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BEGIN:VEVENT
SUMMARY:Stanislav Shvartsman (Princeton University)
DTSTART:20200615T150000Z
DTEND:20200615T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/1
DESCRIPTION:Title: How to make a large cell\nby Stanislav Shvartsman (Princet
on University) as part of Mathematical and Computational Biology Seminar S
eries\n\n\nAbstract\nTo see a single cell\, one usually requires a microsc
ope. However\, some cells can be seen with the naked eye\; a chicken egg\,
for example\, is a macroscopic object that contains just one cell. The la
rgest human cell\, at ~50 microns in diameter\, is also an egg - the oocyt
e - and regularly features in popular science movies on in vitro fertiliza
tion and early stages of our development. Across species\, proper developm
ent of an egg is critically dependent on auxiliary cells that nurse the oo
cyte\, supplying it with components that cannot be synthesized by the oocy
te itself. Using the fruit fly\, Drosophila melanogaster as an experimenta
l model\, one that provides unrivaled opportunities for combining advanced
genetic perturbations and high-resolution imaging of molecular and cellul
ar processes\, I will present data from our latest studies that suggest th
at growing oocytes can control their own nursing by the auxiliary cells. O
ur experiments have also led us to an interesting class of mathematical mo
dels in which limit cycle oscillators are coupled on tree-like networks. C
omputational analysis of synchronized regimes in these models makes clear
experimental predictions and moves us one step closer to understanding the
mechanisms that coordinate the growth and development of one of the anima
l’s largest cells.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Helen Byrne (University of Oxford)
DTSTART:20200629T150000Z
DTEND:20200629T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/2
DESCRIPTION:Title: Data-driven mathematical oncology: evolution\, revolution or c
o-evolution?\nby Helen Byrne (University of Oxford) as part of Mathema
tical and Computational Biology Seminar Series\n\n\nAbstract\nThe past twe
nty-five years have witnessed an unparalleled increase in understanding of
cancer biology. This transformation is exemplified by Hanahan and Weinber
g's decision in 2011 to expand their Hallmarks of Cancer from six traits t
o ten! At the same time\, the prominence of mathematical modelling as a to
ol for unravelling the complex processes that contribute to the initiation
and progression of tumours has increased\, \n\nIn this talk\, I will revi
sit early models of avascular tumour growth\, angiogenesis and tumour bloo
d flow. Following Hanahan and Weinberg's lead\, I will reflect on how clos
er collaboration with cancer scientists and\, in particular\, access to ex
perimental data have driven extensions to these models which increase thei
r ability to generate qualitative and quantitative predictions about the g
rowth and response to treatment of solid tumours.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Natalia Komarova (University of California Irvine)
DTSTART:20200713T150000Z
DTEND:20200713T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/3
DESCRIPTION:Title: Mathematics of Evolution: mutations\, selection\, and random e
nvironments\nby Natalia Komarova (University of California Irvine) as
part of Mathematical and Computational Biology Seminar Series\n\n\nAbstrac
t\nEvolutionary dynamics permeates life and life-like systems. Mathematica
l methods can be used to study evolutionary processes\, such as selection\
, mutation\, and drift\, and to make sense of many phenomena in life scien
ces. I will present two very general types of evolutionary patterns\, loss
-of-function and gain-of-function mutations\, and discuss scenarios of pop
ulation dynamics -- including stochastic tunneling and calculating the ra
te of evolution. I will also talk about evolution in random environments.
The presence of temporal or spatial randomness significantly affects the
competition dynamics in populations and gives rise to some counterintuitiv
e observations. Applications include origins of cancer\, passenger and dri
ver mutations\, and how aspirin might help prevent cancer.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Santiago Schnell (University of Michigan)
DTSTART:20200727T150000Z
DTEND:20200727T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/4
DESCRIPTION:Title: Developing models for the accurate measurement of enzyme kinet
ic parameters\nby Santiago Schnell (University of Michigan) as part of
Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nThe
conditions under which the Michaelis–Menten equation accurately captures
the steady-state kinetics of a simple enzyme-catalyzed reaction is contra
sted with the conditions under which the same equation is used to estimate
kinetic parameters in progress curve or initial rate experiments. A syste
matic analysis of kinetic models shows that satisfaction of the underlying
assumptions leading to the Michaelis–Menten equation are necessary\, bu
t not sufficient to guarantee accurate estimation of kinetic parameters. W
e present a detailed error analysis and numerical “experiments” to inv
estigate experimental designs for accurate estimation of kinetic parameter
s in progress curve and initial rate experiments. Our analysis suggests so
me of the leading causes for reported large variance in error estimates of
enzyme activity between different laboratories.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:BREAK - no talks
DTSTART:20200810T150000Z
DTEND:20200810T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/5
DESCRIPTION:by BREAK - no talks as part of Mathematical and Computational
Biology Seminar Series\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:James Glazier (Indiana University)
DTSTART:20200824T150000Z
DTEND:20200824T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/6
DESCRIPTION:Title: Multiscale multicellular modeling of tissue function and dise
ase using CompuCell3D: A simplified computer simulation of acute primary v
iral infection and immune response in an epithelial tissue\nby James G
lazier (Indiana University) as part of Mathematical and Computational Biol
ogy Seminar Series\n\n\nAbstract\nMultiscale multicellular models combine
representations of subcellular biological networks\, cell behaviors\, tiss
ue level effects and whole body effects to describe tissue outcomes during
development\, homeostasis and disease. I will briefly introduce these sim
ulation methodologies\, the CompuCell3D simulation environment and their a
pplications\, then focus on a multiscale simulation of an acute primary in
fection of an epithelial tissue infected by a virus like SARS-CoV-2\, a si
mplified cellular immune response and viral and immune-induced tissue dama
ge. The model exhibits four basic parameter regimes: where the immune res
ponse fails to contain or significantly slow the spread of viral infection
\, where it significantly slows but fails to stop the spread of infection\
, where it eliminates all infected epithelial cells\, but reinfection occu
rs from residual extracellular virus and where it clears the both infected
cells and extracellular virus\, leaving a substantial fraction of epithel
ial cells uninfected. Even this simplified model can illustrate the effect
s of a number of drug therapy concepts. Inhibition of viral internalizatio
n and faster immune-cell recruitment promote containment of infection. Fas
t viral internalization and slower immune response lead to uncontrolled sp
read of infection. Existing antivirals\, despite blocking viral replicatio
n\, show reduced clinical benefit when given later during the course of in
fection. Simulation of a drug which reduces the replication rate of viral
RNA\, shows that a low dosage that provides only a relatively limited redu
ction of viral RNA replication greatly decreases the total tissue damage a
nd extracellular virus when given near the beginning of infection. However
\, even a high dosage that greatly reduces the rate of RNA replication rap
idly loses efficacy when given later after infection. Many combinations of
dosage and treatment time lead to distinct stochastic outcomes\, with som
e replicas showing clearance or control of the virus (treatment success)\,
while others show rapid infection of all epithelial cells (treatment fail
ure). This switch between a regime of frequent treatment success and frequ
ent failure occurs is quite abrupt as the time of treatment increases. The
model is open-source and modular\, allowing rapid development and extensi
on of its components by groups working in parallel.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alain Goriely (University of Oxford)
DTSTART:20200921T150000Z
DTEND:20200921T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/7
DESCRIPTION:Title: Modelling dementia\nby Alain Goriely (University of Oxford
) as part of Mathematical and Computational Biology Seminar Series\n\n\nAb
stract\nNeurodegenerative diseases such as Alzheimer’s or Parkinson’s
are devastating conditions with poorly understood mechanisms and no known
cure. Yet a striking feature of these conditions is the characteristic pat
tern of invasion throughout the brain\, leading to well-codified disease s
tages visible to neuropathology and associated with various cognitive defi
cits and pathologies. How can we use mathematical modelling to gain insigh
t into this process and\, doing so\, gain understanding about how the brai
n works? In this talk\, I will show that by linking new methods of applied
mathematics to recent progress in imaging\, we can unravel some of the un
iversal features associated with dementia and\, more generally\, brain fun
ctions.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mohit Kumar Jolly (Indian Institute of Science)
DTSTART:20200907T150000Z
DTEND:20200907T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/8
DESCRIPTION:Title: Multi-scale modeling of the dynamics of cancer metastasis: a
computational systems biology approach\nby Mohit Kumar Jolly (Indian I
nstitute of Science) as part of Mathematical and Computational Biology Sem
inar Series\n\n\nAbstract\nMetastasis – the spread of cancer cells from
one organ to another – causes above 90% of all cancer-related deaths. D
espite extensive ongoing efforts in cancer genomics\, no unique genetic or
mutational signature has emerged for metastasis. However\, a hallmark tha
t has been observed in metastasis is adaptability or phenotypic plasticity
– the ability of a cell to reversibly switch among different phenotypes
(states) in response to various internal or external stimuli. This talk w
ill describe how the concepts of nonlinear dynamics can help (a) identify
