\n\nT
his talk has been CANCELED due to unexpected circumstances.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chao Tang (Peking University)
DTSTART:20211021T020000Z
DTEND:20211021T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/10
DESCRIPTION:Title: Scaling in development\nby Chao Tang (Peking University)
as part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nWi
thin a given species\, fluctuations in egg or embryo size is unavoidable.
Despite this\, the gene expression pattern and hence the embryonic structu
re often scale in proportion with the body length. This scaling phenomenon
is very common in development and regeneration\, and has long fascinated
scientists. I will first discuss a generic theoretical framework to show h
ow scaling gene expression pattern can emerge from non-scaling morphogen g
radients. I will then demonstrate that the Drosophila gap gene system achi
eves scaling in a way that is entirely consistent with our theory. Remarka
bly\, a parameter-free model based on the theory quantitatively accounts f
or the gap gene expression pattern in nearly all morphogen mutants. Furthe
rmore\, the regulation logic and the coding/decoding strategy of the gap g
ene system can be revealed. Our work provides a general theoretical framew
ork on a large class of problems where scaling output is induced by non-sc
aling input\, as well as a unified understanding of scaling\, mutants’ b
ehavior and regulation in the Drosophila gap gene and related systems.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Annabelle Ballesta (University of Warwick)
DTSTART:20211027T080000Z
DTEND:20211027T090000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/11
DESCRIPTION:Title: Systems Pharmacology towards Personalized Chronotherapy\
nby Annabelle Ballesta (University of Warwick) as part of IBS Biomedical M
athematics Online Colloquium\n\n\nAbstract\nChronotherapeutics- that is ad
ministering drugs following the patient's biological rhythms over the 24 h
span- may largely impact on both drug toxicities and efficacy in various
pathologies including cancer [1]. However\, recent findings highlight the
critical need of personalizing circadian delivery according to the patient
sex\, genetic background or chronotype. Chronotherapy personalization req
uires to reliably account for the temporal dynamics of molecular pathways
of patient’s response to drug administration [2]. In a context where cli
nical molecular data is usually minimal in individual patients\, multi-sca
le- from preclinical to clinical- systems pharmacology stands as an adapte
d solution to describe gene and protein networks driving circadian rhythms
of treatment efficacy and side effects and allow for the design of person
alized chronotherapies.\n\nSuch a multiscale approach is being undertaken
for personalizing the circadian administration of irinotecan\, one of the
cornerstones of chemotherapies against digestive cancers. Irinotecan molec
ular chronopharmacology was studied at the cellular level in an in vitro/i
n silico investigation. Large transcription rhythms of period T= 28 h 06 m
in (SD 1 h 41 min) moderated drug bioactivation\, detoxification\, transpo
rt\, and target in synchronized Caco-2 colorectal cancer cell cultures. Th
ese molecular rhythms translated into statistically significant changes ac
cording to drug timing in irinotecan pharmacokinetics\, pharmacodynamics\,
and drug-induced apoptosis. Clock silencing through siBMAL1 exposure abla
ted all the chronopharmacology mechanisms. Mathematical modeling highlight
ed circadian bioactivation and detoxification as the most critical determi
nants of irinotecan chronopharmacology [3]. The cellular model of irinotec
an chronoPK-PD was further tested on SW480 and SW620 cell lines\, and conn
ected to a new clock model to investigate the feasibility of irinotecan ti
ming personalization solely based on clock gene expression monitoring (Hes
se\, Martinelli et al.\, under review).\n\nTo step towards the clinics\, o
n one side\, mathematical models of irinotecan\, oxaliplatin and 5-fluorou
racil pharmacokinetics were designed to precisely compute the exposure con
centration of tissue over time after complex chronomodulated drug administ
ration through programmable pumps [4]. On the other side\, we aimed to des
ign a model learning methodology predicting from non-invasively measured c
ircadian biomarkers (e.g. rest-activity\, body temperature\, cortisol\, fo
od intake\, melatonin)\, the patient peripheral circadian clocks and assoc
iated optimal drug timing [5]. We investigated at the molecular scale the
influence of systemic regulators on peripheral clocks in four classes of m
ice (2 strains\, 2 sexes). Best models involved a modulation of either Bma
l1 or Per2 transcription most likely by temperature or nutrient exposure c
ycles. The strengths of systemic regulations were found to be significantl
y different according to mouse sex and genetic background.\n\nReferences \
n\n1. Ballesta\, A.\, et al.\, Systems Chronotherapeutics. Pharmacol Rev\,
2017. 69(2): p. 161-199.\n\n2. Sancar\, A. and R.N. Van Gelder\, Clocks\,
cancer\, and chronochemotherapy. Science\, 2021. 371(6524).\n\n3. Dulong\
, S.\, et al.\, Identification of Circadian Determinants of Cancer Chronot
herapy through In Vitro Chronopharmacology and Mathematical Modeling. Mol
Cancer Ther\, 2015.\n\n4. Hill\, R.J.W.\, et al.\, Optimizing circadian dr
ug infusion schedules towards personalized cancer chronotherapy. PLoS Comp
ut Biol\, 2020. 16(1): p. e1007218.\n\n5. Martinelli\, J.\, et al.\, Model
learning to identify systemic regulators of the peripheral circadian cloc
k. 2021\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lisa J. Fauci (Tulane University)
DTSTART:20211111T020000Z
DTEND:20211111T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/12
DESCRIPTION:Title: Biofluiddynamics of reproduction\nby Lisa J. Fauci (Tula
ne University) as part of IBS Biomedical Mathematics Online Colloquium\n\n
\nAbstract\nFrom fertilization to birth\, successful mammalian reproductio
n relies on interactions of elastic structures with a fluid environment.
