\n
\n Bio
\n
“Uncovering the Origins of Heterogeneity in Responses to MAPK
-Targeted Cancer Therapies<
span style='font-size: 21pt\;'>”
\n
<
a href='https://icm.jhu.edu/wp-content/uploads/2021/10/Fallahi-Sichani-Hea
dshot-scaled.jpg'>
\n
Dr. Mohammad Fallahi-Sichani is a ten
ure-track Assistant Professor of Biomedical Engineering in the University
of Virginia (UVa)\, Basic Science Lead of the Melanoma Research Team at UV
A Cancer Center\, and a member of the Molecular and Cellular Basis of Dise
ase (MCBD) Graduate Program at UVa. Prior to relocating his laboratory to
UVa in June 2020\, he served as an Assistant Professor of Biomedical Engin
eering in the University of Michigan\, beginning in Summer 2017. Since the
n\, he has established a laboratory that currently comprises 1 research la
b specialist\, 3 PhD students\, 1 MD/PhD student\, 2 postdoctoral fellows\
, and 3 undergraduates. He received my Ph.D. in Chemical Engineering from
the Univ. of Michigan in 2012. Under the joint supervision of Jennifer Lin
derman and Denise Kirschner\, he developed multi-scale models of lung path
ology to bridge molecular\, cellular and tissue level mechanisms of immune
response to M. tuberculosis infection. Dr. Fallahi-Sichani completed his
postdoctoral training with Peter Sorger during 2012-2017\, working as a Li
fe Sciences Research Foundation (LSRF) Postdoctoral Fellow in Systems Biol
ogy at Harvard Medical School. He was then interested in understanding how
responses of heterogeneous tumor cells to targeted therapeutics are deter
mined at the level of their signaling networks. His postdoctoral training
provided a great opportunity to expand his scientific expertise towards hi
gh-throughput and single-cell quantitative biology\, cancer biology and ph
armacology. Dr. Fallahi-Sichani developed new methods to combine live-cell
imaging\, multiplexed measurements\, single-cell analysis\, and data-driv
en computational modeling\, to link the observed cell-to-cell heterogeneit
y in drug effect in tumors to the consequential changes in cell cycle regu
lation and acquisition of drug resistance phenotypes. Research in his own
laboratory aims at designing\, building\, and utilizing new experimental a
nd computational tools to discover fundamental mechanisms that regulate th
e behavior of human cells in response to perturbations\, such as cytokines
\, environmental stress\, and therapeutic drugs. By merging multiplexed\,
high-throughput\, single-cell data acquisition with multi-scale computatio
nal modeling\, he hopes to understand the molecular mechanisms that underl
ie adaptive cell fate decisions in the presence of cellautonomous\, microe
nvironment\, and therapy-induced selective pressures\, and elucidate how t
hey vary under unhealthy conditions\, particularly in cancer cells. From a
therapeutic point of view\, a detailed understanding of these mechanisms
will provide a rational basis for choosing the optimal therapeutic targets
to: (i) maximize the desired effect in diseased cells (e.g.\, tumor cell
killing)\, (ii) prevent the development of therapeutic resistance\, and (i
ii) reduce therapy-induced adverse effects in healthy cells.
\n
\n
To join the live event please request the link by emailing: icm@jhu.edu
\n
\n
Recording
\n\n
\n Abstract
\n
\n
“Un
covering the Origins of Heterogeneity in Responses to MAPK-Targeted Cancer
Therapies”
\n
\n
\n
\n
\n
Tumor cells tha
t carry the same mutated oncogenes respond nonidentically to therapeutic i
nhibitors of oncogenic signaling. This poses a major challenge to the use
of targeted therapies\, arguably the cornerstone of precision cancer medic
ine. Although genetic alterations may cause late drug resistance\, emergin
g evidence suggests that tissue-specific\, epigenetic mechanisms and rewir
ing of transcriptional networks rapidly induce drug-tolerant states with r
educed dependency on the oncogenic activity. I will describe systems biolo
gy approaches to identify such states in the context of BRAF-mutated melan
omas treated with BRAF/MAPK targeted therapies. By merging highly multiple
xed single-cell assays\, high-throughput data acquisition\, and integrativ
e computational modeling\, we assemble an integrated picture that links mo
lecular determinants of drug response to the diversity of intrinsic and ad
aptive phenotypes across both genetically diverse and isogenic populations
of tumor cells. This picture will allow us to identify and test efficient
ways to block subpopulations of drug-resistant cells\, and thereby advanc
e the development of more effective therapies.
\n
Recording
\n
\n
div>\n
DTSTART;TZID=America/New_York:20211102T103000
DTEND;TZID=America/New_York:20211102T113000
LOCATION:Zoom
SEQUENCE:0
SUMMARY:Uncovering the Origins of Heterogeneity in Responses to MAPK-Target
ed Cancer Therapies
URL:https://icm.jhu.edu/events/uncovering-the-origins-of-heterogeneity-in-r
esponses-to-mapk-targeted-cancer-therapies/
X-COST-TYPE:free
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allahi-Sichani-Headshot-1021x1024.jpg\;209\;209\,medium\;https://icm.jhu.e
du/wp-content/uploads/2021/10/Fallahi-Sichani-Headshot-1021x1024.jpg\;209\
;209\,large\;https://icm.jhu.edu/wp-content/uploads/2021/10/Fallahi-Sichan
i-Headshot-1021x1024.jpg\;209\;209\,full\;https://icm.jhu.edu/wp-content/u
ploads/2021/10/Fallahi-Sichani-Headshot-1021x1024.jpg\;209\;209
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4982927@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES:
CONTACT:Mishka Colombo\; 410-516-4116\; mcolomb4@jhu.edu\; https://wse.zoom
.us/j/96691674778
DESCRIPTION:
DTSTART;TZID=America/New_York:20211102T183000
DTEND;TZID=America/New_York:20211102T200000
LOCATION:Zoom
SEQUENCE:0
SUMMARY:Computational Medicine Minor Q&A Session
URL:https://icm.jhu.edu/events/computational-medicine-minor-qa-session/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2021/10/C
MMinorQA.png\;741\;959\,medium\;https://icm.jhu.edu/wp-content/uploads/202
1/10/CMMinorQA.png\;741\;959\,large\;https://icm.jhu.edu/wp-content/upload
s/2021/10/CMMinorQA.png\;741\;959\,full\;https://icm.jhu.edu/wp-content/up
loads/2021/10/CMMinorQA.png\;741\;959
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4983067@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES:
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://icm.jhu.ed
u/seminar-series/
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“The Landscape of the Heritable Cancer Genome
”
\n
\n
Giovanni Stra
cquadanio is an UKRI EPSRC fellow\, Senior Lecturer (Associate Professor)
in Synthetic Biology and co-director of the Edinburgh Genome Foundry (EGF)
. He group is interested in understanding the molecular mechanisms underpi
nning complex phenotypes and diseases using two of the most disruptive tec
hnologies available: synthetic biology and machine learning. His long-term
goal is to reverse-engineer biological systems to develop generative algo
rithms to design\, build and test biological agents for addressing healthc
are problems\, such as rare metabolic diseases and cancer\, and industrial
biotechnology challenges\, like de-novo enzyme engineering.
\n
Dr St
racquadanio obtained a PhD in Informatics from the University of Catania (
Italy) in 2010\, working on global optimisation methods for protein struct
ure prediction and metabolic engineering. He then received postdoctoral tr
aining in synthetic biology in Joel Bader and Jef Boeke labs at the Johns
Hopkins University working on the synthetic yeast genome.
\n
Dr Strac
quadanio was a main contributor to the Synthetic Yeast (Sc2.0) genome proj
ect\, pioneering algorithms and developing software at the foundation of t
he first synthetic eukaryotic genome. He has also developed tools used in
large-scale synthesis projects\, streamlining chromosomes engineering and
the assembly of biological pathways.
\n
In 2014\, he moved to the Lud
wig Institute for Cancer Research at the University of Oxford UK to work o
n cancer genetics\, in Gareth Bond’s lab\; here\, he focused on studying h
ow high-frequency inherited p53 mutations affect the risk of cancer and re
sponse to treatment using statistical genetics methods and cell line model
s.
\n
In 2016\, prior to joining the University of Edinburgh\, Dr Str
acquadanio was an assistant professor at the School of Computer Science an
d Electronic Engineering of the University of Essex\, where he received th
e Wellcome Trust Seed Award in Science to study metabolic switching in ren
al cell carcinoma.
\n
In 2021\, he was awarded the UKRI EPSRC fellows
hip to design and manufacture enzyme replacement therapies for Fabry’s dis
ease using generative deep learning and yeast. He also collaborates with i
ndustrial stakeholders in the biotechnology field\, such as Fujifilm Diosy
nth\, on protein design\, codon optimisation and lab automation.