how cancer cells can leverage this plasticity to drive cancer metastasis\,
(b) interpret existing clinical data\, (c) guide the next set of crucial
in vitro and in vivo experiments\, and (d) elucidate the role of non-mutat
ional mechanisms in cancer biology. Collectively\, my work highlights how
an iterative crosstalk between mathematical modeling and experiments can b
oth generate novel insights into the multi-scale dynamics of phenotypic pl
asticity and uncover previously unknown accelerators of metastasis.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Heiko Enderling (Moffitt Cancer Center)
DTSTART:20201019T150000Z
DTEND:20201019T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/9
DESCRIPTION:Title: Mathematical modeling of cancer radiotherapy\; the past\, the
present\, and the future\nby Heiko Enderling (Moffitt Cancer Center) a
s part of Mathematical and Computational Biology Seminar Series\n\n\nAbstr
act\nRadiotherapy is the single most applied cancer treatment in the world
. More than half of all cancer patients will receive radiation at some poi
nt during their clinical care. Most clinical protocols are informed by the
average results of large prospective clinical studies. Thus\, most patie
nts receive the same total dose delivered in the same daily fractionation
protocol. To date we have no reliable biomarkers to predict whether an ind
ividual patient will be controlled by radiation or not. As the field of ra
diation oncology is driven by medical physics\, mathematical modeling in r
adiotherapy has a long history. Here we discuss different novel mathematic
al modeling approaches to evaluate if tumor growth and treatment response
dynamics can be used to personalize and dynamically adapt radiation on a p
er patient basis. We will extend the modeling into studies of tumor-immune
interactions to identify the systemic consequences of local radiotherapy\
, and how to derive the optimal radiation dose to best harness radiation-i
nduced immune system activation.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mary Lou Zeeman (Bowdoin College)
DTSTART:20201005T150000Z
DTEND:20201005T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/10
DESCRIPTION:Title: A flow-kick framework for studying resilience\nby Mary Lo
u Zeeman (Bowdoin College) as part of Mathematical and Computational Biolo
gy Seminar Series\n\n\nAbstract\nAs climate change and human activities de
liver new disturbance patterns to urban and ecological systems\, resilienc
e questions make us look at familiar mathematics through a new lens. Resil
ience is a slippery concept that has different meanings in different conte
xts. It is often described as the ability of a system to absorb change and
disturbance while maintaining its basic structure and function. There is\
, therefore\, an inherent interplay between transient dynamics and perturb
ation in resilience questions\, especially when the perturbations are repe
ated. There is also an interplay between qualitative and quantitative data
. If we interpret the “structure” of a system as it’s dynamical beha
vior\, then its “function” is more value-laden as there are typically
“desirable” and “undesirable” regions of state space\, correspondi
ng to desirable or undesirable properties of the system. \n\nIn this talk\
, we subject the flow of an autonomous system of ODEs to regular shocks (
“kicks”) of constant size and direction\, representing repeated\, disc
rete disturbances. The resulting flow-kick systems occupy a surprisingly u
nder-explored area between deterministic and stochastic dynamics. We illus
trate some of the dynamical properties of flow-kick systems in the context
of resilience in ecological examples\, and describe some of the open math
ematical questions they raise.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thomas Hillen (University of Alberta)
DTSTART:20201102T160000Z
DTEND:20201102T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/11
DESCRIPTION:Title: Mathematical Modeling of the Immune-Mediated Theory of Metast
asis\nby Thomas Hillen (University of Alberta) as part of Mathematical
and Computational Biology Seminar Series\n\n\nAbstract\nAccumulating expe
rimental and clinical evidence suggests that the immune response to\ncance
r is not exclusively anti-tumor. In fact\, several pro-tumor effects of th
e immune system have been identified\, such as production of growth factor
s\, establishment of angiogenesis\, inhibition of immune response\, initia
tion of cell movement and metastasis\, and establishment of metastatic nic
hes. \n\nBased on experimental data\, we develop a mathematical model for
the immune-mediated theory of metastasis\, which includes anti- and pro-tu
mor effects of the immune system. The immune-mediated theory of metastasi
s can explain dormancy of metastasis and metastatic blow-up after resecti
on of the primary tumor. It can explain increased metastasis at sites of i
njury\, and the relatively poor performance of Immunotherapies\, due to pr
o-tumor effects of the immune system. \nOur results suggest that further w
ork is warranted to fully elucidate and control the pro-tumor effects of t
he immune system in metastatic cancer. (with Adam Rhodes)\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Veronica Ciocanel (Duke University)
DTSTART:20201116T160000Z
DTEND:20201116T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/12
DESCRIPTION:Title: Modeling and data analysis for intracellular protein organiza
tion\nby Veronica Ciocanel (Duke University) as part of Mathematical a
nd Computational Biology Seminar Series\n\n\nAbstract\nActin filaments are
protein polymers that interact with motor proteins inside cells and play
important roles in cell motility\, shape\, and development. Depending on i
ts function\, this dynamic network of interacting proteins reshapes and or
ganizes in a variety of structures\, including bundles\, clusters\, and co
ntractile rings.\nMotivated by observations from the reproductive system o
f the roundworm C. elegans\, we use an agent-based modeling framework to s
imulate interactions between actin filaments and myosin motor proteins ins
ide cells. We also develop tools based on topological data analysis to und
erstand time-series data extracted from these filamentous network interact
ions. Our analysis suggests potential mechanistic differences between moto
r proteins that are believed to shape the organization of structures such
as circular rings. In addition\, we show that changes in actin filament tr
eadmilling may significantly modulate the actin-myosin network organizatio
n during cell cycle progression.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Leonid Hanin (Idaho State University)
DTSTART:20201130T160000Z
DTEND:20201130T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/13
DESCRIPTION:Title: Mathematical discovery of natural laws in biomedical sciences
with application to metastasis\nby Leonid Hanin (Idaho State Universi
ty) as part of Mathematical and Computational Biology Seminar Series\n\n\n
Abstract\nMathematical modeling of systemic biomedical processes faces two
principal challenges: (1) enormous complexity of these processes and (2)
variability and heterogeneity of individual characteristics of biological
systems and organisms. As a result\, in the grand scheme of things\, mathe
matical models have so far played an auxiliary role in biomedical sciences
. We propose a new methodology of mathematical modeling that would allow m
athematics to give\, in certain cases\, definitive answers to important qu
estions that elude empirical resolution. The new methodology is based on t
wo ideas: (1) to employ mathematical models that are so general and flexib
le that they can account for many possible mechanisms\, both known and unk
nown\, of biomedical processes of interest\; (2) to find those model param
eters whose optimal values are independent of observations. These universa
l parameter values may reveal general regularities in biomedical processes
(that we call natural laws). Existence of such universal parameters presu
pposes that the model does not meet the conditions required for consistenc
y of the maximum likelihood estimator.\n\nWe illustrate this approach with
the discovery of a natural law governing cancer metastasis. Specifically\
, we found that under minimal mathematical and biomedical assumptions the
likelihood-maximizing scenario of metastatic cancer progression is always
the same: complete suppression of metastatic growth before primary tumor r
esection followed by an abrupt growth acceleration after surgery. This sce
nario is widely observed in clinical practice\, represents a common knowle
dge among veterinarians\, and is supported by a wealth of experimental stu
dies on animals and clinical observations accumulated over the last 115 ye
ars. Furthermore\, several biological mechanisms\, both hypothetical and e
xperimentally verified\, have been proposed that could explain this natura
l law. The above scenario does not preclude other possibilities that are a
lso observed in clinical practice. In particular\, metastases may surface
before surgery or may remain dormant thereafter.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jonathan Rubin (University of Pittsburgh)
DTSTART:20201214T160000Z
DTEND:20201214T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/14
DESCRIPTION:Title: Multiple roles of synaptic “inhibition” & how they arise
in decision-making pathways in the basal ganglia\nby Jonathan Rubin (U
niversity of Pittsburgh) as part of Mathematical and Computational Biology
Seminar Series\n\n\nAbstract\nThis talk concerns topics in mathematical n
euroscience but will not assume any specific knowledge of neuroscience. I
t should be of interest to anyone who would like to learn more about gener
al ideas of mathematical neuroscience or about certain specific topics: t
he role of the basal ganglia in decision-making and action selection\, cor
tico-striatal synaptic plasticity\, integration of multiple streams of inh
ibition in neural circuits\, and mechanisms of neural synchronization and
oscillations. \n \nThe phrase “inhibition” suggests a holding back or
suppression of activity. It has long been recognized that the roles of sy
naptic inhibition in neuronal circuits can be more diverse\, however\, and
include promotion of activity through effects such as post-inhibitory reb
ound and disynaptic disinhibition. The basal ganglia (BG) is a hub for th
e reward signal dopamine and is believed to be involved in decision-making
and action selection. Interestingly\, most synaptic pathways within the
BG involve neurotransmitters that are traditionally inhibitory. In the fi
rst section of my talk\, I will introduce this circuitry and present model
ing of how these pathways can collaborate to produce reward-driven action.