Sperm flagella must move through cervical mucus to the uterus and into the
oviduct\, where fertilization occurs. In fact\, some sperm may adhere to
oviductal epithelia\, and must change their pattern of oscillation to esc
ape. In addition\, coordinated beating of oviductal cilia also drive the
flow. Sperm-egg penetration\, transport of the fertilized ovum from the o
viduct to its implantation in the uterus and\, indeed\, birth itself are r
ich examples of elasto-hydrodynamic coupling. We will discuss successes
and challenges in the mathematical and computational modeling of the biofl
uids of reproduction.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jeremy Gunawardena (Harvard University)
DTSTART:20211118T020000Z
DTEND:20211118T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/13
DESCRIPTION:Title: Following the energy in cellular information processing\
nby Jeremy Gunawardena (Harvard University) as part of IBS Biomedical Math
ematics Online Colloquium\n\n\nAbstract\nJohn Hopfield first pointed out t
hat there are barriers - we call them Hopfield barriers - to biological in
formation-processing at thermodynamic equilibrium. I will explain how the
widely-used Hill function with coefficient n is the universal Hopfield bar
rier to the sharpness of binding to n sites. Away from thermodynamic equil
ibrium\, I will describe the challenge of path dependent complexity and
introduce the entropy-production index as a measure of non-equilibrium com
plexity.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ruth Baker (University of Oxford)
DTSTART:20211125T090000Z
DTEND:20211125T100000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/14
DESCRIPTION:Title: Quantitative comparisons between models and data to provide
new insights in cell and developmental biology\nby Ruth Baker (Univers
ity of Oxford) as part of IBS Biomedical Mathematics Online Colloquium\n\n
\nAbstract\nSimple mathematical models have had remarkable successes in bi
ology\, framing how we understand a host of mechanisms and processes. Howe
ver\, with the advent of a host of new experimental technologies\, the las
t ten years has seen an explosion in the amount and types of quantitative
data now being generated. This sets a new challenge for the field – to d
evelop\, calibrate and analyse new models to interpret these data. In this
talk I will use examples relating to intracellular transport and cell mot
ility to showcase how quantitative comparisons between models and data can
help tease apart subtle details of biological mechanisms.\nReferences:\n
• T. P. Prescott\, K. Zhu\, M. Zhao and R. E. Baker (2021). Quantifying
the impact of electric fields on single-cell motility. Biophys. J. In pres
s.\n• J. U. Harrison\, R. M. Parton\, I. Davis and R. E. Baker (2019). T
esting models of mRNA localization reveals robustness regulated by reducin
g transport between cells. Biophys. J. 117(11):2154-2165.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alexander Hoffmann (UCLA)
DTSTART:20211007T020000Z
DTEND:20211007T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/15
DESCRIPTION:Title: A temporal signaling code to specify immune responses\nb
y Alexander Hoffmann (UCLA) as part of IBS Biomedical Mathematics Online C
olloquium\n\n\nAbstract\nImmune sentinel cells must initiate the appropria
te immune response upon sensing the presence of diverse pathogens or immun
e stimuli. To generate stimulus-specific gene expression responses\, immun
e sentinel cells have evolved a temporal code in the dynamics of stimulus
responsive transcription factors. I will present recent works 1) using an
information theoretic approach to identify the codewords\, termed “signa
ling codons”\, 2) using a machine learning approach to characterize thei
r reliability and points of confusion\, and 3) dynamical systems modeling
to characterize the molecular circuits that allow for their encoding. I wi
ll present progress on how the temporal code may be decoded to specify imm
une responses. Further\, I will discuss to what extent such a code may be
harnessed to achieve greater pharmacological specificity when therapeutic
ally targeting pleiotropic signaling hubs. \n\n\nNFκB Signaling: inform
ation theory\, signaling codons\n\nAdelaja\, A.\, Taylor\, B.\, Sheu\, K.M
.\, Liu\, Y.\, Luecke\, S.\, Hoffmann\, A. 2021 Six distinct NFκB signali
ng codons convey discrete information to distinguish stimuli and enable ap
propriate macrophage responses. Immunity\, 54\, pp.916-930. e7. PMID: 3397
9588\n\nTang\, Y.\, Adelaja\, A.\, Ye\, X\, Deeds\, E.\, Wollman\, R.\, Ho
ffmann\, A. 2021. Quantifying information accumulation encoded in the dyna
mics of biochemical signaling. Nature Communications 12\, pp.1-10\n\nDecod
ing signaling codons to specify immune responses\n\nSen S.\, Cheng\, Z.\,
Sheu\, K.\, Chen\, E.Y.H.\, Hoffmann\, A. 2020 Gene Regulatory Strategies
that Decode the Duration of NFkB Dynamics Contribute to LPS- versus TNF-Sp
ecific Gene Expression. Cell Systems\, 10\, pp.1-14. PMID:31972132\, PMC70
47529\n\nCheng\, Q.J.\, Ohta\, S.\, Sheu\, K.M.\, Spreafico\, R.\, Adelaja
\, A.\, Taylor\, B.\, Hoffmann\, A. 2021 NFκB dynamics determine the sti
mulus-specificity of epigenomic reprogramming in macrophages. Science\, 37
2\, pp.1349-1353\; PMID: 34140389.\n\nPharmacologic manipulation of the co
de\n\nBehar\, M.\, Barken\, D.\, Werner\, S.L.\, Hoffmann\, A. 2013 The D
ynamics of Signaling as a Pharmacological Target. Cell\, 155\, pp.448-461
. PMID: 24120141\, PMC3856316\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alexander Anderson (Moffitt Cancer Center)
DTSTART:20210902T010000Z
DTEND:20210902T020000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/16
DESCRIPTION:Title: Exploiting evolution to design better cancer therapies\n
by Alexander Anderson (Moffitt Cancer Center) as part of IBS Biomedical Ma
thematics Online Colloquium\n\n\nAbstract\nOur current approach to cancer
treatment has been largely driven by finding molecular targets\, those pat
ients fortunate enough to have a targetable mutation will receive a fixed
treatment schedule designed to deliver the maximum tolerated dose (MTD). T
hese therapies generally achieve impressive short-term responses\, that un
fortunately give way to treatment resistance and tumor relapse. The import
ance of evolution during both tumor progression\, metastasis and treatment
response is becoming more widely accepted. However\, MTD treatment strat
egies continue to dominate the precision oncology landscape and ignore the
fact that treatments drive the evolution of resistance. Here we present
an integrated theoretical/experimental/clinical approach to develop treatm
ent strategies that specifically embrace cancer evolution. We will conside
r the importance of using treatment response as a critical driver of subse
quent treatment decisions\, rather than fixed strategies that ignore it. W
e will also consider using mathematical models to drive treatment decision
s based on limited clinical data. Through the integrated application of ma
thematical and experimental models as well as clinical data we will illust
rate that\, evolutionary therapy can drive either tumor control or extinct
ion using a combination of drug treatments and drug holidays. Our results
strongly indicate that the future of precision medicine shouldn’t be in
the development of new drugs but rather in the smarter evolutionary\, and
model informed\, application of preexisting ones.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Qing Nie (University of California\, Irvine)
DTSTART:20220303T020000Z
DTEND:20220303T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/17
DESCRIPTION:Title: Spatiotemporal reconstruction of static single-cell genomics
data\nby Qing Nie (University of California\, Irvine) as part of IBS
Biomedical Mathematics Online Colloquium\n\n\nAbstract\nCells make fate de
cisions in response to dynamic environments and multicellular structure em
erges from interplays among cells in space and time. The recent single-cel
l genomics technology provides an unprecedented opportunity to profile cel
ls. However\, those measurements are taken as snapshots for groups of indi
vidual cells with only static information. Can one infer interactions amon
g cells from such datasets? Is it possible to recover spatial information
from non-spatial datasets? How to obtain temporal relationships of cells f
rom the static measurements? In this talk I will present our newly develop
ed computational tools that reconstruct interactions and spatiotemporal re
lationships for cells using single-cell RNA-seq\, ATAC-seq\, and spatial t
ranscriptomics datasets. Through applications of those methods to systems
in development and regeneration\, we show the discovery power of such meth
ods and identify areas for further development in spatiotemporal reconstru