\n
\n
Dr Stracquadanio has authored more than 40 research articles publ
ished in international peer-reviewed journals\, including Science\, Nature
Rev. Cancer\, Cancer Research and PNAS. He also serves as Associate Edito
r for BMC Genomics and as reviewer and panel member for EPSRC\, BBSRC\, MR
C and FLF. Since 2021\, he is also a member of the EPSRC Peer Review Assoc
iate College.
\n
.
\n
\n
To join the live event please re
quest the link by emailing: icm@jhu.e
du
\n
\n
Recording
\n
\n
\n Abstract
\n
\n
“
strong>The
Landscape of the Heritable Cancer Genome”
\n
\n
\n
\n
\n
Genome-wide association studies (GWAS) have found hundreds of s
ingle nucleotide polymorphisms (SNPs) associated with increased risk of ca
ncer. However\, the amount of heritable risk explained by SNPs is limited\
, leaving most of cancer heritability unexplained. Tumor sequencing projec
ts have shown that causal mutations are enriched in genic regions. We hypo
thesized that SNPs located in protein coding genes and nearby regulatory r
egions could explain a significant proportion of the heritable risk of can
cer.
\n
To perform gene-level heritability analysis\, we developed a
new method\, called Bayesian Gene HERitability Analysis (BAGHERA)\, to est
imate the heritability explained by all genotyped SNPs and by those locate
d in genic regions using GWAS summary statistics [1]. BAGHERA-based analys
is of 38 cancers reported in the UK Biobank showed that SNPs explain at le
ast 10% of the heritable risk for 14 of them\, including late onset malign
ancies. We then identified 1\,146 genes\, called cancer heritability genes
(CHGs)\, explaining a significant proportion of cancer heritability. CHGs
were involved in hallmark processes controlling the transformation from n
ormal to cancerous cells. Importantly\, 60 of them also harbored somatic d
river mutations\, and 27 are tumor suppressors. Evidence for a causal role
of CHGs was shown in testicular cancer\, where a cluster of SNPs in the K
ITLG CHG is responsible for p53-mediated responses to genotoxic therapies
[2].
\n
Our results suggest that germline and somatic mutation inform
ation could be exploited to identify subgroups of individuals at higher ri
sk of cancer in the broader population and could prove useful to establish
strategies for early detection and cancer surveillance.
\n
<
a href='https://wse.zoom.us/rec/play/HgCrgcBrOdFHEsT2uUGCgFxweieSLqeYLIL-T
B9G_QTmgFAGHSLq9zGUYcluPGEop2w7zU63B3-sVP4Y.UHyJh9lml5lhr0r0?continueMode=
true&_x_zm_rtaid=q-sVNbInQxiKUX6G4XCSAA.1638828278055.afa5a5198cac855d5f1f
8d5f5312667c&_x_zm_rhtaid=639' target='_blank' rel='noopener'>Reco
rding
\n
\n
\n
DTSTART;TZID=America/New_York:20211130T103000
DTEND;TZID=America/New_York:20211130T113000
LOCATION:Zoom @ email mcolomb4@jhu.edu for link
SEQUENCE:0
SUMMARY:The Landscape of the Heritable Cancer Genome
URL:https://icm.jhu.edu/events/the-landscape-of-the-heritable-cancer-genome
/
X-COST-TYPE:free
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stracquadanio-short-bio-pic-lq-1024x1024.jpg\;222\;223\,medium\;https://ic
m.jhu.edu/wp-content/uploads/2021/11/gstracquadanio-short-bio-pic-lq-1024x
1024.jpg\;222\;223\,large\;https://icm.jhu.edu/wp-content/uploads/2021/11/
gstracquadanio-short-bio-pic-lq-1024x1024.jpg\;222\;223\,full\;https://icm
.jhu.edu/wp-content/uploads/2021/11/gstracquadanio-short-bio-pic-lq-1024x1
024.jpg\;222\;223
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4983373@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series
CONTACT:Mishka Colombo\; 4105164116\; mishka@jhu.edu\; https://icm.jhu.edu/
seminar-series/
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Mechanistic and Data-Driven Dissection of Cell Communicat
ion Through Tensor Methods<
span style='font-size: 21pt\;'>”
\n
<
a href='https://icm.jhu.edu/wp-content/uploads/2022/01/aaron-meyer-headsho
t-1.jpg'>
\n
Dr
. Aaron Meyer is an Assistant Professor of Bioengineering and Bioinformati
cs at the University of California\, Los Angeles. He previously received h
is B.S. in Bioengineering from UCLA\, his Ph.D. in Biological Engineering
from the Massachusetts Institute of Technology\, and then was an independe
nt fellow at the Koch Institute for Integrative Cancer Research. Dr. Meyer
’s research aims to develop new computational methods\, deeply integrated
with experiments\, to understand intercellular communication and how it ca
n be therapeutically manipulated. He primarily focuses on combinations of
mechanistic and data-driven modeling as a solution to our incomplete knowl
edge of cellular pathways\, and applications in cancer and immunity where
models can have immediate therapeutic impact. His work has been recognized
by the NIH Director’s Early Independence Award\, a Siebel Scholars award\
, a Hellman Fellowship\, the UCLA Faculty Career Development Award\, the N
orthrop Grumman Excellence in Teaching Award\, and named as Ten to Watch b
y the Amgen Foundation.
\n
\n
To join the live event please re
quest the link by emailing: icm@jhu.e
du
\n
\n
Recording
\n
\n
\n Abstract
\n
\n
“Mechanistic a
nd Data-Driven Dissection of Cell Communication Through Tensor Methods”
\n
\n
\n
\n
\n
Studies of even simple c
ell responses to their environment are hindered by how responses are multi
-dimensional. For example\, a simple receptor-ligand pathway can display d
iffering responses based on timescale\, cell type\, stimulation\, type of
response measured\, and context. Interrogating and manipulating these syst
ems is thus almost always constrained by an incomplete view of the overall
pathway.
\n
\n
Like how principal component analysis uses a l
ow-rank approximation for dimensionality reduction of matrix-structured da
ta\, tensor generalizations provide solutions for pattern recognition in d
ata with a higher-dimensional structure. Using several recent and unpublis
hed applications\, including engineering cell-type selective IL-2 therapie
s\, serology analysis\, and clinical multi-omic studies of infection respo
nse\, I will describe some of the unique benefits of tensor-based analysis
and the biological discoveries it has revealed. Specifically\, tensor app
roximations enable more effective dimensionality reduction\, separation of
dimension-specific effects\, and a natural\, flexible solution to data in
tegration. Finally\, I will discuss some of the reasons tensor-based metho
ds remain limited in their application to molecular biology. Resolving the
se limitations\, and applying tensor methods in a more widespread manner\,
will help provide a complete view of cellular communication.
\n
Recording
\n
\n
\n
DTSTART;TZID=America/New_York:20220201T103000
DTEND;TZID=America/New_York:20220201T113000
LOCATION:Zoom
SEQUENCE:0
SUMMARY:Mechanistic and Data-Driven Dissection of Cell Communication Throug
h Tensor Methods
URL:https://icm.jhu.edu/events/mechanistic-and-data-driven-dissection-of-ce
ll-communication-through-tensor-methods/
X-COST-TYPE:free
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aron-meyer-headshot-1-1024x1024.jpg\;111\;111\,medium\;https://icm.jhu.edu
/wp-content/uploads/2022/01/aaron-meyer-headshot-1-1024x1024.jpg\;111\;111
\,large\;https://icm.jhu.edu/wp-content/uploads/2022/01/aaron-meyer-headsh
ot-1-1024x1024.jpg\;111\;111\,full\;https://icm.jhu.edu/wp-content/uploads
/2022/01/aaron-meyer-headshot-1-1024x1024.jpg\;111\;111
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4983944@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series
CONTACT:Mishka Colombo\; 4105164116\; mishka@jhu.edu\; https://wse.zoom.us/
rec/play/DvxOSxUu_9WVBbRq34jXYwNn3JQT-jg0CLF9MWN4cZ4c7AZ2taBpdKQq4ER9C1RSv
13DEpkjE9liT1qH.ebm19KbcRLY59FZG?continueMode=true&_x_zm_rtaid=VygAu5OITom
e1kFCWhsfmw.1648580481104.a2f113c9ae4cb6de3e9f7f07dc46450b&_x_zm_rhtaid=42
3
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Mechanistic Modeling of Signal Transduction and Dynamic C
ell Morphologies”
\n
\n
Dr. Meier-Sche
llersheim’s research group has been developing tools for quantitative comp
utational image analysis and mechanistic modeling of cellular behavior for
several years now. They were able to develop computational methods and to
ols that permit the simulation of cellular signaling networks embedded int
o dynamic\, realistic 3D morphologies to take into account the coupling be
tween cellular biochemistry and morphological dynamics. The simulation pla
tform Simmune was the first modeling software to permit this degree of rea
lism and we have continuously been improving its capabilities with regard
to the size of multi-cellular systems that can be simulated and parameter
scans that can be performed on distributed computer systems. Simmune can m
odel cells that express receptors for chemosensing and adhesion on their s
urface that react to receptor mediated stimuli by adjusting their geometry
to adhere to extracellular structures or by directed migration in respons
e to chemotactic signals. Although the coupling between biochemistry and c
ell motion based on Potts model rules is phenomenological applying such si
mulation techniques to explore the role cell-cell and cell-matrix interact
ions may play in complex 3D structures is a first step towards understandi
ng how chemical and mechanical cues regulate cellular migration. Dr. Meier
-Schellersheim’s group develops and applies quantitative image analysis to
ols to extract the information needed for detailed spatially resolved simu
lations directly in an unbiased and automated manner from image data. Havi
ng been part of the Laboratory of Systems Biology for several years now\,
his group acquired considerable experience in coordinating interdisciplina
ry work and in handling large heterogeneous data sets.