I will also present joint work with Tim Verstynen\, Cati Vich and our tr
ainees\, which (1) introduces a way to map between biologically detailed m
odels and more abstract decision-making models and (2) suggests how differ
ent BG inhibitory neurons serve different roles in terms of evidence accum
ulation and decision thresholds. In the second section of my talk\, I wil
l present work with postdoc Ryan Phillips and our collaborator Aryn Gittis
in which we model the integration of two inhibitory pathways by BG output
neurons. Our modeling takes into account chloride dynamics and its impac
t on synaptic reversal potentials and shows how these pathways can actuall
y induce excitatory effects\, can contribute to synchronization and oscill
ations\, and can affect action selection\, in ways that may be related to
Parkinson’s disease.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Leah Edelstein-Keshet (University of British Columbia)
DTSTART:20210322T150000Z
DTEND:20210322T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/15
DESCRIPTION:Title: Mathematical and computational models: from sub cellular to m
ulticellular behaviour\nby Leah Edelstein-Keshet (University of Britis
h Columbia) as part of Mathematical and Computational Biology Seminar Seri
es\n\n\nAbstract\nDepending on their internal structure (the cytoskeleton)
animal cells can take on many shapes: compact\, flat\, long\, polarized\,
or ramified. Some cell types adhere tightly to one another\, forming shee
t-like tissue (epithelia)\, while other types\, such as white blood cells
(neutrophils)\, migrate\, seeking pathogens to destroy. In this talk\, I w
ill describe how we use mathematical and computational models to address a
number of biological questions about cell shape and motility\, including
the following: What mechanisms account for directed migration of neutrophi
ls? How does the cell environment (extracellular matrix\, ECM) affect cell
migration? How can we understand more complex cell migration patterns\, i
ncluding oscillations and internal waves of activity? How do we bridge fro
m an understanding of single cells to that of multicellular collective mig
ration? I will argue that we can use computational modeling as a tool in b
iological discovery\, both to test hypotheses\, to probe systems that are
not easily measured experimentally\, and to gain insights that would other
wise be obscure.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Philip Maini (University of Oxford)
DTSTART:20210125T160000Z
DTEND:20210125T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/16
DESCRIPTION:Title: Modelling collective cell movement in biology and medicine\nby Philip Maini (University of Oxford) as part of Mathematical and Comp
utational Biology Seminar Series\n\n\nAbstract\nCollective cell movement o
ccurs throughout biology and medicine and there\nare many common features
shared across different areas. I will review\nwork we have carried out ove
r the past few years on\n(i) systematically deriving a PDE model for tumou
r angiogenesis from a discrete\nformulation and comparing this model with
the classical\, phenomenological snail-trail\nmodel\;\n(ii) agent-based mo
dels for cranial neural crest cell migration in a collaboration with\nexpe
rimental biologists that has revealed a number of new biological insights.
\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mark Chaplain (University of St Andrews)
DTSTART:20210222T160000Z
DTEND:20210222T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/17
DESCRIPTION:Title: A Mathematical Framework for Modelling the Metastatic Spread
of Cancer\nby Mark Chaplain (University of St Andrews) as part of Math
ematical and Computational Biology Seminar Series\n\n\nAbstract\nInvasion
and metastasis are two of the hallmarks of cancer and are intimately conne
cted processes. Invasion\, as the name suggests\, involves cancer cells sp
reading out from the main cancerous mass into the surrounding tissue\, thr
ough production and secretion of matrix degrading enzymes. Metastatic spre
ad is the process whereby invasive cancer cells enter nearby blood vessels
(or lymph vessels)\, are carried around the body in the main circulatory
system and then succeed in escaping from the circulatory system at distant
secondary sites where the growth of the cancer starts again. It is this
metastatic spread that is responsible for around 90% of deaths from cance
r. To shed light on the metastatic process\, we present a mathematical mod
elling framework that captures for the first time the interconnected proce
sses of invasion and metastatic spread of individual cancer cells in a spa
tially explicit manner—a multigrid\, hybrid\, individual-based approach.
This framework accounts for the spatiotemporal evolution of mesenchymal-
and epithelial-like cancer cells\, membrane-type-1 matrix metalloproteinas
e (MT1-MMP) and the diffusible matrix metalloproteinase-2 (MMP-2)\, and fo
r their interactions with the extracellular matrix. Using computational si
mulations\, we demonstrate that our model captures all the key steps of th
e invasion-metastasis cascade\, i.e. invasion by both heterogeneous cancer
cell clusters and by single mesenchymal-like cancer cells\; intravasation
of these clusters and single cells both via active mechanisms mediated by
matrix-degrading enzymes (MDEs) and via passive shedding\; circulation of
cancer cell clusters and single cancer cells in the vasculature with the
associated risk of cell death and disaggregation of clusters\; extravasati
on of clusters and single cells\; and metastatic growth at distant seconda
ry sites in the body.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Olivia Prosper (University of Tennessee\, Knoxville)
DTSTART:20210208T160000Z
DTEND:20210208T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/18
DESCRIPTION:Title: Modeling within-mosquito dynamics of the malaria parasite
\nby Olivia Prosper (University of Tennessee\, Knoxville) as part of Mathe
matical and Computational Biology Seminar Series\n\n\nAbstract\nThe malari
a parasite Plasmodium falciparum requires a vertebrate host and a female A
nopheles mosquito to complete a full life cycle\, with sexual reproduction
occurring in the mosquito. While parasite dynamics within the vertebrate
host\, such as humans\, has been extensively studied\, less is understood
about dynamics within the mosquito\, a critical component of malaria trans
mission dynamics. This sexual stage of the parasite life cycle allows for
the production of genetically novel parasites. In the meantime\, a mosquit
o’s biology creates bottlenecks in the infecting parasites’ developmen
t. We developed a two-stage stochastic model of the generation of parasite
diversity within a mosquito and were able to demonstrate the importance o
f heterogeneity amongst parasite dynamics across a population of mosquitoe
s on estimates of parasite diversity. A key epidemiological parameter rela
ted to the timing of onward transmission from mosquito to vertebrate host
is the extrinsic incubation period (EIP). Using simple models of within-mo
squito parasite dynamics fitted to empirical data\, we investigated factor
s influencing the EIP.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Miranda Teboh-Ewungkem (Lehigh University)
DTSTART:20210308T160000Z
DTEND:20210308T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/19
DESCRIPTION:Title: Malaria and Mathematics as Viewed from the Lens of the Transm
itting Mosquitoes\nby Miranda Teboh-Ewungkem (Lehigh University) as pa
rt of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\
nMalaria is a disease caused by Plasmodium parasites and transmitted from
human to human via a bite from an infectious blood feeding female Anophele
s sp mosquito. Successful transmission of the parasite to humans requires
that a susceptible female mosquito feeds on two distinct humans – one in
fected with the parasite and the other susceptible\, at two distinct seque
ntial time points. In addition\, the parasite must be in its transmissible
form in the mosquito at the latter feeding. The bottlenecks involved in t
he process illuminates how the parasite\, driven by the need to survive\,
has captured the evolutionary and reproductive needs of the mosquito to en
sure the parasite’s survivability. Thus\, understanding the disease thro
ugh the lens of the transmitting mosquitoes\, driven by the evolutionary n
eed to survive\, has shown that interesting dynamics can be observed even
under simple mass action assumptions. Moreover\, it allows for the incorpo
ration of mosquito gonotrophic cycles and how these cycles contribute to m
osquito abundance that can directly and indirectly affect malaria transmis
sibility and intensity. It also illuminates how a mosquito’s age is link
ed to disease transmissibility success when the parasite dynamics is incor
porated into an interactive model that captures the interaction of mosquit
oes\, humans and the malaria causing parasite. A by-product of explicitly
incorporating the mosquitoes’ gonotrophic cycles is the implicit embeddi
ng of the incubation period of the disease within the mosquito population
in the modelling framework. In this talk\, I will present a series of resu
lts that have been obtained when malaria disease transmissibility is studi
ed via the lens of the transmitting mosquito.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Víctor M. Pérez García (Universidad de Castilla-La Mancha)
DTSTART:20210405T150000Z
DTEND:20210405T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/20
DESCRIPTION:Title: Scaling laws and evolutionary dynamics in cancer: Recent resu
lts and open mathematical problems.\nby Víctor M. Pérez García (Uni
versidad de Castilla-La Mancha) as part of Mathematical and Computational
Biology Seminar Series\n\n\nAbstract\nMost physical and other natural syst
ems are complex entities that are composed of a large number of interactin
g individual elements. It is a surprising fact that they often obey the so
-called scaling laws that relate an observable quantity to a measure of th
e size of the system [1]. In this talk I will describe the discovery of un
iversal scaling laws in human cancers [2] and how that implies the increas
e of tumor aggressiveness that leads to an explosive growth as the disease
progresses. The observations can be understood using different types of b
iologically inspired mathematical models. The most complex ones are discre
te and recapitulate the variety of clonal populations emerging within neop
lasms and their interactions [3]. However\, most of the observed phenomena
can be described using different types of nonlocal partial differential e
quations. The mathematical approaches lead to the definition of different
biomarkers of the disease aggressiveness that have been validated using ca
ncers imaging data [1\,3].\n\nI will also discuss several open mathematica
l problems of relevance arising in the context of this research.\n[1] West
G\, Scale: The Universal Laws of Life and Death in Organisms\, Cities and
Companies. Penguin (2018).\n\n[2] V. M. Pérez-García et al\, Universal
scaling laws rule explosive growth in human cancers\, Nature Physics 16\,
1232-1237 (2020).\n\n[3] J. Jiménez-Sánchez\, A. Martínez-Rubio\, A. Po
pov\, J. Pérez-Beteta\, Y. Azimzade\, D. Molina-García\, J. Belmonte-Bei
tia\, G. F. Calvo\, V. M. Pérez-García. A mesoscopic simulator to uncove
r heterogeneity and evolutionary dynamics in tumors. PLOS Computational Bi
ology (2021).\n\n[4] J. Jiménez-Sánchez\, J. J. Bosque\, G. A. Jiménez-
Londoño\, D. Molina-García\, A. Martínez-Rubio\, J. Pérez-Beteta\, C.