ction.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mason Porter (UCLA)
DTSTART:20220324T013000Z
DTEND:20220324T015500Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/18
DESCRIPTION:Title: Introduction to topological data analysis\nby Mason Port
er (UCLA) as part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbs
tract\nI will give an introduction to topological data analysis (TDA)\, in
which one uses ideas from algebraic topology to study the "shape" of data
. I will focus on persistent homology (PH)\, which is the most common appr
oach in TDA.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mason Porter (UCLA)
DTSTART:20220324T020000Z
DTEND:20220324T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/19
DESCRIPTION:Title: Topological data analysis of spatial systems\nby Mason P
orter (UCLA) as part of IBS Biomedical Mathematics Online Colloquium\n\n\n
Abstract\nFrom the venation patterns of leaves to spider webs\, roads in c
ities\, social networks\, and the spread of COVID-19 infections and vaccin
ations\, the structure of many systems is influenced significantly by spac
e. In this talk\, I will discuss the application of topological data analy
sis (specifically\, persistent homology) to spatial systems. I will presen
t a few examples\, such as voting in presidential elections\, city street
networks\, spatiotemporal dynamics of COVID-19 infections and vaccinations
\, and webs that were spun by spiders under the influence of various drugs
.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Uri Alon (Weizmann Institute of Science)
DTSTART:20220331T020000Z
DTEND:20220331T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/20
DESCRIPTION:Title: Design principles of physiological circuits\nby Uri Alon
(Weizmann Institute of Science) as part of IBS Biomedical Mathematics Onl
ine Colloquium\n\n\nAbstract\nWe will discuss hormone circuits and their d
ynamics using new models that take into account timescales of weeks due to
growth of the hormone glands. This explains some mysteries in diabetes an
d autoimmune disease.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kunihiko Kaneko (The University of Tokyo)
DTSTART:20220407T020000Z
DTEND:20220407T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/21
DESCRIPTION:Title: Universal biology in adaptation and evolution: Dimensional r
eduction\, and fluctuation-response relationship\nby Kunihiko Kaneko (
The University of Tokyo) as part of IBS Biomedical Mathematics Online Coll
oquium\n\n\nAbstract\nA macroscopic theory for cellular states with steady
-growth is presented\, based on consistency between cellular growth and mo
lecular replication\, together with robustness of phenotypes against pertu
rbations. Adaptive changes in high-dimensional phenotypes are shown to be
restricted within a low-dimensional slow manifold\, from which a macroscop
ic law for cellular states is derived\, as is confirmed by adaptation expe
riments of bacteria under stress. The theory is extended to phenotypic evo
lution\, leading to proportionality between phenotypic responses against g
enetic evolution and by environmental adaptation\, which explains the evol
utionary fluctuation-response relationship previously uncovered. \n\nRe
ferences\n\n1)Kaneko K.\, Life: An Introduction to Complex Systems Biology
\, Springer (2006)\n\n2)K. Kaneko\, C.Furusawa\, T. Yomo\, "Macroscopic ph
enomenology for cells in steady-growth state"\, Phys.Rev.X(2015) 011014\n\
n3)C. Furusawa\, K. Kaneko "Global Relationships in Fluctuation and Respon
se in Adaptive Evolution"\, J of Royal Society Interface 12(2015)\, 201504
82.\n\n4)C. Furusawa\, K. Kaneko " Formation of Dominant Mode by Evolution
in Biological Systems” Phys. Rev. E 97(2018)042410\n\n5)K. Kaneko\, C.
Furusawa “Macroscopic Theory for Evolving Biological Systems Akin to The
rmodynamics”\, Annual Rev. Biophys. (2018) 47\, 273-290\n\n6)A. Sakata a
nd K. Kaneko\, “Dimensional Reduction in Evolving Spin-Glass Model: Corr
elation of Phenotypic Responses to Environmental and Mutational Changes”
\, Phys. Rev. Lett. (2020) 124\, 218101\n\n7)Q-Y. Tang and K. Kaneko\, “
Dynamics-evolution correspondence in protein structures”\, Phys. Rev.
Lett. (2021) 127\, 098103\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Denise Kirschner (University of Michigan)
DTSTART:20220414T013000Z
DTEND:20220414T015500Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/22
DESCRIPTION:Title: An overview of methods used for multi-scale modeling and anl
ysis\nby Denise Kirschner (University of Michigan) as part of IBS Biom
edical Mathematics Online Colloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Denise Kirschner (University of Michigan)
DTSTART:20220414T020000Z
DTEND:20220414T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/23
DESCRIPTION:Title: A systems biology approach using multi-scale modeling to und
erstand the immune response to tuberculosis infection and treatment\nb
y Denise Kirschner (University of Michigan) as part of IBS Biomedical Math
ematics Online Colloquium\n\n\nAbstract\nTuberculosis (TB) is one of the w
orld’s deadliest infectious diseases. Caused by the pathogen Mycobacteri
um tuberculosis (Mtb)\, the standard regimen for treating TB consists of t
reatment with multiple antibiotics for at least six months. There are a nu
mber of complicating factors that contribute to the need for this long tre
atment duration and increase the risk of treatment failure. The structure
of granulomas\, lesions forming in lungs in response to Mtb infection\, cr
eate heterogeneous antibiotic distributions that limit antibiotic exposure
to Mtb. We can use a systems biology approach pairing experimental dat
a from non-human primates with computational modeling to represent and pre
dict how factors impact antibiotic regimen efficacy and granuloma bacteria
l sterilization. We utilize an agent-based\, computational model that simu
lates granuloma formation\, function and treatment\, called GranSim. A g
oal in improving antibiotic treatment for TB is to find regimens that can
shorten the time it takes to sterilize granulomas while minimizing the amo
unt of antibiotic required. We also created a whole host model\, called HO
STSIM\, to study Mtb dynamics within a human host. Overall\, we use thes
e models to help better understand TB treatment and strengthen our ability
to predict regimens that can improve clinical treatment of TB.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kun Hu (Harvard University)
DTSTART:20220428T020000Z
DTEND:20220428T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/24
DESCRIPTION:Title: Scaling behaviors in physiological fluctuations: relevance t
o circadian regulation and insights into the development of Alzheimer’s
disease\nby Kun Hu (Harvard University) as part of IBS Biomedical Math
ematics Online Colloquium\n\n\nAbstract\nOutputs from health biological sy
stems display complex fluctuations that are not random but display robust
and often self-similar (fractal) temporal correlations at different time s
cales— scaling behaviors. The scaling behaviors in the fluctuations of b
iological outputs such as neural activities\, cardiac dynamics\, motor act
ivity are believed to be originated from feedbacks within the complex biol
ogical networks\, reflecting the system adaptability to internal and exter
nal inputs. Supporting this concept\, our studies have demonstrated a mech
anistic link between the scaling regulation of physiological fluctuations
and the circadian control system— a result of evolutionary adaptation to
daily environmental light-dark cycles on the earth. In this talk\, I will
discuss certain evidence for this ‘scaling-circadian’ link and its re
lated implications. Moreover\, I will review some recent studies\, in whic
h we examined how the scaling patterns of human motor activity fluctuation
s change with aging and in Alzheimer’s disease. Our results showed that
(1) alterations in scaling activity patterns occur before the clinical man
ifestation of Alzheimer’s disease (i.e.\, cognitive impairment) and pred
ict cognitive decline and the risk for Alzheimer’s dementia\; and (2) th
e progression of Alzheimer’s disease accelerates the aging effect on the
scaling activity patterns. Our work provides strong evidence that altered
scaling activity patterns may also be a risk factor for neurodegeneration
\, playing a role in the development and progression of Alzheimer’s dise
ase.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Krešimir Josić (University of Houston)
DTSTART:20220512T013000Z
DTEND:20220512T015500Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/25
DESCRIPTION:Title: Introduction to balanced networks\nby Krešimir Josić (
University of Houston) as part of IBS Biomedical Mathematics Online Colloq
uium\n\n\nAbstract\nThe idea of balance between excitation and inhibition
is central in the theory of biological neural networks. I will give a b