\n
In addition
to exploring the interplay between cell morphology and cellular responses
towards stimuli\, Dr. Meier-Schellersheim’s group develops and applies to
ols that can perform systematic analyses of the behavior of computational
models of cellular signaling pathways. These tools can identify which elem
ents of a pathway model are responsible for reproducing specific features
in the experimentally observed cellular behavior.
\n
\n
To joi
n the live event please request the link by emailing: icm@jhu.edu
\n
\n
▶RECORDING<
/span>
\n
\n
\n Abstract
\n
\n
“Mechanistic Modeling of Signal Transduction a
nd Dynamic Cell Morphologies”
\n
\n
\n
\n
\n
Detailed mechanistic models of cellul
ar signaling pathways have the advantage that the conclusions they allow u
s to draw can be tested with little ambiguity. However\, building such mod
els typically requires more data and involves far more parameters than phe
nomenological approaches do. Using examples from cytokine and growth facto
r signaling\, I will discuss some recent progress in applying detailed mod
eling tools and describe what can be learned from models whose parameters
cannot be uniquely determined. Then\, I will show how detailed models of i
ntracellular biochemistry can be linked to a realistic treatment of morpho
logical dynamics using a novel approach for representing cellular surfaces
.
\n
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20220329T103000
DTEND;TZID=America/New_York:20220329T113000
LOCATION:Zoom @ email mishka@jhu.edu for link
SEQUENCE:0
SUMMARY:Mechanistic Modeling of Signal Transduction and Dynamic Cell Morpho
logies
URL:https://icm.jhu.edu/events/mechanistic-modeling-of-signal-transduction-
and-dynamic-cell-morphologies/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2022/03/M
artin-Meier-Schellersheim.jpg\;107\;132\,medium\;https://icm.jhu.edu/wp-co
ntent/uploads/2022/03/Martin-Meier-Schellersheim.jpg\;107\;132\,large\;htt
ps://icm.jhu.edu/wp-content/uploads/2022/03/Martin-Meier-Schellersheim.jpg
\;107\;132\,full\;https://icm.jhu.edu/wp-content/uploads/2022/03/Martin-Me
ier-Schellersheim.jpg\;107\;132
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984318@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; email mcolomb4@jhu
.edu for link
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Towards a New Comprehensive Human Gene Catalogue
”
\n
\n
Mihaela Pertea is an Associate Professor in the Department o
f Biomedical Engineering at Johns Hopkins University. She received her B.S
. and M.S. degrees in Computer Science from University of Bucharest in Rom
ania\, and her Ph.D in Computer Science from the Johns Hopkins University
School of Engineering. Dr. Pertea’s work in computational biology draws up
on techniques and data from multiple disciplines\, including computer scie
nce and molecular biology\, genetics\, biotechnology\, and statistics. Her
work has focused on computational gene finding and sequence pattern recog
nition and she has developed several open-source gene finders that were us
ed for the annotation of the genomes of Plasmodium falciparum (ma
laria parasite)\, Arabidopsis thaliana\, rice\, Aspergillus f
umigatus\, Cryptococcus neoformans\, and others. A major focus of her
current research is on developing innovative and efficient methods to ana
lyze large DNA and RNA sequence data in order to provide a genome-scale un
derstanding of cellular function. Dr. Pertea believes that the principled
use of algorithms from other fields\, adapted to the problems of computati
onal biology and coupled with careful software engineering and high perfor
mance computing\, has the potential to make a significant impact in the li
fe sciences. She has published over 50 scientific papers that have receive
d more than 30\,000 citations to date.
\n
\n
To join the live
event please request the link by emailing: icm@jhu.edu
\n
\n
▶RECORDING
\n
\n
\n Abstrac
t
\n
\n
“Towards a New Comprehensive Human G
ene Catalogue”
\n
\n
\n
\n
\n
\n
A huge and still-growing number of genetic studies depe
nd on the human gene catalogue\, including thousands of experiments each y
ear and an enormous investment of time and effort. However\, despite its c
ritical role in biomedical research\, the human gene list is still incompl
ete and\, in many ways\, unstable. The widespread use of RNA sequencing te
chnology over the past decade has allowed scientists to discover a far lar
ger and richer repertoire of genes and transcripts than previously known.
Our own recent efforts led to the creation of a new human gene catalogue\
, called CHESS\, that we built using a very large collection of nearly 10\
,000 RNA-seq experiments from 31 tissues\, all sequenced as part of the GT
Ex project. Processing this large amount of data was one of the most chall
enging tasks\, made possible by the computational efficiency of StringTie\
, a transcriptome assembler we developed in our lab.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20220426T103000
DTEND;TZID=America/New_York:20220426T113000
LOCATION:Zoom
SEQUENCE:0
SUMMARY:Towards a New Comprehensive Human Gene Catalogue
URL:https://icm.jhu.edu/events/towards-a-new-comprehensive-human-gene-catal
ogue/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2022/03/M
ichaela-Pertea-1.jpg\;137\;137\,medium\;https://icm.jhu.edu/wp-content/upl
oads/2022/03/Michaela-Pertea-1.jpg\;137\;137\,large\;https://icm.jhu.edu/w
p-content/uploads/2022/03/Michaela-Pertea-1.jpg\;137\;137\,full\;https://i
cm.jhu.edu/wp-content/uploads/2022/03/Michaela-Pertea-1.jpg\;137\;137
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984268@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events
CONTACT:Mishka Colombo\; 4105164116\; mishka@jhu.edu\; email mishka@jhu.edu
for link
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Exploring the Potentials of Selective Blocking of the IL-
1 System by Distinct Targeting of the Co-receptor TILRR”
\n
\n
Dr. Qwarnstrom’s research focuses on regulation of receptor func
tion and cell signalling. Work on signal transduction\, has centred on reg
ulation of NF-B pathways and on using single cell recordings of regulator
y events in live cells. The single cell data was used as the basis for dev
eloping highly detailed\, predictive models of the IL-1 system and the NF-
B network. Interdisciplinary projects led to identification and character
isation of the IL-1R1 co-receptor TILRR and established its role in IL-1 r
eceptor function\, NF-B regulation and disease. Ongoing work focuses on e
valuating TILRR as a potential therapeutic target.
\n
\n
To jo
in the live event please request the link by emailing: icm@jhu.edu
\n
\n
▶RECORDING
\n
\n
\n
Abstract
\n
\n
“Exploring the Potentials of
Selective Blocking of the IL-1 System by Distinct Targeting of the Co-rec
eptor TILRR”
\n
\n
\n
\n
\n
Aberrant activation of NF-B plays a cent
ral role in disease. IL-1 is a key regulator of NF-B and has emerged as a
rational therapeutic target. Clinical trials have demonstrated that nonse
lective blocking of the IL-1 system can cause serious side effects related
to impairment of the immune system\, highlighting the need for more speci
fic targeting. Our interdisciplinary studies on regulation of NF-B led to
identification of the IL1R1 coreceptor TILRR and established its role in
controlling IL1R1 function and in driving aberrant activation of NF-B. Ou
r published work shows that genetic deletion or antibody blocking of TILRR
reduces progression of inflammatory conditions. Pathway enrichment analys
is of TILRR-induced gene expression profiles has revealed significant link
s with NF-B signalling\, Alzheimer’s disease and cancer. I will describe
the predictive modelling approaches used in these studies\, outline the ke
y regulatory events that underpin TILRR’s control of the IL-1 system and s
elective regulation the NF-B pathways\, present recent data on the role o
f TILRR in disease and discuss its potential as a therapeutic target.