Ortega-Sabater\, A. F. Honguero-Martínez\, A. M. García-Vicente\, G. F.
Calvo\, V. M. Pérez-García. Evolutionary dynamics at the tumor edge reve
als metabolic imaging biomarkers. Proceedings of the National Academy of S
ciences 118(6) e2018110118 (2021).\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mark Lewis (University of Alberta)
DTSTART:20210419T150000Z
DTEND:20210419T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/21
DESCRIPTION:Title: Population Dynamics in Changing Environments\nby Mark Lew
is (University of Alberta) as part of Mathematical and Computational Biolo
gy Seminar Series\n\n\nAbstract\nClassical population dynamics problems as
sume constant unchanging environments. However\, realistic environments fl
uctuate in both space and time. My lecture will focus on the analysis of p
opulation dynamics in environments that shift spatially\, due either to ad
vective flow (eg.\, river population dynamics) or to changing environmenta
l conditions (eg.\, climate change). The emphasis will be on the analysis
of nonlinear advection-diffusion-reaction equations and related models in
the case where there is strong advection and environments are heterogeneou
s. I will use methods of spreading speed analysis and "inside dynamics" to
understand qualitative outcomes. Applications will be made to river popul
ations and to the genetic structure of populations subject to climate chan
ge.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Germán Enciso (University of California Irvine)
DTSTART:20210503T150000Z
DTEND:20210503T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/22
DESCRIPTION:Title: Stochastic Modeling of Nucleosome Dynamics and Gene Expressio
n\nby Germán Enciso (University of California Irvine) as part of Math
ematical and Computational Biology Seminar Series\n\n\nAbstract\nDNA is ti
ghtly packaged around histone proteins in order to increase its density in
side cells\, and a potential mechanism for DNA expression regulation is to
control DNA-histone interactions. In this talk I will present recent mod
els of this behavior\, including a novel ultrasensitive\, noncooperative m
echanism for DNA packaging\, as well as a collaboration to study time-depe
ndent NFkB inputs in inflammatory signaling. Both models combine basic an
alysis ideas with computational analysis to better understand the qualitat
ive principles for gene regulation.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Tanveer Syeda-Mahmood (IBM Fellow\, IBM Research)
DTSTART:20210517T150000Z
DTEND:20210517T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/23
DESCRIPTION:Title: Multimodal Fusion Across Scales for Disease Understanding
\nby Tanveer Syeda-Mahmood (IBM Fellow\, IBM Research) as part of Mathemat
ical and Computational Biology Seminar Series\n\n\nAbstract\nIn a complex
disease such as cancer\, the interactions between the tumor and host can e
xist at the molecular\, cellular\, tissue\, and organism levels. Thus evid
ence for the disease and its evolution may be present in multiple modaliti
es across scale such as clinical\, genomic\, molecular\, pathological and
radiological imaging. Effective patient-tailored therapeutic guidance and
planning in the future will require bridging spatiotemporal scales through
novel multimodal fusion formalisms. In this talk\, I will present some of
the latest published work from our team in developing new deep learning a
lgorithms for multimodal fusion. Specifically\, I will describe our work o
n fusing data from multiple information sources towards addressing many pr
oblems in cancer and cardiovascular disease understanding.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Wenrui Hao (Pennsylvania State University)
DTSTART:20210531T150000Z
DTEND:20210531T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/24
DESCRIPTION:Title: Computational models of cardiovascular disease\nby Wenrui
Hao (Pennsylvania State University) as part of Mathematical and Computati
onal Biology Seminar Series\n\n\nAbstract\nIn this talk\, I will introduce
several computational models of cardiovascular disease\, including athero
sclerosis and aortic aneurysm growth to quantitatively predict long-term c
ardiovascular risk. These models integrate both the multi-layered structur
e of the arterial wall and the aneurysm pathophysiology. The heterogeneou
s multiscale method is employed to tackle different time scales while the
finite element method is adopted to deformation the hyperelastic arterial
wall. A three-dimensional realistic cardiovascular FSI problem with an aor
tic aneurysm growth based upon the patients' CT scan data is simulated to
validate a medically reasonable long-term prediction.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Charles S. Peskin (Courant Institute of Mathematical Sciences New
York University)
DTSTART:20210920T150000Z
DTEND:20210920T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/25
DESCRIPTION:Title: Inference of crossbridge properties from A.V. Hill's descript
ion of the heat of shortening and force-velocity relation of skeletal musc
le\nby Charles S. Peskin (Courant Institute of Mathematical Sciences N
ew York University) as part of Mathematical and Computational Biology Semi
nar Series\n\n\nAbstract\nWe set up and solve an inverse problem\, in whic
h microscopic properties of myosin motors in skeletal muscle are derived f
rom the macroscopic mechanical and thermal properties of muscle that were\
ndiscoverd by A.V. Hill in 1938. The solution is made unique by imposing
a finite range condition on crossbridge deformation. Results are in surpri
singly good agreement with 21st-century data.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ami Radunskaya (Pomona College)
DTSTART:20211115T160000Z
DTEND:20211115T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/27
DESCRIPTION:Title: DCs\, Doses and Drugs: mathematical models for tumor treatmen
ts over the past 20 years.\nby Ami Radunskaya (Pomona College) as part
of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nI
n this talk I will trace a trajectory of mathematical models used to infor
m cancer treatments. The mathematical tools used include systems of diffe
rential equations\, heuristic optimization\, hybrid cellular automata an
d network complexity. This story highlights the power of flexibility and
collaboration\, and illustrates how current mysteries and available data
can drive the modeling process.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Paul Macklin (Indiana University)
DTSTART:20211101T150000Z
DTEND:20211101T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/28
DESCRIPTION:Title: Using agent-based models to explore complex multicellular sys
tems\nby Paul Macklin (Indiana University) as part of Mathematical and
Computational Biology Seminar Series\n\n\nAbstract\nMulticellular biologi
cal systems are driven by the nonlinear interactions of cells in their dyn
amical microenvironments. Agent-based models explore these systems by simu
lating each cell as a discrete agent with an independent state and behavio
ral rules\, while coupling with partial differential equation models of th
e chemical microenvironment. Individual agents may also incorporate reacti
on kinetics networks\, dynamic flux models\, or Boolean networks to model
intracellular processes that drive cell behaviors. After introducing cell-
based modeling\, we will introduce PhysiCell: an open source\, cross-platf
orm agent-based modeling systems for multicellular systems biology. We wil
l demonstrate applications in cancer biology\, immunotherapy\, and infecti
ous diseases including COVID-19. We will close with a brief look at how me
thods developed for our COVID-19 project are now driving new work in cance
r immunology and cancer patient digital twins. This talk will also present
how agent-based modeling\, high performance computing\, and machine learn
ing can be combined to enhance discovery.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chris Sander (Harvard Medical School)
DTSTART:20211018T150000Z
DTEND:20211018T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/29
DESCRIPTION:Title: Machine learning for hard biological problems - three example
s\nby Chris Sander (Harvard Medical School) as part of Mathematical an
d Computational Biology Seminar Series\n\n\nAbstract\nExamples are: \n- co
mputational models of cell biological processes from systematic perturbati
on-response experiments\n- identifying high risk of pancreatic cancer from
real-world clinical records\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chun Liu (Illinois Institute of Technology)
DTSTART:20211004T150000Z
DTEND:20211004T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/30
DESCRIPTION:Title: Energetic Variational Approaches (EnVarA) for Active Material
s and Reactive Fluids\nby Chun Liu (Illinois Institute of Technology)
as part of Mathematical and Computational Biology Seminar Series\n\n\nAbst
ract\nActive/reactive fluids convert and transduce energy from their surro
unding into a motion and other mechanical activities. These systems are us
ually out of mechanical or even thermodynamic equilibrium. One can find s
uch examples in almost all biological systems. In this talk I will develop
a general theory for active fluids which convert chemical energy into var
ious types of mechanical energy. This is the extension of the classical en
ergetic variational approaches for mechanical systems. The methods will co
ver a wide range of both chemical reaction kenetics and mechanical process
es. This is a joint work with Yiwei Wang.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/30/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ruth Baker (University of Oxford)
DTSTART:20220131T160000Z
DTEND:20220131T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/31
DESCRIPTION:Title: Mathematical and computational challenges in interdisciplinar
y bioscience: efficient approaches for simulating and calibrating stochast
ic models of biological processes.\nby Ruth Baker (University of Oxfor
d) as part of Mathematical and Computational Biology Seminar Series\n\n\nA