rief introduction to the concept of such balance\, and an overview of the
mathematical ideas that can be used to study it.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Krešimir Josić (University of Houston)
DTSTART:20220512T020000Z
DTEND:20220512T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/26
DESCRIPTION:Title: Plasticity and balance in neuronal networks\nby Krešimi
r Josić (University of Houston) as part of IBS Biomedical Mathematics Onl
ine Colloquium\n\n\nAbstract\nI will first describe how to extend the theo
ry of balanced networks to account for synaptic plasticity. This theory ca
n be used to show when a plastic network will maintain balance\, and when
it will be driven into an unbalanced state. I will next discuss how this a
pproach provides evidence for a novel form of rapid compensatory inhibitor
y plasticity. Experimental evidence for such plasticity comes from optogen
etic activation of excitatory neurons in primate visual cortex (area V1) w
hich induces a population-wide dynamic reduction in the strength of neuron
al interactions over the timescale of minutes during the awake state\, but
not during rest. I will shift gears in the final part of the talk\, and d
iscuss how community detection algorithms can help uncover the large scale
organization of neuronal networks from connectome data.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Radek Erban (University of Oxford)
DTSTART:20220525T073000Z
DTEND:20220525T075500Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/27
DESCRIPTION:Title: Stochastic modelling of reaction-diffusion processes\nby
Radek Erban (University of Oxford) as part of IBS Biomedical Mathematics
Online Colloquium\n\n\nAbstract\nI will introduce mathematical and computa
tional methods for spatio-temporal modelling in molecular and cell biology
\, including all-atom and coarse-grained molecular dynamics (MD)\, Brownia
n dynamics (BD)\, stochastic reaction-diffusion models and macroscopic mea
n-field equations. Microscopic (BD\, MD) models are based on the simulatio
n of trajectories of individual molecules and their localized interactions
(for example\, reactions). Mesoscopic (lattice-based) stochastic reaction
-diffusion approaches divide the computational domain into a finite number
of compartments and simulate the time evolution of the numbers of molecul
es in each compartment\, while macroscopic models are often written in ter
ms of mean-field reaction-diffusion partial differential equations for spa
tially varying concentrations.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Radek Erban (University of Oxford)
DTSTART:20220525T080000Z
DTEND:20220525T090000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/28
DESCRIPTION:Title: Multi-resolution methods for modelling intracellular process
es\nby Radek Erban (University of Oxford) as part of IBS Biomedical Ma
thematics Online Colloquium\n\n\nAbstract\nI will discuss the development\
, analysis and applications of multi-resolution methods for spatio-tempora
l modelling of intracellular processes\, which use (detailed) Brownian dyn
amics or molecular dynamics simulations in localized regions of particular
interest (in which accuracy and microscopic details are important) and a
(less-detailed) coarser model in other regions in which accuracy may be tr
aded for simulation efficiency. I will discuss the error analysis and conv
ergence properties of the developed multi-resolution methods\, their softw
are implementation and applications of these multiscale methodologies to m
odelling of intracellular calcium dynamics\, actin dynamics and DNA dynami
cs. I will also discuss the development of multiscale methods which couple
molecular dynamics and coarser stochastic models in the same dynamic simu
lation.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Heinz Koeppl (TU Darmstadt)
DTSTART:20220601T080000Z
DTEND:20220601T090000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/29
DESCRIPTION:Title: From live cell imaging to moment-based variational inference
\nby Heinz Koeppl (TU Darmstadt) as part of IBS Biomedical Mathematics
Online Colloquium\n\n\nAbstract\nQuantitative characterization of biomole
cular networks is important for the analysis and design of network functio
nality. Reliable models of such networks need to account for intrinsic and
extrinsic noise present in the cellular environment. Stochastic kinetic m
odels provide a principled framework for developing quantitatively predict
ive tools in this scenario. Calibration of such models requires an experim
ental setup capable of monitoring a large number of individual cells over
time\, automatic extraction of fluorescence levels for each cell and a sca
lable inference approach. In the first part of the talk we will cover our
microfluidic setup and a deep-learning based approach to cell segmentation
and data extraction. The second part will introduce moment-based variatio
nal inference as a scalable framework for approximate inference of kinetic
models based on single cell data.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:James Ferrell (Standford University)
DTSTART:20220902T020000Z
DTEND:20220902T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/30
DESCRIPTION:Title: Cell signaling in 2D vs. 3D\nby James Ferrell (Standford
University) as part of IBS Biomedical Mathematics Online Colloquium\n\n\n
Abstract\nThe activation of Ras depends upon the translocation of its guan
ine nucleotide exchange factor\, Sos\, to the plasma membrane. Moreover\,
artificially inducing Sos to translocate to the plasma membrane is suffici
ent to bring about Ras activation and activation of Ras’s targets. There
are many other examples of signaling proteins that must translocate to th
e membrane in order to relay a signal.\n\nOne attractive idea is that tran
slocation promotes signaling by bringing a protein closer to its target. H
owever\, proteins that are anchored to the membrane diffuse more slowly th
an cytosolic proteins do\, and it is not clear whether the concentration e
ffect or the diffusion effect would be expected to dominate. Here we have
used a reconstituted\, controllable system to measure the association rate
for the same binding reaction in 3D vs. 2D to see whether association is
promoted\, and\, if so\, how.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/30/
END:VEVENT
BEGIN:VEVENT
SUMMARY:John Tyson (Virginia Tech)
DTSTART:20221007T013000Z
DTEND:20221007T020000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/31
DESCRIPTION:Title: A Dynamic Paradigm for Molecular Cell Biology\nby John T
yson (Virginia Tech) as part of IBS Biomedical Mathematics Online Colloqui
um\n\n\nAbstract\nThe driving passion of molecular cell biologists is to u
nderstand the molecular mechanisms that control important aspects of cell
physiology\, but this ambition is – paradoxically – limited by the ver
y wealth of molecular details currently known about these mechanisms. Thei
r complexity overwhelms our intuitive notions of how molecular regulatory
networks might respond under normal and stressful conditions. To make prog
ress we need a new paradigm for connecting molecular biology to cell physi
ology. I will outline an approach that uses precise mathematical methods t
o associate the qualitative features of dynamical systems\, as conveyed by
‘bifurcation diagrams’\, with ‘signal–response’ curves measured
by cell biologists.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:John Tyson (Virginia Tech)
DTSTART:20221007T020000Z
DTEND:20221007T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/32
DESCRIPTION:Title: Time-keeping and Decision-making in the Cell Cycle\nby J
ohn Tyson (Virginia Tech) as part of IBS Biomedical Mathematics Online Col
loquium\n\n\nAbstract\nCell growth\, DNA replication\, mitosis and divisio
n are the fundamental processes by which life is passed on from one genera
tion of eukaryotic cells to the next. The eukaryotic cell cycle is intrins
ically a periodic process but not so much a ‘clock’ as a ‘copy machi
ne’\, making new daughter cells as warranted. Cells growing under ideal
conditions divide with clock-like regularity\; however\, if they are chall
enged with DNA-damaging agents or mitotic spindle disruptors\, they will n
ot progress to the next stage of the cycle until the damage is repaired. T
hese ‘decisions’ (to exit and re-enter the cell cycle) are essential t
o maintain the integrity of the genome from generation to generation. A cr
ucial challenge for molecular cell biologists in the 1990s was to unravel
the genetic and biochemical mechanisms of cell cycle control in eukaryotes
. Central to this effort were biochemical studies of the clock-like regula
tion of ‘mitosis promoting factor’ during synchronous mitotic cycles o
f fertilized frog eggs and genetic studies of the switch-like regulation o