\n
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20220503T103000
DTEND;TZID=America/New_York:20220503T113000
LOCATION:Zoom
SEQUENCE:0
SUMMARY:Exploring the Potentials of Selective Blocking of the IL-1 System b
y Distinct Targeting of the Co-receptor TILRR
URL:https://icm.jhu.edu/events/exploring-the-potentials-of-selective-blocki
ng-of-the-il-1-system-by-distinct-targeting-of-the-co-receptor-tilrr/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2022/03/E
Qwarnstrom-e1648747764368.jpg\;163\;183\,medium\;https://icm.jhu.edu/wp-co
ntent/uploads/2022/03/EQwarnstrom-e1648747764368.jpg\;163\;183\,large\;htt
ps://icm.jhu.edu/wp-content/uploads/2022/03/EQwarnstrom-e1648747764368.jpg
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om-e1648747764368.jpg\;163\;183
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984426@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series
CONTACT:
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Elucidating the Neural Control of Movement Using AI<
strong>”
\n
\n
Shreya Saxen
a is broadly interested in the neural control of complex\, coordinated beh
avior. She is an Assistant Professor at the University of Florida’s Depart
ment of Electrical and Computer Engineering. Before this\, Shreya was a Sw
iss National Science Foundation Postdoctoral Fellow at Columbia University
’s Zuckerman Mind Brain Behavior Institute. She did her PhD in the Departm
ent of Electrical Engineering and Computer Science at the Massachusetts In
stitute of Technology studying the closed-loop control of fast movements f
rom a control theory perspective. Shreya received a B.S. in Mechanical Eng
ineering from the Swiss Federal Institute of Technology (EPFL)\, and an M.
S. in Biomedical Engineering from Johns Hopkins University. She is honored
to have been selected as a Rising Star in both Electrical Engineering (20
19) and Biomedical Engineering (2018).
\n
\n
▶RECORDING
\n
\n
\n Abs
tract
\n
\n
“E
lucidating the Neural Control of Movement Using AI”<
/span>
\n
\n
\n
\n
\n
\n
How does the motor cortex achieve
generalizable and purposeful movements from the complex\, nonlinear muscul
oskeletal system? Previous research in the field typically does not cons
ider the biophysical underpinnings of the musculoskeletal system\, and thu
s fails to elucidate the computational role of neural activity in driving
the musculoskeletal system such that the body reaches a desired state. Her
e\, I will present a deep reinforcement learning framework for training re
current neural network controllers that act on anatomically accurate limb
models such that they achieve desired movements. We apply this framework t
o kinematic and neural recordings made in macaques as they perform movemen
ts at different speeds. This framework for the control of the musculoskele
tal system mimics biologically observed neural strategies\, and enables hy
pothesis generation for prediction and analysis of novel movements and neu
ral strategies.
\n
Effectively modeling and quantifying behavior is e
ssential for our understanding of the brain. Modeling behavior across diff
erent subjects and in a naturalistic setting remains a significant challen
ge in the field of behavioral quantification. We develop novel explainable
AI methods for modeling continuously varying differences in behavior\, wh
ich successfully represent distinct features of multi-subject and social b
ehavior in an unsupervised manner. These methods are also successful at un
covering the relationships between recorded neural data and the ensuing be
havior. I will end with future avenues on explainable AI methods for eluci
dating the neural control of movement.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20220906T103000
DTEND;TZID=America/New_York:20220906T113000
LOCATION:Levering Hall: Great Hall
SEQUENCE:0
SUMMARY:Elucidating the Neural Control of Movement Using AI
URL:https://icm.jhu.edu/events/elucidating-the-neural-control-of-movement-u
sing-ai/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2022/08/S
hreya-291x300.jpeg\;183\;189\,medium\;https://icm.jhu.edu/wp-content/uploa
ds/2022/08/Shreya-291x300.jpeg\;183\;189\,large\;https://icm.jhu.edu/wp-co
ntent/uploads/2022/08/Shreya-291x300.jpeg\;183\;189\,full\;https://icm.jhu
.edu/wp-content/uploads/2022/08/Shreya-291x300.jpeg\;183\;189
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984560@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events
CONTACT:Mishka Colombo\; 410-516-4116\; mcolomb4@jhu.edu\; https://tinyurl.
com/2tcz6w4s
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Significance of Event Related Causality (ERC) in Neural N
etworks”
\n
\n
Dr. Anna Korzeniewska obtained MS in Phy
sics with concentration in Medical Physics from University of Warsaw\, Pol
and and PhD in Biological Sciences with concentration in Neurophysiology f
rom Nencki Institute of Experimental Biology\, Polish Academy of Sciences.
Since 2004 she works at Johns Hopkins’ Epilepsy Center. Her research inte
rest is focused on the dynamics of causal interactions among functional an
d pathological neural networks.
\n
\n
▶REC
ORDING
\n
\n
\n Abstract
\n
\n
<
/h2>\n“Significance of Event Related Causality (ERC) in Ne
ural Networks”
\n\n
\n
\n
Neural activity is propagated acro
ss large-scale cortical networks on very brief time scales. Studying such
transient and complex systems calls for a short time-window on one hand\,
and a great extent of recording sites in the brain\, on the other. These d
emands are not easily satisfied\, as short time intervals do not provide e
nough data-points to model the dynamics of large-scale brain networks. The
limitation can be overcome by using multiple realizations of the same pro
cess\, but the price to be paid is that traditional statistical methods ca
nnot be used to assess the significance of event-related changes in the es
timated dynamics of the system. To obtain statistical confidence of the dy
namics of neural interactions among large-scale networks revealed by event
-related causality (ERC)\, we propose using the variance of a two-dimensio
nal moving average. We also propose a criterion for the two-dimensional mo
del selection\, which combines the difference between the smooth estimator
and the real values with the confidence interval. We show that this estim
ator is efficient\, stable\, and ensures precise embedding of statistical
significance in two-dimensional (time-frequency) space. Here\, we show tha
t the method can be used to investigate information flow among eloquent ne
twork\, to provide a guidance for epileptic surgery.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20221004T103000
DTEND;TZID=America/New_York:20221004T113000
LOCATION:Levering Hall: Great Hall @ 3400 N. Charles Street
SEQUENCE:0
SUMMARY:Significance of Event Related Causality (ERC) in Neural Networks
URL:https://icm.jhu.edu/events/significance-of-event-related-causality-erc-
in-neural-networks/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2022/09/J
HUanna_small-290x300.jpg\;222\;230\,medium\;https://icm.jhu.edu/wp-content
/uploads/2022/09/JHUanna_small-290x300.jpg\;222\;230\,large\;https://icm.j
hu.edu/wp-content/uploads/2022/09/JHUanna_small-290x300.jpg\;222\;230\,ful
l\;https://icm.jhu.edu/wp-content/uploads/2022/09/JHUanna_small-290x300.jp
g\;222\;230
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984606@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES:
CONTACT:Mishka\; 14105164116\; mcolomb4@jhu.edu\; https://tinyurl.com/3w5cc
3er
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Biophysically Realistic Cortical Network Models for Simul
ation of Cortical and Intracortical Electrical Stimulations”<
/strong>
\n
\n
Dr. Kudela is an expert in neural signal
data modeling and analysis. His research is focused on computational mode
ling of cortical dynamics (cortical electrical stimulation\, cortical audi
tory processing\, and seizure) to rationalize experimental observations fr
om novel microelectrode recordings in invasively monitored epilepsy patien
ts. He is a computational neuroscientist\, and skilled in many domains ran
ging from computer science and scientific programming to parallel computin
g and high-performance computing. He collaborates with several Johns Hopki
ns investigators working on new medical therapies and devices.
\n
p>\n
▶RECORDING
\n
\n <
div id='tbs_nav_item_25' class='tab-pane'>
\n Abstract
\n
\n
\n“Biophysically Reali
stic Cortical Network Models for Simulation of Cortical and Intracortical
Electrical Stimulations”
\n\n
\n
\nModeling electrical stimulation of neural elements can be p
erformed in two steps. The first step involves the calculation of the spat
ial distributions of the induced electric fields in cortical volume produc
ed by stimulating electrodes. The second step is to model the response of
neuronal elements to an electric field using multicompartmental representa
tions of neurons. The response of an individual neuron to electrical stimu
lation is determined by several factors like neuronal morphology and the c
ortical geometry that affects electric field distribution in the cortical
volume. We use computational models of cortical neurons to investigate the
effects of cortical and intracortical electrical stimulations in a cortic
al volume. Two high-resolution cortical network models will be presented t
hat were developed to study 1) cortical responses to subdural cortical sti
mulations and 2) neuronal recruitment by intracortical microstimulation fo
r restoring touch sensation.