bstract\nSimple mathematical models have had remarkable successes in biolo
gy\, framing how we understand a host of mechanisms and processes. However
\, with the advent of a host of new experimental technologies\, the last t
en years has seen an explosion in the amount and types of data now being g
enerated. Increasingly larger and more complicated processes are now being
explored\, including large signalling or gene regulatory networks\, and t
he development\, dynamics and disease of entire cells and tissues. As such
\, the mechanistic\, mathematical models developed to interrogate these pr
ocesses are also necessarily growing in size and complexity. These detaile
d models have the potential to provide vital insights where data alone can
not\, but to achieve this goal requires meeting significant mathematical c
hallenges in efficiently simulating models and calibrating them to experim
ental data. In this talk\, I will outline some of these challenges\, and r
ecent steps we have taken in addressing them.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Raymond Goldstein (University of Cambridge)
DTSTART:20220214T160000Z
DTEND:20220214T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/32
DESCRIPTION:Title: Cytoplasmic Streaming and the Swirling Instability of the Mic
rotubule Cytoskeleton\nby Raymond Goldstein (University of Cambridge)
as part of Mathematical and Computational Biology Seminar Series\n\n\nAbst
ract\nCytoplasmic streaming is the persistent circulation of the fluid con
tents of large eukaryotic cells\, driven by the action of molecular motors
moving along cytoskeletal filaments\, entraining fluid. Discovered in 177
4 by Bonaventura Corti\, it is now recognized as a common phenomenon in a
very broad range of model organisms\, from plants to flies and worms. Thi
s talk will discuss physical approaches to understanding this phenomenon t
hrough a combination of experiments (on aquatic \nplants\, Drosophila\, an
d other active matter systems)\, theory\, and computation. A particular f
ocus will be on streaming in the Drosophila oocyte\, for which I will desc
ribe a recently discovered “swirling instability” of the microtubule c
ytoskeleton.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Belinda Akpa (Department of Energy)
DTSTART:20211129T160000Z
DTEND:20211129T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/33
DESCRIPTION:Title: Bridging the gaps: Multiscale modeling in 'tiny data' biology
\nby Belinda Akpa (Department of Energy) as part of Mathematical and C
omputational Biology Seminar Series\n\n\nAbstract\nAt a time when many are
wrangling with biological 'big data'\, there remain important problems th
at are fundamentally data limited – often physiological questions for wh
ich there is little quantitative data\, and further data collection may be
hampered by limited resources\, ethical constraints\, or simply a lack of
clarity as to which measurements are most likely to shed light on mechani
sms of interest. Mathematical modeling can make impactful contributions in
these contexts by maximizing the value of the existing biological literat
ure and operationalizing data from disparate studies to build quantitative
models. In this presentation\, I will describe how multiscale mathematica
l models can be built using 'tiny data'.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael Reed (Duke University)
DTSTART:20211213T160000Z
DTEND:20211213T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/34
DESCRIPTION:Title: Serotonin\, Histamine\, and Depression\nby Michael Reed (
Duke University) as part of Mathematical and Computational Biology Seminar
Series\n\n\nAbstract\nA long-term collaboration between Parry Hashemi\, a
n electrochemist (Imperial College)\, H. Fredrik Nijhout\, a biologist at
Duke\, Janet Best\, a mathematician at Ohio State and\nthe speaker will be
described. Hashemi can measure the time courses of serotonin and histamin
e (in vivo in mouse) in the extracellular space in the brain after stimula
tion of serotonin and histamine neurons. The modelers have helped Hashemi
interpret her data and the data has shown where the models are right or wr
ong. New results on autoreceptors and serotonin reuptake transporters will
be described. Recent work on the interaction between histamine and seroto
nin have led to a new hypothesis on the causative mechanisms of depression
and has explained why select serotonin reuptake inhibitors have proven to
be notoriously unreliable therapeutic agents for Depression.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anita Layton (University of Toronto)
DTSTART:20220328T150000Z
DTEND:20220328T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/36
DESCRIPTION:Title: His or Her Mathematical Models --- Modeling Kidney Physiology
and Beyond\nby Anita Layton (University of Toronto) as part of Mathem
atical and Computational Biology Seminar Series\n\n\nAbstract\nImagine som
eone having a heart attack. Do you visualize the dramatic Hollywood portra
yal of a heart attack\, in which a man collapses\, grabbing his chest in a
gony? Even though heart disease is the leading killer of women worldwide\,
the misconception that heart disease is a men’s disease has persisted.
A dangerous misconceptions and risks women ignoring their own symptoms. Ge
nder biases and false impressions are by no means limited to heart attack
symptoms. Such prejudices exist throughout our healthcare system\, from sc
ientific research to disease diagnosis and treatment strategies. A goal of
our research program is to address this gender equity\, by identifying an
d disseminating insights into sex differences in health and disease\, usin
g computational modeling tools.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adriana Dawes (The Ohio State University)
DTSTART:20220314T150000Z
DTEND:20220314T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/37
DESCRIPTION:Title: Experimental and mathematical approaches to investigate dynei
n localization and pronuclear movement in the early C. elegans embryo\
nby Adriana Dawes (The Ohio State University) as part of Mathematical and
Computational Biology Seminar Series\n\n\nAbstract\nAsymmetric cell divisi
on\, where daughter cells inherit unequal amounts of specific factors\, is
critical for development and cell fate specification. In polarized cells\
, where specific factors are segregated to opposite ends of the cell\, asy
mmetric cell division occurs as a result of dynein-mediated centrosome pos
itioning along the polarity axis. Early embryos of the nematode worm C. el
egans polarize in response to fertilization and rely on proper centrosome
positioning for cell fate specification and development. Depletion of cert
ain proteins results in defective movement of centrosomes and the associat
ed pronuclear complex. We developed a novel measure to characterize and q
uantify the oscillatory nature of these movement defects\, revealing a com
mon movement defect induced by the loss of seemingly unrelated proteins. W
e further demonstrated in vivo that dynein localization is not impaired in
the presence of this oscillatory movement\, suggesting that the proteins
identified by our measure play a role in regulating dynein activity. Curre
nt work integrates mathematical modeling with quantitative imaging of the
centrosome and pronuclear complex movement to identify the signaling netwo
rks and physical mechanisms responsible for the impaired movement.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sunčica Čanić (University of California\, Berkeley)
DTSTART:20220411T150000Z
DTEND:20220411T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/38
DESCRIPTION:Title: Mathematical and computational modeling of a bioartificial pa
ncreas\nby Sunčica Čanić (University of California\, Berkeley) as p
art of Mathematical and Computational Biology Seminar Series\n\n\nAbstract
\nThe work reported here has been motivated by the design of lab-grown org
ans\, such as a bioartificial pancreas. The design of lab-grown organs rel
ies on using biocompatible materials\, typically poroelastic hydrogels\, t
o generate scaffolds to support seeded cells of different organs. Additio
nally\, to prevent the patient's own immune cells from attacking the trans
planted organ\, the hydrogel containing seeded cells is encapsulated betwe
en two semi-permeable\, nano-pore size membranes/plates and connected to t
he patient's vascular system via a tube (anastomosis graft). The semi-perm
eable membranes are designed to prevent the patient's own immune cells fro
m attacking the transplant\, while permitting oxygen and nutrients carryin
g blood plasma (Newtonian fluid) to reach the cells for long-term cell via
bility. A key challenge is to design a hydrogel with ``roadways'' for blo
od plasma to carry oxygen and nutrients to the transplanted cells. \nWe pr
esent a complex\, multi-scale model\, and a first well-posedness result in
the area of fluid-poroelastic structure interaction (FPSI) with multi-lay
ered structures modeling organ encapsulation. We show global existence of
a weak solution to a FPSI problem between the flow of an incompressible\,
viscous fluid\, modeled by the time-dependent Stokes equations\, and a mul
ti-layered poroelastic medium consisting of a thin poroelastic plate and a
thick poroelastic medium modeled by a Biot model. Numerical simulations o
f the underlying problem showing optimal design of a bioartificial pancrea
s\, will be presented. This is a joint work with bioengineer Shuvo Roy (UC
SF)\, and mathematicians Yifan Wang (UCI)\, Lorena Bociu (NCSU)\, Boris Mu
ha (University of Zagreb)\, and Justin Webster (University of Maryland\, B
altimore County).\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Omar Saucedo (Virginia Tech University)
DTSTART:20220228T160000Z
DTEND:20220228T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/39
DESCRIPTION:Title: Host movement\, transmission hot spots\, and vector-borne dis
ease dynamics on spatial networks\nby Omar Saucedo (Virginia Tech Univ
ersity) as part of Mathematical and Computational Biology Seminar Series\n
\n\nAbstract\nHuman movement plays a key part on how a disease can propaga