f ‘cyclin-dependent kinases’ in yeast cells. The complexity of these c
ontrol systems demands a dynamical approach\, as described in the first le
cture. Using mathematical models of the control systems\, I will uncover s
ome of the secrets of cell cycle ‘clocks’ and ‘switches’.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Domitilla Del Vecchio (MIT)
DTSTART:20221202T020000Z
DTEND:20221202T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/33
DESCRIPTION:Title: Mammalian synthetic biology by controller design\nby Dom
itilla Del Vecchio (MIT) as part of IBS Biomedical Mathematics Online Coll
oquium\n\n\nAbstract\nThe ability to reliably engineer the mammalian cell
will impact a variety of applications in a disruptive way\, including cell
fate control and reprogramming\, targeted drug delivery\, and regenerativ
e medicine. However\, our current ability to engineer mammalian genetic c
ircuits that behave as predicted remains limited. These circuits depend on
the intra and extra cellular environment in ways that are difficult to an
ticipate\, and this fact often hampers genetic circuit performance. This l
ack of robustness to poorly known and often variable cellular environment
is the subject of this talk. Specifically\, I will describe control engine
ering approaches that make the performance of genetic devices robust to co
ntext. I will show a feedforward controller that makes gene expression ro
bust to variability in cellular resources and\, more generally\, to change
s in intra-cellular context linked to differences in cell type. I will the
n show a feedback controller that uses bacterial two component signaling s
ystems to create a quasi-integral controller that makes the input/output r
esponse of a genetic device robust to a variety of perturbations that affe
ct gene expression. These solutions support rational and modular design of
sophisticated genetic circuits and can serve for engineering biological c
ircuits that are more robust and predictable across changing contexts.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Anderson (University of Wisconsin-Madison)
DTSTART:20221021T020000Z
DTEND:20221021T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/34
DESCRIPTION:Title: Stationary distributions and positive recurrence of chemical
reaction networks\nby David Anderson (University of Wisconsin-Madison
) as part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nC
ellular\, chemical\, and population processes are all often represented vi
a networks that describe the interactions between the different population
types (typically called the “species”). If the counts of the species
are low\, then these systems are often modeled as continuous-time Markov c
hains on the d-dimensional integer lattice (with d being the number of spe
cies)\, with transition rates determined by stochastic mass-action kinetic
s. A natural (broad) mathematical question is: how do the qualitative prop
erties of the dynamical system relate to the graph properties of the netwo
rk? For example\, it is of particular interest to know which graph propert
ies imply that the stochastically modeled reaction network is positive rec
urrent\, and therefore admits a stationary distribution. After a general i
ntroduction to the models of interest\, I will discuss this problem\, givi
ng some of the known results. I will also discuss recent progress on the C
hemical Recurrence Conjecture\, which has been open for decades\, which is
the following: if each connected component of the network is strongly con
nected\, then the associated stochastic model is positive recurrent.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anne Skeldon (University of Surrey)
DTSTART:20221026T070000Z
DTEND:20221026T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/35
DESCRIPTION:Title: Mathematical Modelling of the sleep-wake cycle: light\, cloc
ks and social rhythms\nby Anne Skeldon (University of Surrey) as part
of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nWe’re all
familiar with sleep\, but how can we mathematically model it? And what de
termines how long and when we sleep? In this talk I’ll introduce the non
smooth coupled oscillator systems that form the basis of current models of
sleep-wake regulation and discuss their dynamical behaviour. I will descr
ibe how we are using models to unravel environmental\, societal and physio
logical factors that determine sleep timing and outline how we are using m
odels to inform the quantitative design of light interventions for mental
health disorders and address contentious societal questions such as whethe
r to move school start time for adolescents.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/35/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marko Okada (Osaka University)
DTSTART:20221109T070000Z
DTEND:20221109T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/36
DESCRIPTION:Title: Modeling cell-to-cell heterogeneity from a signaling network
\nby Marko Okada (Osaka University) as part of IBS Biomedical Mathemat
ics Online Colloquium\n\n\nAbstract\nCells make individual fate decisions
through linear and nonlinear regulation of gene network\, generating diver
se dynamics from a single reaction pathway. In this colloquium\, I will pr
esent two topics of our recent work on signaling dynamics at cellular and
patient levels. The first example is about the initial value of the model\
, as a mechanism to generate different dynamics from a single pathway in c
ancer and the use of the dynamics for stratification of the patients [1-3]
. Models of ErbB receptor signaling have been widely used in prediction of
drug sensitivity for many types of cancers. We trained the ErbB model wit
h the data obtained from cancer cell lines and predicted the common parame
ters of the model. By simulation of the ErbB model with those parameters a
nd individual patient transcriptome data as initial values\, we were able
to classify the prognosis of breast cancer patients and drug sensitivity b
ased on their in silico signaling dynamics. This result raises the questio
n whether gene expression levels\, rather than genetic mutations\, might b
e better suited to classify the disease. Another example is about the regu
lation of transcription factors\, the recipients of signal dynamics\, for
target gene expression [4-6]. By focusing on the NFkB transcription factor
\, we found that the opening and closing of chromatin at the DNA regions o
f the putative transcription factor binding sites and the cooperativity in
their interaction significantly influenced the cell-to cell heterogeneity
in gene expression levels. This study indicates that the noise in gene ex
pression is rather strongly regulated by the DNA side\, even though the si
gnals are similarly regulated in a cell population. Overall these mechanis
ms are important in our understanding the cell as a system for encoding an
d decoding signals for fate decisions and its application to human disease
s.\n\n[References]\n\n[1] Nakakuki et al. Cell 2010\,\n[2] Imoto et al. iS
cience 2022\,\n[3] Imoto et al. STAR Protocols 2022\,\n[4] Shinohara et al
. Science 2014\,\n[5] Michida et al. Cell Reports 2020\,\n[6] Wibisana et
al. PLoS Genetics 2022\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Rosemary Braun (Northwestern University)
DTSTART:20221118T020000Z
DTEND:20221118T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/37
DESCRIPTION:Title: Quantifying dynamical changes in sparse\, noisy\, high-dimen
sional data\nby Rosemary Braun (Northwestern University) as part of IB
S Biomedical Mathematics Online Colloquium\n\n\nAbstract\nThe circadian cl
ock orchestrates a vast array of behavioral and physiological processes wi
th a 24-hour cycle\, enabling nearly all organisms -- from bread mold to f
ruit-flies to humans -- to anticipate and adapt to the Earth's day. Entra
inable by environmental cue\, the rhythm itself is generated by a self-sus
tained molecular oscillator present in nearly every cell. This in turn go
verns the expression of thousands of genes\, precisely coordinating biomol
ecular functions at the microscopic scale. While experimental evidence su
ggests that the clock is crucial for mediating the response to changes in
an organism's environment (such as temperature and food availability)\, th
e precise mechanisms underlying circadian regulation remain unclear. Toda
y\, high-throughput omics assays enable us to probe these processes in mol
ecular detail\, with the goal of making inferences about which genes are u
nder circadian control and how their dynamics change under different envir
onmental conditions. Analyzing this transcriptomic time-series data raise
s new challenges: that of characterizing dynamics when the data are noisy\
, sparsely sampled in time\, and may not be strictly periodic. In this ta
lk\, I will discuss our recent work on nonparametric methods to analyze ci