\n
\n▶RECORD
ING
\n
\n
\n
DTSTART;TZID=America/New_York:20221101T103000
DTEND;TZID=America/New_York:20221101T113000
LOCATION:Levering Hall: Great Hall
SEQUENCE:0
SUMMARY:Biophysically Realistic Cortical Network Models for Simulation of C
ortical and Intracortical Electrical Stimulations
URL:https://icm.jhu.edu/events/biophysically-realistic-cortical-network-mod
els-for-simulation-of-cortical-and-intracortical-electrical-stimulations/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2022/10/P
icture1.png\;148\;164\,medium\;https://icm.jhu.edu/wp-content/uploads/2022
/10/Picture1.png\;148\;164\,large\;https://icm.jhu.edu/wp-content/uploads/
2022/10/Picture1.png\;148\;164\,full\;https://icm.jhu.edu/wp-content/uploa
ds/2022/10/Picture1.png\;148\;164
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984668@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES:
CONTACT:Mishka\; 14105164116\; mcolomb4@jhu.edu\; https://icm.jhu.edu/semin
ar-series/
DESCRIPTION:
DTSTART;TZID=America/New_York:20221206T103000
DTEND;TZID=America/New_York:20221206T113000
LOCATION:Zoom: email mishka@jhu.edu for link
SEQUENCE:0
SUMMARY:“Evolutionary Dynamics of Tumor Progression”
URL:https://icm.jhu.edu/events/evolutionary-dynamics-of-tumor-progression/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2022/11/i
vana-1.jpg\;200\;194\,medium\;https://icm.jhu.edu/wp-content/uploads/2022/
11/ivana-1.jpg\;200\;194\,large\;https://icm.jhu.edu/wp-content/uploads/20
22/11/ivana-1.jpg\;200\;194\,full\;https://icm.jhu.edu/wp-content/uploads/
2022/11/ivana-1.jpg\;200\;194
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984738@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events\,Special Sem
inars
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://tinyurl.co
m/4v8n6rcw
DESCRIPTION:
DTSTART;TZID=America/New_York:20230207T103000
DTEND;TZID=America/New_York:20230207T113000
LOCATION:Levering Hall: Great Hall
SEQUENCE:0
SUMMARY:Resting State fMRI in Epilepsy for Seizure Onset Localization: Evi
dence and Methods
URL:https://icm.jhu.edu/events/resting-state-fmri-in-epilepsy-for-seizure-o
nset-localization-evidence-and-methods/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/01/B
oerwinkle-scaled-e1673628465991.jpg\;158\;168\,medium\;https://icm.jhu.edu
/wp-content/uploads/2023/01/Boerwinkle-scaled-e1673628465991.jpg\;158\;168
\,large\;https://icm.jhu.edu/wp-content/uploads/2023/01/Boerwinkle-scaled-
e1673628465991.jpg\;158\;168\,full\;https://icm.jhu.edu/wp-content/uploads
/2023/01/Boerwinkle-scaled-e1673628465991.jpg\;158\;168
X-TAGS;LANGUAGE=en-US:Distinguished Seminar Series\,Varina Boerwinkle
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984763@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://wse.zoom.u
s/j/92808587799
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Harnessing Artificial
\nIntelligence for Healthcare<
/strong>”
\n
\n
\n
Sushmita Mitra is a
full professor at the Machine Intelligence Unit (MIU)\, Indian Statistical
Institute\, Kolkata. From 1992 to 1994 she was in the RWTH\, Aachen\, Ger
many as a DAAD Fellow. She was a Visiting Professor in the Computer
Science Departments of the University of Alberta\, Edmonton\, Canada\; Me
iji University\, Japan\; and Aalborg University Esbjerg\, Denmark. Dr. Mit
ra received the National Talent Search Scholarship (1978-1983) from NCERT\
, India\, the University Gold Medal in 1988\, the IEEE TNN Outst
anding Paper Award in 1994 for her pioneering work in neuro-fuzzy comp
uting\, the CIMPA-INRIA-UNESCO Fellowship in 1996\, and Fulbright-Nehru
Senior Research Fellowship in 2018-2020. She was the INAE Chair Pr
ofessor during 2018-2020. Dr. Mitra has been awarded the prestigious J
. C. Bose National Fellowship\, 2021.
\n
Dr. Mitra is th
e author of the books “Neuro-Fuzzy Pattern Recognition: Methods in Soft Co
mputing” and “Data Mining: Multimedia\, Soft Computing\, and Bioinformatic
s” published by John Wiley\, and “Introduction to Machine Learning and Bio
informatics”\, Chapman & Hall/CRC Press\, beside a host of other edited bo
oks. Dr. Mitra has guest edited special issues of several journals\, is an
Associate Editor of “IEEE/ACM Trans. on Computational Biology and Bioi
nformatics“\, “Information Sciences“\, “Fundamenta Informati
ca“\, “Computers in Biology and Medicine“\, SN Computer Scie
nces and is a Founding Associate Editor of “Wiley Interdisciplinary
Reviews: Data Mining and Knowledge Discovery (WIRE DMKD)“. She has mo
re than 150 research publications in referred international journals. Acco
rding to the Stanford List\, Dr. Mitra is ranked among the top 2% scientis
ts worldwide in the domain of Artificial Intelligence and Image Processing
.
\n
Dr. Mitra is a Fellow of the IEEE\, The Worl
d Academy of Sciences (TWAS)\, Indian National Science Academy (INSA)\, In
ternational Association for Pattern Recognition (IAPR)\, Asia-Pacific Arti
ficial Intelligence Association (AAIA)\, and Fellow of the Indian N
ational Academy of Engineering (INAE) and The National Academy of Sciences
\, India (NASI). She serves as a Member of the Inter-Academy Panel Panel f
or Women in STEMM. She has visited more than 30 countries as a Plenary/Inv
ited Speaker or an academic visitor. She served in the capacity of General
Chair\, Program Chair\, Tutorial Chair\, of many international conference
s\; was the Chair\, IEEE Kolkata Section (2021-2022) and an IEEE CIS Di
stinguished Lecturer. Her current research interests include data scie
nce\, machine learning\, soft computing\, medical image processing\, and B
ioinformatics.
\n
\n
▶RE
CORDING
\n
\n
\n Abstract
\n
\n
\n
“Harnessing Artificial
\nIntelligence for He
althcare”
\n
\n
The talk will focus on the role of Artificial Intellige
nce and Learning in the domain of healthcare. Topics like Genomics\, Radio
mics\, Radiogenomics\, and Personalized Medicine will be discussed in this
perspective. Some research applications made by our group in these areas
will be described. These include segmentation and survival prediction in G
BM tumors from MRI scans of the brain\; screening of covid -19 from X-ray
images of the lungs\; and early detection of diabetic retinopathy from fun
dus images of the eye.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20230307T103000
DTEND;TZID=America/New_York:20230307T113000
LOCATION:Gilman Hall: Gilman 50 (Marjorie Fisher Auditorium)
SEQUENCE:0
SUMMARY:Harnessing Artificial Intelligence for Healthcare
URL:https://icm.jhu.edu/events/harnessing-artificial-intelligence-for-healt
hcare/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/02/S
hushmita-Mitra.jpg\;213\;200\,medium\;https://icm.jhu.edu/wp-content/uploa
ds/2023/02/Shushmita-Mitra.jpg\;213\;200\,large\;https://icm.jhu.edu/wp-co
ntent/uploads/2023/02/Shushmita-Mitra.jpg\;213\;200\,full\;https://icm.jhu
.edu/wp-content/uploads/2023/02/Shushmita-Mitra.jpg\;213\;200
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984789@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Events\,Special Seminars
CONTACT:Sabrina Sengupta\; ssengu19@jhu.edu
DESCRIPTION:
Register for the event by clicking here.
\n
DTSTART;TZID=America/New_York:20230327T170000
DTEND;TZID=America/New_York:20230327T200000
LOCATION:Hackerman B17
SEQUENCE:0
SUMMARY:CM Night
URL:https://icm.jhu.edu/events/cm-night/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/03/C
M-Night-Flyer-2023.png\;8203\;10626\,medium\;https://icm.jhu.edu/wp-conten
t/uploads/2023/03/CM-Night-Flyer-2023.png\;8203\;10626\,large\;https://icm
.jhu.edu/wp-content/uploads/2023/03/CM-Night-Flyer-2023.png\;8203\;10626\,
full\;https://icm.jhu.edu/wp-content/uploads/2023/03/CM-Night-Flyer-2023.p
ng\;8203\;10626
X-TAGS;LANGUAGE=en-US:CM Night
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984874@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://wse.zoom.u
s/j/96264419972
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Reverse Engineering Chronic Mechanisms of Closed-Loop Bra
in Stimulation for Neuropsychiatric Disorders”
\n
\n
Dr. Ankit Khambhati is a bi
omedical engineer who specializes in the development of computational neur
otechnology to map and modulate large-scale neural circuits affected by br
ain network disorders. He is an Assistant Professional Researcher in the D
epartment of Neurological Surgery at the University of California\, San Fr
ancisco. There he leads a research program focused on network neuromodulat
ion and control\, which integrates network science and control theory with
brain electrical recordings and implantable devices to identify electroph
ysiologic biomarkers and develop stimulation-based strategies for rehabili
tating or rewiring impaired circuits. Dr. Khambhati is currently using the
se techniques to investigate and optimize closed-loop brain stimulation th
erapy for treatment-resistant epilepsy and depression. He previously earne
d his B.S. in Electrical and Computer Engineering from Carnegie Mellon Uni
versity and Ph.D. in Bioengineering at the University of Pennsylvania\, an
d he completed a postdoctoral fellowship in Neuroengineering at the Univer
sity of California\, San Francisco.