te through a population as it enables a pathogen to invade a new environme
nt and helps the persistence of a disease in locations that would otherwis
e be isolated. In this talk\, we explore how spatial heterogeneity combine
s with mobility network structure to influence vector-borne disease dynami
cs. We derive an approximation for the domain reproduction number for a n
-patch SIS-SI Ross-Macdonald model using a Laurent series expansion. Furth
ermore\, we analyze the sensitivity equations with respect to the domain r
eproduction number to determine which parameters should be targeted for in
tervention strategies. To observe how these analytical results can be imp
lemented in practice\, we conclude with a case study.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Helen Moore (University of Florida)
DTSTART:20220425T150000Z
DTEND:20220425T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/40
DESCRIPTION:Title: Systems Pharmacology Models in Drug Development\nby Helen
Moore (University of Florida) as part of Mathematical and Computational B
iology Seminar Series\n\n\nAbstract\nA wide variety of mathematical method
s are used to aid the drug development process. One example is the use of
quantitative systems pharmacology (QSP) models. A QSP model is a mathemati
cal\, mechanistic representation of a patient’s disease and therapy dyna
mics. QSP models are typically systems of ordinary differential equations
with a dozen or more nonlinear equations\, and many more parameters. Altho
ugh QSP models have been used to save substantial time and money in drug d
evelopment\, their use is not as widespread as might be expected from thes
e benefits. Lack of buy-in from stakeholders is a major hurdle to adoption
and can\, in part\, be attributed to lack of confidence in QSP models and
their predictions. In this talk\, I will make the case that standardizati
on of systems model evaluation methods\, either within the biotechnology/p
harmaceutical (biopharma) community or more broadly\, would support more e
xtensive use of QSP models\, and would reduce the resources needed for dru
g development. Proposed model evaluation methods include sensitivity and i
dentifiability analysis\, uncertainty quantification\, comparison to data\
, and external review. I will share examples of evaluation methods that ar
e being applied to QSP models. I will also discuss how model credibility c
an support the use of optimal control and mathematical optimization of com
bination drug regimens. \n\nBraakman S\, Pathmanathan P\, Moore H. Evaluat
ion framework for systems models. CPT Pharmacometrics Syst Pharmacol. 2022
\; 11: 264- 289. https://doi.org/10.1002/psp4.12755\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joel Brown
DTSTART:20220926T150000Z
DTEND:20220926T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/41
DESCRIPTION:Title: Using evolutionary game theory to treat cancer\nby Joel B
rown as part of Mathematical and Computational Biology Seminar Series\n\n\
nAbstract\n“You have cancer.” What unfortunate words. To the patient
\, family and friends cancer brings a maelstrom of emotions including fear
and hope. It can be a horrific disease of genetic mutations and unregula
ted proliferation. But\, cancer is much more\, and knowing this can empow
er the patient and suggest new therapies. Cancer cells inhabit a tumor eco
system where they experience much the same hazards and opportunities prese
nt in the ecology of any creature. Furthermore\, like nature\, they evolv
e adaptations to better acquire resources\, avoid the hazards of the immun
e system\, and occupy new spaces and organs of the patient. The failure o
f therapy happens when cancer cells evolve resistance. Evolutionary game t
heory is eminently suited for modelling cancer’s eco-evolutionary dynami
cs. As a game\, cancer cells are the players\, their genetically and epig
enetically heritable traits are their strategies\, proliferation and survi
val are their payoffs\, and the tumor microenvironment sets the rules. Wi
th therapy\, the physician becomes an additional player in this game. Und
erstanding the game that goes on between treatment strategies and the canc
er cells offers new insights and hope. Such therapies aim to use drugs mo
re sparingly and judiciously. We can and should anticipate and steer the
cancer cells’ evolution. In this way\, otherwise incurable cancers may
be managed as a livable\, chronic disease\, or better yet cured by beating
cancer at its own ecological and evolutionary “chess” game. Here I w
ill: 1) model cancer as an evolutionary game\, 2) model cancer therapy as
a leader-follower game\, and 3) present a game theory model and clinical t
rial of adaptive therapy for men with incurable metastatic prostate cancer
.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Vasileios Maroulas (University of Tennessee)
DTSTART:20221128T160000Z
DTEND:20221128T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/42
DESCRIPTION:Title: Grid Cell Navigation via Simplicial Convolutional Recurrent N
eural Network\nby Vasileios Maroulas (University of Tennessee) as part
of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nI
mproving flexibility and adaptability of next generation AI is possible by
designing networks that generate abstract spatial representations in the
same way that mammals do. Complex spatial representation patterns\, as re
corded by neuroscience data\, may be uncovered through the discovery of th
eir underlying manifolds. Such manifolds may be represented by a rich in i
nformation simplicial complex. Simplicial complexes form an important clas
s of topological spaces that are frequently employed in various applicatio
n areas from materials science and chemistry to biology and neuroscience\,
etc. for addressing supervised and unsupervised learning. In this talk\,
we will discuss our recent simplicial convolutional recurrent neural netwo
rk (SCRNN) and its application to automated navigation using grid cell dat
a.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/42/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jude Kong (York University)
DTSTART:20221031T150000Z
DTEND:20221031T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/43
DESCRIPTION:Title: Leveraging AI for Clinical Public Health in the Global South<
/a>\nby Jude Kong (York University) as part of Mathematical and Computatio
nal Biology Seminar Series\n\n\nAbstract\nDisease outbreaks are increasing
both in terms of severity and frequency. Climate change is exacerbating e
xisting health and social inequities by increasing the vulnerability of cl
imate “hotspots” to the emergence and re-emergence of many infectious
diseases such as malaria\, dengue fever and zika. Moreover\, a growing num
ber of these diseases are spread from animals to people\, due to factors s
uch as growing human encroachment into natural landscapes. Responding to t
he complex nature of these interactions in a timely way requires the abili
ty to analyze large data sets across multiple sectors. Artificial intellig
ence solutions and data science approaches are increasingly being used acr
oss the globe to identify risks\, conduct predictive modeling and provide
evidence-based recommendations for public health policy and action. My res
earch program represents a small step in this direction. In this talk\, I
will provide a comprehensive overview of the potential roles and applicati
ons of AI in clinical public health in Africa. As a case study\, I will f
ocus on the work that we have been doing in Africa.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/43/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Amber Smith (University of Tennessee Health Science Center)
DTSTART:20230227T160000Z
DTEND:20230227T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/44
DESCRIPTION:Title: Modeling the Complexity of Viral and Bacterial Coinfections w
ith Influenza\nby Amber Smith (University of Tennessee Health Science
Center) as part of Mathematical and Computational Biology Seminar Series\n
\n\nAbstract\nSecondary viral and bacterial pathogens exacerbate influenza
to cause significant morbidity and mortality. However\, the outcome is de
pendent on the order and timing of each pathogen\, where protection from i
nfluenza is observed in some scenarios. While experimental methods can be
used to identify mechanisms of multi-pathogen infections\, mathematical mo
dels provide a unique lens to determine their contribution to susceptibili
ty and pathogenicity and define hidden mechanisms. I’ll discuss an integ
rative model-experiment exchange that we used to disentangle pathogen-spec
ific effects on host immunity\, dissemination within the lung\, and diseas
e severity during bacterial or viral coinfections during influenza.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/44/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bard Ermentrout (Pittsburgh University)
DTSTART:20230410T150000Z
DTEND:20230410T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/45
DESCRIPTION:Title: Follow your Nose: The Dynamics of Olfactory Guided Search
\nby Bard Ermentrout (Pittsburgh University) as part of Mathematical and C
omputational Biology Seminar Series\n\n\nAbstract\nOlfaction (the sense of
smell) is the oldest of our sensory modalities and has been used for mill
ions of years for animals to find mates\, find food\, avoid predators\, et
c. In a large multi-investigator collaboration\, we have begun to try to u
nderstand the algorithms animals use to navigate complex odor landscapes.
I will describe several simple algorithms that use local spatial and tempo
ral information about the odor to locate its source. The algorithms fall i
nto two simple categories: differences between two sensors and differences
between two different samples. With data from trail-following and spot f
inding by mice\, I attempt to assess the different strategies and how para
meters in the strategies affect performance. I also test the algorithms o
n odor plumes imaged by my collaborators and also in a mobile robot. \n\nU
nderlying these simple algorithms are some interesting nonlinear dynamics.