rcadian transcriptomic data by exploiting results from dynamical systems t
heory\, nonlinear dimension reduction\, and topological data analysis.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Edda Klipp (Humboldt University of Berlin)
DTSTART:20221123T070000Z
DTEND:20221123T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/38
DESCRIPTION:Title: Assessing the limits of control of Covid-19 outbreaks using
agent-based modeling\nby Edda Klipp (Humboldt University of Berlin) as
part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nTrans
mission of SARS-CoV-2 relies on interactions between humans. Heterogeneity
and stochasticity both in human-human interactions and in the transmissio
n of the virus give rise to non-linear infection networks that gain comple
xity with time.\nWe assessed the limits of control and the effect of pharm
aceutical and non-pharmaceutical measures against COVID‐19 outbreaks wit
h a detailed community‐specific agent-based model (GERDA). The demograph
ic and geographic structure of the concrete communities influence the patt
ern of infection spreading. Stochastic community dynamics and limited vacc
ination can lead to bimodal outcomes\, rendering predictions about infecti
on spreading and effects of nonpharmaceutical interventions uncertain.\n\n
By comparing different vaccination strategies\, we found that the herd imm
unity threshold depends strongly on the applied vaccination strategy. Whe
n vaccine supply is limited\, different vaccination strategies are optimal
for the intended goal e.g.\, reducing fatalities or confining an outbreak
. Prioritizing highly interactive people diminishes the risk for an infect
ion wave\, while prioritizing the elderly minimizes fatalities.\nThe inher
ent stochasticity can lead to bimodality in predicting an outbreak in diff
erent low-incidence scenarios and\, thereby\, render the effect of limited
NPI uncertain. Further\, we found that for the low-incidence scenarios t
he reproduction number R0 is not a suitable predictor for the system behav
ior or the infectiousness of the virus.\nThe developed simulation platform
can process and analyze dynamic COVID‐19 epidemiological situations in
diverse communities worldwide to predict pathways to population immunity e
ven with limited vaccination.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Svetlana Postnava (University of Sydney)
DTSTART:20221130T070000Z
DTEND:20221130T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/39
DESCRIPTION:Title: Brain dynamics during shiftwork: from maths and codes to rea
l-world applications\nby Svetlana Postnava (University of Sydney) as p
art of IBS Biomedical Mathematics Online Colloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:George Sugihara (Scripps Institution for Oceanography)
DTSTART:20221209T020000Z
DTEND:20221209T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/40
DESCRIPTION:Title: Taming Complexity in Data-Limited Nonlinear Nonequilibrium S
ettings\nby George Sugihara (Scripps Institution for Oceanography) as
part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nSince
before the time of Aristotle and the natural philosophers\, reductionism h
as played a foundational role in western scientific thought. The premise o
f reductionism is that systems can be broken down into constituent pieces
and studied independently\, then reassembled to understand the behavior of
the system as a whole. It embodies the classical linear perspective. This
approach has been successful in developing basic physical laws and especi
ally in engineering where linear analysis dominates and systems are purpos
efully designed that way. However\, reductionism is not universally applic
able for natural complex systems where behavior is driven\, not by a few f
actors acting independently\, but by complex interactions between many com
ponents acting together and changing in time.\n\nNonlinearity in living sy
stems means that its parts are interdependent – variables do not act in
a mutually independent manner\; rather they interact\, and as a consequenc
e associations (correlations) between them will change as the overall syst
em context (state) changes. This problem is highlighted when extrapolati
ng the results of single-factor experiments to nature\, and surely contrib
utes to the frustrating disconnect between experimental findings and clini
cal outcomes in drug trials. Indeed\, while everyone knows Berkeley’s 17
10 dictum “correlation does not imply causation” few realize that for
nonlinear systems the converse “causation does not imply correlation”
is also true. This conundrum runs counter to deeply ingrained heuristic th
inking that is at the basis of modern science. Biological systems (esp. ec
osystems) are particularly perverse on this issue by exhibiting mirage cor
relations that can continually cause us to rethink relationships we though
t we understood.\n\nHere we examine a minimalist paradigm\, empirical dyna
mics (EDM)\, for studying non-linear systems and a method (CCM) that can d
etect causality when there is no correlation among variables. It is a data
-driven approach that uses time series to study a system holistically by r
econstructing its attractor – a geometric object that embodies the rules
of a full set of equations for the system. The ideas are intuitive and
will be illustrated with examples from genetics\, ecology and epidemiology
.\n\nA python version of EDM tools can be found at https://pepy.tech/proje
ct/pyEDM\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Anderson (University of Wisconsin Madison)
DTSTART:20221021T013000Z
DTEND:20221021T020000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/41
DESCRIPTION:Title: A brief introduction to stochastic reaction networks\nby
David Anderson (University of Wisconsin Madison) as part of IBS Biomedica
l Mathematics Online Colloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Shinya Kuroda (Tokyo University)
DTSTART:20230303T020000Z
DTEND:20230303T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/42
DESCRIPTION:Title: Systems biology of insulin action\nby Shinya Kuroda (Tok
yo University) as part of IBS Biomedical Mathematics Online Colloquium\n\n
\nAbstract\n1. The “temporal information code” of insulin action: a bo
ttom-up approach One of the essential elements of signaling networks is to
encode information from a wide variety of inputs into a limited set of mo
lecules. We have proposed a “temporal information code” that regulates
a variety of physiological functions by encoding input information in tem
poral patterns of molecular activity\, and based on this concept\, we are
analyzing biological homeostasis by insulin signaling. Taking blood insuli
n as an example\, we will explain how the temporal information of blood in
sulin is selectively decoded by downstream networks.\n\n2. Transomics of i
nsulin action: a top-down approach In order to obtain a complete picture o
f insulin action\, we performed transomics measurements integrating metabo
lomics and transcriptomics\, and found that metabolism is regulated by all
osteric regulation in the liver of normal mice and by compensatory gene ex
pression in the liver of obese mice. (Top-down approach). I will talk abou
t approach the principle of homeostasis of living organisms by temporal pa
tterns\, using the analysis of systems biology of insulin action using two
different approaches.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/42/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Martin Nowak (Harvard University)
DTSTART:20230310T010000Z
DTEND:20230310T020000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/43
DESCRIPTION:Title: Evolution of cooperation\nby Martin Nowak (Harvard Unive
rsity) as part of IBS Biomedical Mathematics Online Colloquium\n\nAbstract
: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/43/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Saez-Rodriguez\, Julio (Heidelberg University)
DTSTART:20230315T070000Z
DTEND:20230315T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/44
DESCRIPTION:Title: Dynamic logic models complement machine learning for persona
lized medicine\nby Saez-Rodriguez\, Julio (Heidelberg University) as p
art of IBS Biomedical Mathematics Online Colloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/44/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Stefan Bauer (Helmholtz and TU Munich)
DTSTART:20230324T070000Z
DTEND:20230324T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/45
DESCRIPTION:Title: Neural causal models for experimental design\nby Stefan
Bauer (Helmholtz and TU Munich) as part of IBS Biomedical Mathematics Onli
ne Colloquium\n\n\nAbstract\nMany questions in everyday life as well as in
research are causal in nature: How would the climate change if we lower t
rain prices or will my headache go away if I take an aspirin? Inherently\,