\n
\n
▶RECORDING
\n
\n
\n Abstract
\n
\n
\n
“Reverse
Engineering Chronic Mechanisms of Closed-Loop Brain Stimulation for Neurop
sychiatric Disorders”
\n
\n
\n
Closed
-loop neuromodulation therapy that detects imminent paroxysmal events and
rapidly delivers electrical stimulation to the brain using a chronically i
mplanted device is an emerging treatment for pharmacoresistant neurologic
or psychiatric disorders. Calibration of closed-loop therapy involves “exp
ert-in-the-loop” optimization of device parameters that specify where\, wh
en\, and how electrical stimulation pulses should be delivered to the brai
n for individual patients. A personalized stimulation strategy involves ta
rgeting discrete nodes specific to an individual’s dysfunctional brain net
work and triggering therapeutic stimulation based on neural biomarkers tha
t encode the unique constellation of an individual’s symptoms. In this tal
k\, I will present an idealized model of closed-loop stimulation therapy a
nd its adaptation as a treatment for brain network disorders such as epile
psy and major depressive disorder. Drawing on a range of tools across engi
neering and neuroscience disciplines — machine learning\, graph theory\, s
timulation-based system identification\, and chronic human intracranial EE
G from implanted devices – I will identify biomarkers related to naturalis
tic fluctuation in disease state and characterize effects of neurostimulat
ion on functional brain network activity and connectivity. Based on these
learnings\, I will propose an alternate mechanism of closed-loop therapeut
ic efficacy based on brain network plasticity and discuss opportunities fo
r next-generation devices.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20230404T103000
DTEND;TZID=America/New_York:20230404T113000
LOCATION:Zoom
SEQUENCE:0
SUMMARY:Reverse Engineering Chronic Mechanisms of Closed-Loop Brain Stimula
tion for Neuropsychiatric Disorders
URL:https://icm.jhu.edu/events/reverse-engineering-chronic-mechanisms-of-cl
osed-loop-brain-stimulation-for-neuropsychiatric-disorders/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/03/K
hambhati-Headshot-e1679086948434.jpg\;197\;192\,medium\;https://icm.jhu.ed
u/wp-content/uploads/2023/03/Khambhati-Headshot-e1679086948434.jpg\;197\;1
92\,large\;https://icm.jhu.edu/wp-content/uploads/2023/03/Khambhati-Headsh
ot-e1679086948434.jpg\;197\;192\,full\;https://icm.jhu.edu/wp-content/uplo
ads/2023/03/Khambhati-Headshot-e1679086948434.jpg\;197\;192
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984912@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES:
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://wse.zoom.u
s/j/94823269431
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Leveraging Modern Diagnostics to Personalize Treatment fo
r Multidrug Resistant Tuberculosis”\n
\n
Jeff Tornheim\, MD\, MPH is an Assistant Professor of Medi
cine\, Pediatrics\, and International Health in the Division of Infectious
Diseases at Johns Hopkins University and a TB clinician at the Baltimore
City Health Department. He completed a combined residency in Internal Medi
cine and Pediatrics at the Yale University School of Medicine and a clinic
al fellowship in Infectious Diseases at the Johns Hopkins University Schoo
l of Medicine before joining the faculty in 2017. For the past 20 years he
has engaged in clinical care\, physician education\, and translational re
search in India\, South America\, and sub-Saharan Africa. He is a member o
f the AIDS Clinical Trials Group Tuberculosis Transformative Science Group
\, the IMPAACT Network TB Scientific Committee\, and a principal investiga
tor in the RePORT India Consortium. His research focuses on cohort epidemi
ology and implementation of multi-omic diagnostic tools for personalized t
herapy of drug-resistant tuberculosis\, combining whole genome sequencing\
, expanded susceptibility testing\, and therapeutic drug monitoring to imp
rove treatment outcomes while reducing side effects.
\n
\n
▶RECORDING [available here after event]
\n
\n
\n Abstract
\n
\n
h2>\n\n“Leveraging M
odern Diagnostics to Personalize Treatment for Multidrug Resistant Tubercu
losis”
\n\n
\n
\n
▶RECO
RDING [available here after event]
\n
\n
\n
DTSTART;TZID=America/New_York:20230502T103000
DTEND;TZID=America/New_York:20230502T113000
LOCATION:Gilman Hall: Gilman 50 (Marjorie Fisher Auditorium)
SEQUENCE:0
SUMMARY:“Leveraging Modern Diagnostics to Personalize Treatment for Multidr
ug Resistant Tuberculosis”
URL:https://icm.jhu.edu/events/leveraging-modern-diagnostics-to-personalize
-treatment-for-multidrug-resistant-tuberculosis/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/04/t
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du/wp-content/uploads/2023/04/tornheim_headshot-scaled-e1682432235479.jpg\
;259\;263
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984942@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events
CONTACT:Mishka Colombo\; 667-306-8941\; mcolomb4@jhu.edu\; https://wse.zoom
.us/j/97140116361
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Mathematical and Computational Frameworks for Adaptively
Benchmarking Patients in States of Health\, Disease\, and Recovery”
\n
\n
Dr. Brody Foy is a research fellow in Systems Bio
logy at Harvard Medical School and Massachusetts General Hospital. He earn
ed a BMath at Queensland University of Technology\, and DPhil in Computer
Science from the University of Oxford\, as a Rhodes Scholar. Dr Foy uses m
athematical and computational approaches to quantify blood cell dynamics i
n acute and chronic disease settings. He is particularly interested in how
we can better utilize routine clinical laboratory testing to generate phy
siologic and clinical insights. In the winter he will be starting a lab at
the University of Washington\, Department of Laboratory Medicine & Pathol
ogy\, as an acting Assistant Professor.
\n
\n
▶RECORDING [available here after event]
\n
\n
\n Abstract
\n
\n
\n
“
Mathematical and Computational Frameworks for Adaptively Benchmarking Pati
ents in States of Health\, Disease\, and Recovery”
span>
\n
Laboratory testing is a cornerstone of modern medici
ne. While cutting-edge assays are constantly in development\, the bulk of
worldwide clinical testing is dominated by only a handful of markers. Thes
e ‘boring’ markers are regularly used in patient evaluation – but the phys
iologic insights they can provide are often overlooked. In this talk I wil
l explore how mathematical and statistical methods can be used to generate
deep clinical and physiologic insights from routine clinical laboratory t
ests such as the complete blood count. From my own research I will show ho
w careful analysis and modelling of biomarker dynamics can provide excitin
g and novel insights into homeostatic recovery and regulation\, chronic il
lness\, and physiologic shifts such as pregnancy and menopause.
\n
<
/p>\n
▶RECORDING [available here after ev
ent]
\n
\n
\n
DTSTART;TZID=America/New_York:20230905T160000
DTEND;TZID=America/New_York:20230905T171500
LOCATION:Clark 110
SEQUENCE:0
SUMMARY:Mathematical and Computational Frameworks for Adaptively Benchmarki
ng Patients in States of Health\, Disease\, and Recovery
URL:https://icm.jhu.edu/events/mathematical-and-computational-frameworks-fo
r-adaptively-benchmarking-patients-in-states-of-health-disease-and-recover
y/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/07/B
rody-Foy-Headshot-1.png\;219\;208\,medium\;https://icm.jhu.edu/wp-content/
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.edu/wp-content/uploads/2023/07/Brody-Foy-Headshot-1.png\;219\;208\,full\;
https://icm.jhu.edu/wp-content/uploads/2023/07/Brody-Foy-Headshot-1.png\;2
19\;208
X-TAGS;LANGUAGE=en-US:Brody Foy\,Fall 2023\,ICM Distinguished Seminar Serie
s\,Seminar
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984963@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Events\,Tea Time
CONTACT:Mishka Colombo\; mcolomb4@jhu.edu
DESCRIPTION:
ICM Tea <
strong>Time
\n
Please join us today for tea\, coffee\, snack
s\, and conversations with your fellow ICM colleagues.
\n
Tuesdays 4:
30PM-5:00PM
\n
Second floor hallway of Hackerman Hall
\n
Click <
a href='https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2F
forms.gle%2F7op4232rz9ziJeCk7&data=05%7C01%7Cmishka%40jhu.edu%7C37a7e6c1a4
fd43e2c3d708db6c1e5e75%7C9fa4f438b1e6473b803f86f8aedf0dec%7C0%7C0%7C638222
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Xdn5Cu2YbCOCqWECaegauNs%3D&reserved=0'>Here to RSVP
\n
*Seminars
will replace Team Time on the first Tuesday of each month during the acade
mic calendar.