I will discuss the continuous dynamics of binaral search where the organi
sm uses the concentration differences between two sensors to steer toward
the source. Depending on the odor environment\, various types of complex d
ynamics emerge including stable fixed points\, periodic orbits\, torii\, a
nd chaos.\n\nI will show the role of “noise” on improving the algorith
ms and how it can be leveraged as a search strategy by exploring a first p
assage time problem applied to spot finding. I will also show a kind of st
ochastic resonance can occur in real odor plumes.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/45/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Liu (Dana Farber Cancer Institute)
DTSTART:20230501T150000Z
DTEND:20230501T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/46
DESCRIPTION:Title: Computational approaches to enable precision medicine in canc
er patients\nby David Liu (Dana Farber Cancer Institute) as part of Ma
thematical and Computational Biology Seminar Series\n\n\nAbstract\nThere i
s a burgeoning amount of high-dimensional molecular data generated from pa
tient clinical samples\, including genomics\, transcriptional profiles\, s
patial imaging at bulk and single cell resolution. We illustrate some of t
he challenges\, opportunities\, and ongoing work in developing and adaptin
g computational approaches to elucidate drivers of cancer therapy response
\, progression\, and metastasis towards developing biomarkers of therapy r
esponse and novel therapeutic targets.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/46/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Paul K. Newton (University of Southern California)
DTSTART:20231016T150000Z
DTEND:20231016T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/47
DESCRIPTION:Title: Three problems in mathematical oncology\nby Paul K. Newto
n (University of Southern California) as part of Mathematical and Computat
ional Biology Seminar Series\n\n\nAbstract\nI will introduce three problem
s in mathematical oncology which involve dynamics\, forecasting\, longitud
inal data\, and control theory. In the first problem\, I will describe our
work using Markov chain models to forecast metastatic progression in 12 d
ifferent soft tissue cancers. The models treat progression as a (weighted)
random walk on a directed graph whose nodes are metastatic tumor location
s. We estimate transition probabilities from site-to-site using historical
autopsy data (untreated progression) and longitudinal patient data (treat
ed progression) from Memorial Sloan Kettering and MD Anderson Cancer Cente
rs. We characterize the inherent predictability of each cancer type using
entropy methods. In the second problem\, I will describe models (both dete
rministic and stochastic) that use evolutionary game theory (replicator dy
namical systems with frequency dependent selection) to design novel adapti
ve chemotherapy schedules that mitigate chemoresistance by suppressing the
‘competitive release’ of resistant cells. The models make use of find
ing closed evolutionary cycles in the frequency distribution of competing
subpopulations of cells so that neither the resistant population nor the s
ensitive population ever reach fixation. The third problem will describe o
ur model of Covid-19 vaccine uptake as a reinforcement learning dynamic be
tween two populations: the vaccine adopters\, and the vaccine hesitant. We
use uptake data from the Center for Disease Control (CDC) to estimate the
payoff matrix governing the interaction between these two groups over tim
e and show they are playing a Hawk-Dove evolutionary game with an internal
evolutionarily stable Nash equilibrium. We then use the model\, along wit
h optimal control theory\, to test several hypotheses associated with the
size and timing of incentive programs to improve vaccine uptake (shift the
Nash equilibrium upward) as much as possible. The model shows diminishing
returns for larger incentive sizes.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/47/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Avi Ma’ayan (Icahn School of Medicine at Mount Sinai)
DTSTART:20231113T160000Z
DTEND:20231113T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/48
DESCRIPTION:Title: Rummagene: Mining Gene Sets from Supporting Materials of PMC
Publications\nby Avi Ma’ayan (Icahn School of Medicine at Mount Sina
i) as part of Mathematical and Computational Biology Seminar Series\n\n\nA
bstract\nEvery week thousands of biomedical research papers are published
with a portion of them containing supporting tables with data about genes\
, transcripts\, variants\, and proteins. For example\, supporting tables m
ay contain differentially expressed genes and proteins from transcriptomic
s and proteomics assays\, targets of transcription factors from ChIP-seq e
xperiments\, hits from genome-wide CRISPR screens\, or genes identified to
harbor mutations from GWAS studies. Because these gene sets are commonly
buried in the supplemental tables of research publications\, they are not
widely available for search and reuse. Rummagene\, available from https://
rummagene.com\, is a web server application that provides access to hundre
ds of thousands of human and mouse gene sets extracted from supporting mat
erials of publications listed on PubMed Central (PMC). To create Rummagene
\, we first developed a softbot that extracts human and mouse gene sets fr
om supporting tables of PMC publications. So far\, the softbot has scanned
5\,448\,589 PMC articles to find 121\,237 articles that contain 642\,389
gene sets. These gene sets are served for enrichment analysis\, free text\
, and table title search. Users of Rummagene can submit their own gene set
s to find matching gene sets ranked by their overlap with the input gene s
et. In addition to providing the extracted gene sets for search\, we inves
tigated the massive corpus of these gene sets for statistical patterns. We
show that the number of gene sets reported in publications is rapidly inc
reasing\, containing both short sets that are highly enriched in highly st
udied genes\, and long sets from omics profiling. We also demonstrate that
the gene sets in Rummagene can be used for transcription factor and kinas
e enrichment analyses\, and for gene function predictions. By combining ge
ne set similarity with abstract similarity\, Rummagene can be used to find
surprising relationships between unexpected biological processes\, concep
ts\, and named entities. Finally\, by overlaying the Rummagene gene set sp
ace with the Enrichr gene set space we can discover areas of biological an
d biomedical knowledge unique to each resource.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/48/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Daniel A. Cruz (University of Florida)
DTSTART:20230925T150000Z
DTEND:20230925T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/49
DESCRIPTION:Title: Topological data analysis of pattern formation in stem cell c
olonies\nby Daniel A. Cruz (University of Florida) as part of Mathemat
ical and Computational Biology Seminar Series\n\n\nAbstract\nConfocal micr
oscopy imaging provides valuable information about the current expression
states within in vitro cell cultures. However\, few tools exist to quantif
y the spatial organization of the cells observed in these images. We prese
nt a modular\, general-purpose pipeline that extracts cell-specific signal
intensities from confocal microscopy images. The pipeline then assigns ce
ll types based on corresponding intensities and quantifies spatial informa
tion among cell types through topological data analysis (TDA). We provide
an overview of TDA and discuss biological insights which we may gain from
applying our pipeline to microscopy images. In particular\, we focus on st
udying the pattern formation of human induced pluripotent stem cell (hiPSC
) cultures\, which have become powerful\, patient-specific test beds for i
nvestigating the early stages of embryonic development. By applying our pi
peline to images of hiPSC colonies\, we are able to detect and quantify ch
anges in pattern formation caused by cell-to-cell signaling and differenti
ation. We also contextualize our pipeline within a larger effort toward de
veloping quantitative tools for evaluating spatial models.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/49/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Guowei Wei (Michigan State University)
DTSTART:20240212T160000Z
DTEND:20240212T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/50
DESCRIPTION:Title: Mathematics in action: from pandemic to drug discovery\nb
y Guowei Wei (Michigan State University) as part of Mathematical and Compu
tational Biology Seminar Series\n\n\nAbstract\nMathematics underpins funda
mental theories in physics such as quantum mechanics\, general relativity\
, and quantum field theory. Nonetheless\, its success in modern biology\,
namely cellular biology\, molecular biology\, chemical biology\, genomics\
, and genetics\, has been quite limited. Artificial intelligence (AI) has
fundamentally changed the landscape of science\, engineering\, and technol
ogy in the past decade and holds a great future for discovering the rules
of life. However\, AI-based biological discovery encounters challenges ari
sing from the intricate complexity\, high dimensionality\, nonlinearity\,
and multiscale biological systems. We tackle these challenges by a mathema
tical AI paradigm. We have introduced persistent cohomology\, persistent s
pectral graphs\, persistent path Laplacians\, persistent sheaf Laplacians\
, and evolutionary de Rham-Hodge theory to significantly enhance AI's abil
ity to tackle biological challenges. Using our mathematical AI approaches\
, my team has been the top winner in D3R Grand Challenges\, a worldwide an
nual competition series in computer-aided drug design and discovery for ye
ars. By further integrating mathematical AI with millions of genomes isola
ted from patients\, we discovered the mechanisms of SARS-CoV-2 evolution a
nd accurately forecast emerging dominant SARS-CoV-2 variants months in adv
ance.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/50/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joceline Lega (University of Arizona)
DTSTART:20240415T150000Z
DTEND:20240415T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/51
DESCRIPTION:Title: Epidemics from the Eye of the Pathogen\nby Joceline Lega
(University of Arizona) as part of Mathematical and Computational Biology
Seminar Series\n\n\nAbstract\nWhen plotted in what I call ICC (Incidence v
ersus Cumulative Cases) coordinates\, noisy disease data appear to fluctua
te about a mean curve with generic properties. In this talk\, I will recou
nt the discovery of such universality [1] and describe recent work aimed a
t elucidating this behavior [2\, 3]. In particular\, exact results will be
provided for the deterministic and stochastic SIR models. In addition\, I
will explain how identifying trends in the ICC plane can lead to short-te
rm forecasts and illustrate this approach on COVID-19 cases and deaths in
the US [4].\n\nThis is joint work with Hannh Biegel\, Bill Fries\, Faryad
Sahneh\, and Joe Watkins.\n\n[1] J. Lega and H.E. Brown\, Data-driven outb
reak forecasting with a simple nonlinear growth model\, Epidemics 17\, 19
–26 (2016).\n\n[2] J. Lega\, Parameter estimation from ICC curves\, Jour
nal of Biological Dynamics 15\, 195-212 (2021).\n\n[3] F.D. Sahneh\, W. Fr
ies\, J.C. Watkins\, J. Lega\, Epidemics from the Eye of the Pathogen\, SI
AM J. Appl. Math. 82\, 2036-2056 (2022).\n\n[4] H. Biegel & J. Lega\, EpiC
ovDA: a mechanistic COVID-19 forecasting model with data assimilation\, ht
tps://arxiv.org/abs/2105.05471 (2021).\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/51/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Zoi Rapti (University of Illinois at Urbana-Champaign)
DTSTART:20240513T150000Z
DTEND:20240513T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/52
DESCRIPTION:Title: Generalized Lotka-Volterra equations on graphs\nby Zoi Ra
pti (University of Illinois at Urbana-Champaign) as part of Mathematical a
nd Computational Biology Seminar Series\n\n\nAbstract\nWe investigate the
stability of generalized Lotka-Volterra equations in network topologies\,
such as trees and complete graphs. In particular\, we have proved results
on the stability of solutions where all species in the community are nonze
ro\, namely all species persist. Our analytical findings are corroborated
by numerical simulations and supplement published studies. We give a short
proof of the result that tree networks with amensalistic\, commensalisti
c and antagonistic interactions are stable regardless of the interaction s
trength\, while tree networks with amensalistic\, commensalistic\, mutuali
stic and competitive interactions can be made unstable by choosing any of
the interaction strengths large enough. We also present findings on the ty
pes of networks and interactions that are characterized by the largest rea
l and imaginary parts of the eigenvalues of their corresponding Jacobian m
atrices. This is joint work with Lee DeVille and Shinhae Park.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/52/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Arman Rahmim (UBC Cancer Center)
DTSTART:20241021T150000Z
DTEND:20241021T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/53
DESCRIPTION:Title: Physiologically based Pharmacokinetic (PBPK) Modeling towards
Creation of Theranostic Digital Twins for Cancer Patients\nby Arman R
ahmim (UBC Cancer Center) as part of Mathematical and Computational Biolog
y Seminar Series\n\n\nAbstract\nIn this talk\, we emphasize that patient d
ata\, including images\, are not operable (clinically)\, but that digital
twins are. Based on the former\, the latter can be created. Subsequently\,
virtual clinical operations can be performed towards selection of optimal
therapies. Digital twins are beginning to emerge in the field of medicine
. We suggest that theranostic digital twins (TDTs) are amongst the most na
tural and feasible flavors of digitals twins. We elaborate on the importan
ce of TDTs in a future where ‘one-size-fits-all’ therapeutic schemes w
ill be transcended\; e.g. in radiopharmaceutical therapies (RPTs). Persona
lized RPTs can be deployed\, including optimized intervention parameters.