such questions need to specify the causal variables relevant to the quest
ion and their interactions. However\, existing algorithms for learning cau
sal graphs from data are often not scaling well both with the number of va
riables or the number of observations. This talk will provide a brief intr
oduction to causal structure learning\, recent efforts in using continuous
optimization to learn causal graphs at scale and systematic approaches fo
r causal experimental design at scale.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/45/
END:VEVENT
BEGIN:VEVENT
SUMMARY:George Karniadakis (Brown University)
DTSTART:20230407T020000Z
DTEND:20230407T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/46
DESCRIPTION:Title: BINNS: Biophysics-Informed Neural Networks\nby George Ka
rniadakis (Brown University) as part of IBS Biomedical Mathematics Online
Colloquium\n\n\nAbstract\nWe will present a new approach to develop a data
-driven\, learning-based framework for predicting outcomes of biophysical
systems and for discovering hidden mechanisms and pathways from noisy data
. We will introduce a deep learning approach based on neural networks (NNs
) and on generative adversarial networks (GANs). Unlike other approaches t
hat rely on big data\, here we “learn” from small data by exploiting t
he information provided by the mathematical physics\, e.g..\, conservation
laws\, reaction kinetics\, etc\,. which are used to obtain informative pr
iors or regularize the neural networks. We will demonstrate how we can tra
in BINNs from multifidelity/multimodality data\, and we will present sever
al examples of inverse problems\, e.g.\, in systems biology for diabetes a
nd in biomechanics for non-invasive inference of thrombus material propert
ies. We will also discuss how operator regression in the form of DeepOnet
can be used to accelerate inference based on historical data and only a fe
w new data\, as well its generalization and transfer learning capacity.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/46/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hans P.A. Van Dongen (Washington State Univeristy)
DTSTART:20230428T020000Z
DTEND:20230428T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/47
DESCRIPTION:Title: Modeling the temporal dynamics of neurobehavioral performanc
e impairment due to sleep loss and circadian misalignment\nby Hans P.A
. Van Dongen (Washington State Univeristy) as part of IBS Biomedical Mathe
matics Online Colloquium\n\n\nAbstract\nAbstract: The well-known two-proce
ss model of sleep regulation makes accurate predictions of sleep timing an
d duration\, as well as neurobehavioral performance\, for a variety of acu
te sleep deprivation and nap sleep scenarios\, but it fails to predict the
effects of chronic sleep restriction on neurobehavioral performance. The
two-process model belongs to a broader class of coupled\, non-homogeneous\
, first-order\, ordinary differential equations (ODEs)\, which can capture
the effects of chronic sleep restriction. These equations exhibit a bifur
cation\, which appears to be an essential feature of performance impairmen
t due to sleep loss. The equations implicate a biological system analogous
to two connected compartments containing interacting compounds with time-
varying concentrations\, such as the adenosinergic neuromodulator/receptor
system\, as a key mechanism for the regulation of neurobehavioral functio
ning under conditions of sleep loss. The equations account for dynamic int
eraction with circadian rhythmicity\, and also provide a new approach to d
ynamically tracking the magnitude of sleep inertia upon awakening from res
tricted sleep. This presentation will describe the development of the ODE
system and its experimental calibration and validation\, and will discuss
some novel predictions.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/47/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Morgen Jensen (Niels Bohr Institute)
DTSTART:20230510T070000Z
DTEND:20230510T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/48
DESCRIPTION:Title: DNA repair and chaos in CellsBD\nby Morgen Jensen (Niels
Bohr Institute) as part of IBS Biomedical Mathematics Online Colloquium\n
\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/48/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thomas Philipp (Imperial College London)
DTSTART:20230524T070000Z
DTEND:20230524T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/49
DESCRIPTION:Title: Stochastic gene expression in lineage trees\nby Thomas P
hilipp (Imperial College London) as part of IBS Biomedical Mathematics Onl
ine Colloquium\n\n\nAbstract\nStochasticity in gene expression is an impor
tant source of cell-to-cell variability (or noise) in clonal cell populati
ons. So far\, this phenomenon has been studied using the Gillespie Algorit
hm\, or the Chemical Master Equation\, which implicitly assumes that cells
are independent and do neither grow nor divide. This talk will discuss re
cent developments in modelling populations of growing and dividing cells t
hrough agent-based approaches. I will show how the lineage structure affec
ts gene expression noise over time\, which leads to a straightforward inte
rpretation of cell-to-cell variability in population snapshots. I will als
o illustrate how cell cycle variability shapes extrinsic noise across line
age trees. Finally\, I outline how to construct effective chemical master
equation models based on dilution reactions and extrinsic variability that
provide surprisingly accurate approximations of the noise statistics acro
ss growing populations. The results highlight that it is crucial to consid
er cell growth and division when quantifying cellular noise.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/49/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sushmita Roy (University of Wisconsin-Madison)
DTSTART:20230609T020000Z
DTEND:20230609T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/50
DESCRIPTION:Title: Deciphering gene regulatory networks underlying cell-fate sp
ecification\nby Sushmita Roy (University of Wisconsin-Madison) as part
of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nCell fate
specification is a dynamic process during which gene regulatory networks (
GRNs) transition between different states and define cell type-specific pa
tterns of gene expression. Identifying such cell type-specific gene regula
tory networks is important for understanding how cells differentiate to di
verse lineages from a progenitor state\, how differentiated cells can be r
eprogrammed\, and how these networks get disrupted in diseases such as can
cer and developmental disorders. The advent of single cell omics has enabl
ed us to perform high-throughput molecular phenotyping of individual cells
at different omic levels. These technologies have revolutionized our unde
rstanding of cell type composition across diverse normal and disease condi
tions\; however inferring cell type-specific networks and their dynamics f
rom single cell omic datasets is an open challenge. I will present some of
our recent efforts for inference and analysis of cell type-specific regul
atory networks from single cell omic datasets. Application of our approach
to hematopoietic differentiation and mouse cellular reprogramming predict
ed key regulatory nodes likely important for establishing different cell-t
ype specific expression programs.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/50/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sebastian Walcher (Mathematik A\, RWTH Aachen\, Germany)
DTSTART:20230920T070000Z
DTEND:20230920T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/51
DESCRIPTION:Title: Reaction networks: Reduction of dimension and critical param
eters\nby Sebastian Walcher (Mathematik A\, RWTH Aachen\, Germany) as
part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nTypica
lly\, the mathematical description of reaction networks involves a system
of parameter-dependent ordinary differential equations. Generally\, one is
interested in the qualitative and quantitative behavior of solutions in v
arious parameter regions. In applications\, identifying the reaction param
eters is a fundamental task. Reduction of dimension is desirable from a pr
actical perspective\, and even necessary when different timescales are pre
sent. For biochemical reaction networks\, a classical reduction technique
assumes quasi-steady state (QSS) of certain species. From a general mathem
atical perspective\, singular perturbation theory – involving a small pa
rameter – is often invoked. The talk is mathematically oriented. The fol
lowing points will be discussed: Singular perturbation reduction in genera
l coordinates. (“How does one compute reductions?”) Critical parameter
s for singular perturbations. (“How does one find small parameters?”)