DTSTART;TZID=America/New_York:20230912T163000
DTEND;TZID=America/New_York:20230912T170000
EXRULE:FREQ=MONTHLY;BYDAY=1TU;WKST=MO
LOCATION:Hackerman Hall 2nd Floor Hallway
RRULE:FREQ=WEEKLY;BYDAY=TU;WKST=MO
SEQUENCE:0
SUMMARY:ICM Tea Time
URL:https://icm.jhu.edu/events/icm-tea-time/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/09/I
CM-Tea-Time-Sign.jpg\;781\;1078\,medium\;https://icm.jhu.edu/wp-content/up
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/wp-content/uploads/2023/09/ICM-Tea-Time-Sign.jpg\;781\;1078\,full\;https:
//icm.jhu.edu/wp-content/uploads/2023/09/ICM-Tea-Time-Sign.jpg\;781\;1078
X-TAGS;LANGUAGE=en-US:Tea Time\,Tuesdays
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4984969@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Special Seminars
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://wse.zoom.u
s/j/95676026583
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Leveraging P
atient-Reported Outcome Dynamics to Predict Treatment Response”
\n
\n
Dr. Renee Brady completed her postdoctoral training i
n the Integrated Mathematical Oncology Department of the Houston Lee Moffi
tt Cancer Center & Research Institute after earning her Bachelor of Scienc
e in Mathematics from Florida A&M University and her Masters and PhD from
North Carolina State University. Her research focuses on developing novel\
, predictive models of non- and minimally-invasive biomarkers. After caref
ul model calibration and validation\, these models can be used to propose
alternative treatment strategies that can ultimately be used to reduce can
cer health disparities.
\n
\n
<
strong>▶RECORDING
\n
\n
\n Abstract
\n
\n
\n
“Leveraging Patient-Reported Outcome Dynamics to Predict Treatm
ent Response”
\n
Patient-reporte
d outcomes (PROs)\, collected using standardized questionnaires at various
time
\npoints throughout a patient’s care\, provide an unbiased asse
ssment of a patient’s health
\ncondition\, reported directly by the p
atient. Recent studies have shown that changes in PROs
\nover time ca
n be early indicators of clinically important events such as cancer develo
pment and
\nsurvival. While incredibly promising\, these studies fail
to consider the patient-specific dynamics
\nof individual PROs and h
ow they might be leveraged to predict individual patient responses to
\ntreatment. This is especially important in non-small cell lung cancer (
NSCLC)\, which has the
\nlowest survival rates among all cancers. In
this talk\, we demonstrate how PRO dynamics can be
\nused as inter-ra
diographic predictors of tumor volume changes. That is\, how PROs can be\nleveraged between radiographic scans to predict tumor volume dynamic
s. This is assessed in
\n108 NSCLC patients receiving immune checkpoi
nt inhibitors. The patients completed biweekly
\nPRO questionnaires a
nd received monthly tumor volume scans. We found that changes in
\nvo
lume were significantly correlated with dizziness (p<0.005)\, insomnia (p
<0.05)\, and fatigue
\n(p<0.05). Further analysis revealed that chang
es in insomnia could predict progressive disease
\nwith a 77% accurac
y\, with correct predictions of progressive disease occurring on average 4
5
\ndays prior to the next imaging study. Our study is an important f
irst step in understanding how
\nPROs can be utilized as a non-invasi
ve and easily-obtained biomarker of when to change
\ntreatment to del
ay the development of treatment progression.
\n
\n
▶RECORDING
span>
\n
\n
\n
DTSTART;TZID=America/New_York:20231003T160000
DTEND;TZID=America/New_York:20231003T170000
LOCATION:Clark 110
SEQUENCE:0
SUMMARY:Leveraging Patient-Reported Outcome Dynamics to Predict Treatment
Response
URL:https://icm.jhu.edu/events/leveraging-patient-reported-outcome-dynamics
-to-predict-treatment-response/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/09/H
eadshot_Brady-2.jpg\;150\;188\,medium\;https://icm.jhu.edu/wp-content/uplo
ads/2023/09/Headshot_Brady-2.jpg\;150\;188\,large\;https://icm.jhu.edu/wp-
content/uploads/2023/09/Headshot_Brady-2.jpg\;150\;188\,full\;https://icm.
jhu.edu/wp-content/uploads/2023/09/Headshot_Brady-2.jpg\;150\;188
X-TAGS;LANGUAGE=en-US:Renee Brady\,Special Seminar
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4985033@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://wse.zoom.u
s/j/98797164936
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Big Data Approaches to Study
\nIntercellular Signali
ng during
\nTumor Immune Evasion”
\n
\n
Dr. Peng
Jiang started his research program at the National Cancer Institute (NCI)
in July 2019. His Lab focuses on developing big-data and artificial intel
ligence frameworks to identify biomarkers and new therapeutic approaches f
or cancer immunotherapies in solid tumors. Before joining NCI\, he finishe
d his postdoctoral training at the Dana Farber Cancer Institute and Harvar
d University. During his postdoctoral research\, Peng developed computatio
nal frameworks that repurposed public domain data to identify biomarkers a
nd regulators of cancer immunotherapy resistance. Notably\, his computatio
nal model TIDE revealed that cancer cells could utilize the self-protectio
n strategy of cytotoxic lymphocytes to resist lymphocyte killing under imm
une checkpoint blockade. Dr. Peng finished his Ph.D. at the Department of
Computer Science & Lewis Sigler Genomics Institute at Princeton University
\, and his undergraduate study with the highest national honors at the Dep
artment of Computer Science at Tsinghua University (GPA rank 1st in his ye
ar). He is a recipient of the NCI K99 Pathway to Independence Award\, the
Scholar-In-Training Award of the American Association of Cancer Research\,
and the Technology Innovation Award of the Cancer Research Institute.
\n
\n
▶RECORDING
\n
\n
\n Abstract
\n
\n
h2>\n“Big Data Approaches to Study
\nIntercellular Si
gnaling during
\nTumor Immune Evasion”
span>
\n
My talk will present three computational frameworks we deve
loped to study cytokine signaling activities and cell-cell communications
during the antitumor immune response. The basic immunology tool to study c
ytokine signaling mostly measures cytokine release\, which is transient an
d does not represent downstream target activities. Therefore\, we first de
veloped the CytoSig platform\, providing a database of target genes modula
ted by cytokines and a predictive model of cytokine signaling cascades fro
m transcriptomic profiles. We collected 20\,591 transcriptome profiles for
human cytokine\, chemokine\, and growth factor responses. This atlas of t
ranscriptional patterns induced by cytokines enabled the reliable predicti
on of signaling activities in distinct cell populations in infectious dise
ases\, chronic inflammation\, and cancer using bulk and single-cell transc
riptomic data. CytoSig revealed previously unidentified roles of many cyto
kines\, such as BMP6 as an anti-inflammatory factor. Then\, based on CytoS
ig\, we developed Tres\, a computational model utilizing single-cell trans
criptomic data to identify signatures of T cells that are resilient to imm
unosuppressive signals. Tres reliably predicts clinical responses to immun
otherapy in multiple cancer types using bulk T cell transcriptomic data fr
om pre-treatment patient tumors or infusion/pre-manufacture samples for ce
llular immunotherapies. Further\, Tres identified FIBP as a candidate immu
notherapy target to potentiate adoptive cell therapy efficacies. Last\, I
will briefly show our SpaCET framework for deconvolving cell fractions and
identifying cell-cell interactions in tumor spatial transcriptomics data.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20231107T160000
DTEND;TZID=America/New_York:20231107T170000
LOCATION:Clark 110
SEQUENCE:0
SUMMARY:Big Data Approaches to Study Intercellular Signaling during Tumor I
mmune Evasion
URL:https://icm.jhu.edu/events/big-data-approaches-to-study-intercellular-s
ignaling-during-tumor-immune-evasion/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/10/1
1-7-23-Peng-Jiang-Headshot-1.jpg\;232\;232\,medium\;https://icm.jhu.edu/wp
-content/uploads/2023/10/11-7-23-Peng-Jiang-Headshot-1.jpg\;232\;232\,larg
e\;https://icm.jhu.edu/wp-content/uploads/2023/10/11-7-23-Peng-Jiang-Heads
hot-1.jpg\;232\;232\,full\;https://icm.jhu.edu/wp-content/uploads/2023/10/
11-7-23-Peng-Jiang-Headshot-1.jpg\;232\;232
X-TAGS;LANGUAGE=en-US:Distinguished Seminar Series\,Peng Jiang
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4985055@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events
CONTACT:Mishka Colombo\; 667-306-8941\; mcolomb4@jhu.edu\; https://wse.zoom
.us/j/94073678803
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Predicting Immunogenic
\nNeoepitopes in Cancer<
strong>”
\n
\n
Dr. Rachel Karchin is a Profes
sor in the Departments of Biomedical Engineering and Oncology\, with a sec
ondary appointment in Computer Science at Johns Hopkins University\, She r
eceived a Ph.D. in Computer Science from the University of California\, Sa
nta Cruz in 2003\, spent three years as a postdoctoral fellow in the Depar
tment of Biopharmaceutical Sciences at University of California\, San Fran
cisco\, and joined the Hopkins faculty in 2006. Working closely with cance
r geneticists\, pathologists and oncologists\, her lab has developed novel
tools to identify pathogenic missense mutations and driver genes\, to mod
el tumor evolution from next-generation sequencing data\, and to predict t
umor neoepitopes.
\n
\n
▶RECORDING
\n
\n
\n Abstract
\n
\n
\n
“Predicting Immunogenic
\n
Neoepitopes in Cancer”
\n
Identi
fying neoepitopes that elicit an adaptive immune response is a major bottl
eneck to developing personalized cancer vaccines. Experimental validation
of candidate neoepitopes is extremely resource intensive and the vast majo
rity of candidates are non-immunogenic\, creating a needle-in-a-haystack p
roblem. Here we address this challenge\, presenting computational methods
for predicting class I major histocompatibility complex (MHC-I) epitopes a
nd identifying immunogenic neoepitopes with improved precision. The BigMHC
method comprises an ensemble of seven pan-allelic deep neural networks tr
ained on peptide-MHC eluted ligand data from mass spectrometry assays and
transfer learned on data from assays of antigen-specific immune response.
Compared with four state-of-the-art classifiers\, BigMHC significantly imp
roves the prediction of epitope presentation on a test set of 45\,409 MHC
ligands among 900\,592 random negatives (area under the receiver operating
characteristic = 0.9733\; area under the precision-recall curve = 0.8779)
. After transfer learning on immunogenicity data\, BigMHC yields significa
ntly higher precision than seven state-of-the-art models in identifying im
munogenic neoepitopes\, making BigMHC effective in clinical settings.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20231205T160000
DTEND;TZID=America/New_York:20231205T170000
LOCATION:Clark 110
SEQUENCE:0
SUMMARY:Predicting Immunogenic Neoepitopes in Cancer
URL:https://icm.jhu.edu/events/predicting-immunogenic-neoepitopes-in-cancer
/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2023/11/R
achel-Headshot-Photo.jpg\;300\;300\,medium\;https://icm.jhu.edu/wp-content
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hu.edu/wp-content/uploads/2023/11/Rachel-Headshot-Photo.jpg\;300\;300\,ful
l\;https://icm.jhu.edu/wp-content/uploads/2023/11/Rachel-Headshot-Photo.jp
g\;300\;300
X-TAGS;LANGUAGE=en-US:Rachel Karchin\,Seminar
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4985083@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series
CONTACT:Mishka Colombo\; 4105164116\; mcolomb4@jhu.edu\; https://wse.zoom.u
s/j/99695749134
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Mathematical and Computational Frameworks for Adaptively
Benchmarking Patients in States of Health\, Disease\, and Recovery”
\n
\n
Dr. Brody Foy is a junior faculty member at the Univ
ersity of Washington\, Department of Laboratory Medicine & Pathology. Brod
y completed his DPhil in Computer Science at the University of Oxford\, as
a Rhodes Scholar\, and undertook postdoctoral training at Harvard Medical
School & Massachusetts General Hospital. A mathematician by training\, Br
ody’s research is focused on using math modelling and machine learning to
improve utilization of clinical laboratory data\, and increase the quality
of information extraction from blood testing. His lab uses computational
tools to learn about human physiology\, improve clinical workflows\, and d
evelop novel tools for patient care.
\n
\n
▶REC
ORDING
\n
\n
\n Abstract
\n
\n
\n
“Mathematical and Computational Frameworks
for Adaptively Benchmarking Patients in States of Health\, Disease\, and R
ecovery”
\n
Laboratory testing i
s a cornerstone of modern medicine. While cutting-edge assays are constant
ly in development\, the bulk of worldwide clinical testing is dominated by
only a handful of markers. These ‘boring’ markers are regularly used in p
atient evaluation – but the physiologic insights they can provide are ofte
n overlooked. In this talk I will explore how mathematical and statistical
methods can be used to generate deep clinical and physiologic insights fr
om routine clinical laboratory tests such as the complete blood count. Fro
m my own research I will show how careful analysis and modelling of biomar
ker dynamics can provide exciting and novel insights into homeostatic reco
very and regulation\, chronic illness\, and physiologic shifts such as pre
gnancy and menopause.
\n
\n
▶RECORDING
\n
\n
\n
DTSTART;TZID=America/New_York:20240305T160000
DTEND;TZID=America/New_York:20240305T170000
LOCATION:Clark 110
SEQUENCE:0
SUMMARY:Mathematical and Computational Frameworks for Adaptively Benchmarki
ng Patients in States of Health\, Disease\, and Recovery
URL:https://icm.jhu.edu/events/mathematical-and-computational-frameworks-fo
r-adaptively-benchmarking-patients-in-states-of-health-disease-and-recover
y-2/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2024/01/B
rody-Foy_Cropped.jpg\;220\;220\,medium\;https://icm.jhu.edu/wp-content/upl
oads/2024/01/Brody-Foy_Cropped.jpg\;220\;220\,large\;https://icm.jhu.edu/w
p-content/uploads/2024/01/Brody-Foy_Cropped.jpg\;220\;220\,full\;https://i
cm.jhu.edu/wp-content/uploads/2024/01/Brody-Foy_Cropped.jpg\;220\;220
X-TAGS;LANGUAGE=en-US:Brody Foy
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4985143@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES;LANGUAGE=en-US:Distinguished Seminar Series\,Events
CONTACT:Mishka Colombo\; 667-306-8941\; mcolomb4@jhu.edu\; https://wse.zoom
.us/j/97571700790
DESCRIPTION:
\n
Jump to:
\n
\n
\n
\n Bio
\n
“Overcoming Analytic Challenges in Microbiome Science
”
\n
UMIACS -Dr. Mihai Pop
\n
M
ihai Pop is a professor of computer science and director of the University
of Maryland
\nInstitute for Advanced Computer Studies. He develops c
omputational approaches for analyzing
\nmicrobial communities\, parti
cularly for characterizing their strain-level diversity. Other
\ninte
rests include biological databases\, antibiotic resistance\, and software
testing. His lab
\nhas developed several widely used open-source soft
ware tools for the analysis of genomic and
\nmetagenomic data. Pop te
aches at all academic professional levels\, and is particularly
\nint
erested in developing open educational resources for introductory computer
science and
\nbioinformatics. He strongly advocates for inclusion an
d diversity within the scientific
\ncommunity. Pop completed his unde
rgraduate studies in 1994 at the Politehnica University in
\nBuchares
t\, Romania\, received his Ph.D. in computer science from Johns Hopkins Un
iversity in 2000\,
\nand has been at the University of Maryland since
2005. He is a fellow of the Association of
\nComputing Machinery and
of the International Society for Computational Biology.
\n
\n
▶RECORDING [available here after event]
\n
\n
\n Abstract
\n
\n
\n
“Overcoming Analytic Challenges in
Microbiome Science”
\n
\n
As microbiome research matures\, it has beco
me clear that a better understanding of the
\nmicrobial communities i
nhabiting our world is key to a better understanding of our
\nenviron
ment and of animal and human health. At the same time\, we have become awa
re of
\nthe limitations current microbiome technologies have\, and of
the tremendous challenges
\nposed by the analysis of the massive dat
a sets generated in microbiome studies. In my
\ntalk I will describe
some of the research taking place in my lab aimed at developing
\ncom
putational tools for microbiome analyses. I will specifically focus on cha
llenges
\nrelated to the structure of biological databases\, and the
resulting impact on the
\ninsights that can be derived from microbiom
e data.
\n
\n
▶RECORDING [avail
able here after event]
\n
\n
\n <
/div>
DTSTART;TZID=America/New_York:20240402T160000
DTEND;TZID=America/New_York:20240402T170000
SEQUENCE:0
SUMMARY:Overcoming Analytic Challenges in Microbiome Science
URL:https://icm.jhu.edu/events/overcoming-analytic-challenges-in-microbiome
-science/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2024/01/M
ihai-Pop.jpg\;220\;220\,medium\;https://icm.jhu.edu/wp-content/uploads/202
4/01/Mihai-Pop.jpg\;220\;220\,large\;https://icm.jhu.edu/wp-content/upload
s/2024/01/Mihai-Pop.jpg\;220\;220\,full\;https://icm.jhu.edu/wp-content/up
loads/2024/01/Mihai-Pop.jpg\;220\;220
X-TAGS;LANGUAGE=en-US:Mihai Pop
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4985119@icm.jhu.edu
DTSTAMP:20240328T145456Z
CATEGORIES:
CONTACT:Sabrina Sengupta\; 410-516-6892\; ssengu19@jhu.edu\; https://forms.
gle/jnG6nmcSgkZNMWmp8
DESCRIPTION:
Register for the event by clicking here.
DTSTART;TZID=America/New_York:20240402T170000
DTEND;TZID=America/New_York:20240402T193000
LOCATION:Hackerman B17
SEQUENCE:0
SUMMARY:CM Night
URL:https://icm.jhu.edu/events/cm-night-2/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;https://icm.jhu.edu/wp-content/uploads/2024/03/C
M-Night-Flyer-2024-002-1-791x1024.png\;791\;1024\,medium\;https://icm.jhu.
edu/wp-content/uploads/2024/03/CM-Night-Flyer-2024-002-1-791x1024.png\;791
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t/uploads/2024/03/CM-Night-Flyer-2024-002-1-791x1024.png\;791\;1024
END:VEVENT
END:VCALENDAR