Quasi-steady state and singular perturbations. (“What is applicable\, wh
at is correct?”)\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/51/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Tetsuya J. Kobayashi (Institute of Industrial Science\, the Univer
sity of Tokyo)
DTSTART:20231020T020000Z
DTEND:20231020T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/52
DESCRIPTION:Title: Optimality of Biological Information Processing\nby Tets
uya J. Kobayashi (Institute of Industrial Science\, the University of Toky
o) as part of IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\n
TBD\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/52/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Eder Zavala (Centre for Systems Modelling & Quantitative Biomedici
ne\, University of Birmingham)
DTSTART:20231101T070000Z
DTEND:20231101T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/53
DESCRIPTION:Title: Quantitative analysis of high-resolution daily profiles of H
PA axis hormones\nby Eder Zavala (Centre for Systems Modelling & Quant
itative Biomedicine\, University of Birmingham) as part of IBS Biomedical
Mathematics Online Colloquium\n\n\nAbstract\nThe Hypothalamic-Pituitary-Ad
renal (HPA) axis is the key regulatory pathway responsible for maintaining
homeostasis under conditions of real or perceived stress. Endocrine respo
nses to stressors are mediated by adrenocorticotrophic hormone (ACTH) and
corticosteroid (CORT) hormones. In healthy\, non-stressed conditions\, ACT
H and CORT exhibit highly correlated ultradian pulsatility with an amplitu
de modulated by circadian processes. Disruption of these hormonal rhythms
can occur as a result of stressors or in the very early stages of disease.
Despite the fact that misaligned endocrine rhythms are associated with in
creased morbidity\, a quantitative understanding of their mechanistic orig
in and pathogenicity is missing. Mathematically\, the HPA axis can be unde
rstood as a dynamical system that is optimised to respond and adapt to per
turbations. Normally\, the body copes well with minor disruptions\, but fi
nds it difficult to withstand severe\, repeated or long-lasting perturbati
ons. Whilst a healthy HPA axis maintains a certain degree of robustness to
stressors\, its fragility in diseased states is largely unknown\, and thi
s understanding constitutes a critical step toward the development of digi
tal tools to support clinical decision-making. This talk will explore how
these challenges are being addressed by combining high-resolution biosampl
ing techniques with mathematical and computational analysis methods. This
interdisciplinary approach is helping us quantify the inter-individual var
iability of daily hormone profiles and develop novel “dynamic biomarkers
” that serve as a normative reference and to signal endocrine dysfunctio
n. By shifting from a qualitative to a quantitative description of the HPA
axis\, these insights bring us a step closer to personalised clinical int
erventions for which timing is key.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/53/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Matthew Simpson (Queensland University of Technology\, Australia)
DTSTART:20231110T020000Z
DTEND:20231110T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/54
DESCRIPTION:Title: Efficient prediction\, estimation and identifiability analys
is with mechanistic mathematical models\nby Matthew Simpson (Queenslan
d University of Technology\, Australia) as part of IBS Biomedical Mathemat
ics Online Colloquium\n\n\nAbstract\nInterpreting data using mechanistic m
athematical models provides a foundation for discovery and decision-making
in all areas of science and engineering. Key steps in using mechanistic m
athematical models to interpret data include: (i) identifiability analysis
\; (ii) parameter estimation\; and (iii) model prediction. Here we present
a systematic\, computationally efficient likelihood-based workflow that a
ddresses all three steps in a unified way. Recently developed methods for
constructing profile-wise prediction intervals enable this workflow and pr
ovide the central linkage between different workflow components. These met
hods propagate profile-likelihood-based confidence sets for model paramete
rs to predictions in a way that isolates how different parameter combinati
ons affect model predictions. We show how to extend these profile-wise pre
diction intervals to two-dimensional interest parameters\, and then combin
e profile-wise prediction confidence sets to give an overall prediction co
nfidence set that approximates the full likelihood-based prediction confid
ence set well. We apply our methods to a range of synthetic data and real-
world ecological data describing re-growth of coral reefs on the Great Bar
rier Reef after some external disturbance\, such as a tropical cyclone or
coral bleaching event.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/54/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Samuel Isaacson (Boston University)
DTSTART:20231117T020000Z
DTEND:20231117T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/55
DESCRIPTION:Title: Spatial Particle Modeling of Immune Processes\nby Samuel
Isaacson (Boston University) as part of IBS Biomedical Mathematics Online
Colloquium\n\n\nAbstract\nTBD\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/55/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alfio Quarteroni (Politecnico di Milano)
DTSTART:20231122T070000Z
DTEND:20231122T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/56
DESCRIPTION:Title: Physics-based and data-driven numerical models for computati
onal medicine\nby Alfio Quarteroni (Politecnico di Milano) as part of
IBS Biomedical Mathematics Online Colloquium\n\n\nAbstract\nI will report
on some recent results on modelling the heart\, the external circulation\,
and their application to problems of clinical relevance. I will show that
a proper integration between PDE-based and machine-learning algorithms ca
n improve the computational efficiency and enhance the generality of our i
HEART simulator.\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/56/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Robyn P. Araujo (Australian Research Council Future Fellow\, Queen
sland University of Technology)
DTSTART:20231208T020000Z
DTEND:20231208T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/57
DESCRIPTION:Title: Cellular cognition and the simple complexity of the networks
of life\nby Robyn P. Araujo (Australian Research Council Future Fello
w\, Queensland University of Technology) as part of IBS Biomedical Mathema
tics Online Colloquium\n\n\nAbstract\nTBD\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/57/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Aiden Doherty (Big Data Institute\, University of Oxford)
DTSTART:20251015T070000Z
DTEND:20251015T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/58
DESCRIPTION:Title: Developing time-series machine learning methods to unlock ne
w insights from large-scale biomedical resources\nby Aiden Doherty (Bi
g Data Institute\, University of Oxford) as part of IBS Biomedical Mathema
tics Online Colloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/58/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Luonan Chen (Shanghai Institutes for Biological Sciences)
DTSTART:20251029T070000Z
DTEND:20251029T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/59
DESCRIPTION:Title: Dynamical data science and AI for Biology and Medicine\n
by Luonan Chen (Shanghai Institutes for Biological Sciences) as part of IB
S Biomedical Mathematics Online Colloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/59/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Amir Sharafkhaneh (Department of Medicine\, Baylor College of Medi
cine)
DTSTART:20251112T070000Z
DTEND:20251112T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/60
DESCRIPTION:Title: [Cancelled]\nby Amir Sharafkhaneh (Department of Medicin
e\, Baylor College of Medicine) as part of IBS Biomedical Mathematics Onli
ne Colloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/60/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Stephan Munch (Department of Applied Mathematics\, UC Santa Cruz)
DTSTART:20251205T020000Z
DTEND:20251205T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/61
DESCRIPTION:Title: Empirical modeling of bifurcations and chaos from time serie
s\nby Stephan Munch (Department of Applied Mathematics\, UC Santa Cruz
) as part of IBS Biomedical Mathematics Online Colloquium\n\nAbstract: TBA
\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/61/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chen Jia
DTSTART:20260325T070000Z
DTEND:20260325T080000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/62
DESCRIPTION:Title: Stochastic theory of complex biochemical reaction networks\nby Chen Jia as part of IBS Biomedical Mathematics Online Colloquium\n\
nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/62/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sean Lawley
DTSTART:20260403T020000Z
DTEND:20260403T030000Z
DTSTAMP:20260422T230718Z
UID:IBS_BIMAG_Colloquium/63
DESCRIPTION:Title: Stochastics in medicine: Delaying menopause and missing drug
doses\nby Sean Lawley as part of IBS Biomedical Mathematics Online Co
lloquium\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/IBS_BIMAG_Colloquium/63/
END:VEVENT
END:VCALENDAR