Examples include optimization of injected radioactivities\, sites of injec
tion\, injection intervals and profiles\, and combination therapies. Multi
-modal multi-scale images\, combined with other data and aided by artifici
al intelligence (AI) techniques\, can be utilized towards routine digital
twinning of our patients\, and will enable improved deliveries of RPTs and
overall healthcare.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/53/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thomas Yankeelov (University of Texas at Austin)
DTSTART:20250224T160000Z
DTEND:20250224T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/54
DESCRIPTION:Title: Personalizing interventions through mechanism-based digital t
wins\nby Thomas Yankeelov (University of Texas at Austin) as part of M
athematical and Computational Biology Seminar Series\n\n\nAbstract\nOur la
b is focused on developing tumor forecasting methods by integrating advanc
ed imaging technologies with mathematical models to predict tumor growth a
nd treatment response. In this presentation\, we will focus on how quanti
tative magnetic resonance imaging (MRI) data can be employed to calibrate
mathematical models built on first-order effects related to well-establish
ed “hallmarks” of cancer including proliferation\, migration/invasion\
, vascular status\, and drug-related tumor growth inhibition and cell deat
h. In particular\, we will present some of our recent results on using th
ese models to build personalized digital twins that provide a rigorous\, b
ut practical\, methodology for optimizing therapeutic interventions on a p
atient-specific basis.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/54/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adam MacLean (University of Southern California)
DTSTART:20241118T160000Z
DTEND:20241118T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/55
DESCRIPTION:Title: Modeling cell fate dynamics with single-cell genomics data\nby Adam MacLean (University of Southern California) as part of Mathemat
ical and Computational Biology Seminar Series\n\n\nAbstract\nCells make de
cisions to enable multicellular life. Cell fate decision-making underlies
development and homeostasis\, and goes awry as we age. Despite great promi
se\, we have yet to harness the high-resolution information on cell states
and fates that single-cell genomics data offer to understand cell fate de
cisions in development and aging. Nor do we know how these fate decisions
are controlled by gene regulatory networks. I will describe our recent wor
k constructing models of cell fate decisions in hematopoietic stem cells a
nd cancer. These models can be constrained using single-cell genomics data
\, leading to discovery of new network interactions that control decisions
points during cell state transitions.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/55/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yangyang Wang (Brandeis University)
DTSTART:20250324T150000Z
DTEND:20250324T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/56
DESCRIPTION:Title: Mixed-mode oscillations and flexible phase-locking in neural
oscillators\nby Yangyang Wang (Brandeis University) as part of Mathema
tical and Computational Biology Seminar Series\n\n\nAbstract\nMixed-mode o
scillations (MMOs) are complex oscillatory behaviors of multiple-timescale
dynamical systems in which there is an alternation of large-amplitude and
small-amplitude oscillations. In two-timescale systems\, MMOs can arise e
ither from a Canard mechanism associated with folded node singularities or
a delayed Andronov-Hopf bifurcation (DHB) of the fast subsystem. While MM
Os in two-timescale systems have been extensively studied\, less is known
regarding MMOs emerging in three-timescale systems. In this work\, we exam
ine the mechanisms of MMOs in three-timescale neural oscillators and explo
re how the interplay between Canard and DHB mechanisms can produce more ro
bust MMOs. Furthermore\, we examine the roles of these dynamics in facilit
ating flexible phase-locking in response to strong periodic inputs in neur
al oscillators with applications to speech perception.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/56/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Rachel Kuske (Georgia Institute of Technology)
DTSTART:20250428T150000Z
DTEND:20250428T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/57
DESCRIPTION:Title: Dynamical insights in identifying new data-driven mechanistic
microbial models\nby Rachel Kuske (Georgia Institute of Technology) a
s part of Mathematical and Computational Biology Seminar Series\n\n\nAbstr
act\nWe consider some recent model identification tools\, together with co
mplementary computations of dynamical characteristics that can often be ne
cessary to isolate relevant biological mechanisms based on data. In recent
studies of microbial dynamics\, specifically community behavior of bacter
ia and dynamics under antibiotic treatment\, the available data limits the
extensive use of these tools. Nevertheless\, we illustrate their utility\
, together with critical dynamical features\, in identifying new biologic
ally-relevant models that allow for heterogeneity and state-dependent feat
ures that are ubiquitous in the ecology and evolution of microbial dynamic
s.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/57/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mihalis Kavousanakis (National Technical University of Athens)
DTSTART:20250512T150000Z
DTEND:20250512T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/58
DESCRIPTION:Title: Optimizing cancer treatment schedules: a computational study
of combined therapies on vascular tumors\nby Mihalis Kavousanakis (Nat
ional Technical University of Athens) as part of Mathematical and Computat
ional Biology Seminar Series\n\n\nAbstract\nCancer treatment has significa
ntly advanced with therapies such as surgery\, chemotherapy\, radiotherapy
\, immunotherapy\, and hormonal therapy. However\, monotherapies face limi
tations\, making combination therapies a widely adopted strategy in modern
oncology. These combinations enhance efficacy through synergistic effects
\, reduce resistance development\, lower toxicity\, and broaden treatment
applicability. To explore and optimize combination treatments\, we adopt a
multiphase continuum modeling approach\, treating tissue as a mixture of
interacting cellular/fluid phases\, including healthy cells\, cancer cells
\, vasculature\, and interstitial fluid. We solve mass and momentum balanc
e equations for phase evolution and reaction-diffusion equations for criti
cal chemical species like nutrients\, VEGF\, and therapeutic agents. Given
the computational complexity of such simulations\, we apply Bayesian opti
mization to efficiently identify optimal treatment protocols. Our results
demonstrate that concurrent administration of cytotoxic and anti-VEGF agen
ts leads to improved outcomes\, in agreement with clinical data. We furthe
r extend this framework to optimize triple chemo-radiotherapy regimens\, i
ntegrating the rapid cytotoxic effects of radiation therapy alongside the
tumor-suppressive action of chemotherapy.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/58/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yang Kuang (Arizona State University)
DTSTART:20250915T150000Z
DTEND:20250915T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/59
DESCRIPTION:Title: Promising Bio-marks from Predictive Mathematical Models of Ho
rmone Treatment for Prostate Cancer\nby Yang Kuang (Arizona State Univ
ersity) as part of Mathematical and Computational Biology Seminar Series\n
\n\nAbstract\nProstate cancer is a serious public health concern. The prim
ary obstacle to effective long-term\nmanagement for prostate cancer patien
ts is the eventual development of treatment resistance.\nProstate specific
antigen (PSA) is the ubiquitous but inaccurate bio-marker used in mathema
tical\nand artificial intelligence models of prostate cancer. The growth o
f prostate and cancer cells\nproduces PSA\, but their growth is usually de
pendent on androgen. Clinically\, a drug that blocks\nthe production of an
drogen is often applied continuously past the point of effectiveness\, the
reby\nlosing future potential treatment combination with other drugs to av
oid or delay resistance. We\npresent models of predicting treatment failur
e due to drug resistance. The models are built on an\nevolutionary interpr
etation of Droop cell quota theory. We analyze our proposed methods using\
npatient PSA and androgen data from a clinical trial of intermittent treat
ment with androgen\ndeprivation therapy. Our results produce two indicator
s of treatment failure which can serve as\naccurate and practical bio-mark
ers in clinical settings. The first indicator\, proposed from the\nevoluti
onary nature of the cancer population\, is calculated using our mathematic
al model with\na predictive accuracy of 87.3% (sensitivity: 96.1%\, specif
icity: 65%). The second indicator\,\nconjectured from the implication of t
he first indicator\, is calculated directly from serum androgen\nand PSA d
ata with a predictive accuracy of 88.7% (sensitivity: 90.2%\, specificity
85%). Our results\ndemonstrate the feasibility of using an evolutionary tu
mor dynamics model in combination with\npatient data to serve as digital t
win to aid in the adaptive management of prostate cancer.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/59/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jasmine Foo (University of Minnesota-Twin Cities)
DTSTART:20260323T150000Z
DTEND:20260323T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/60
DESCRIPTION:Title: Dosing in a Complex World: The Tumor Microenvironment as a Mo
dulator of Therapeutic Response\nby Jasmine Foo (University of Minneso
ta-Twin Cities) as part of Mathematical and Computational Biology Seminar
Series\n\n\nAbstract\nThe tumor microenvironment (TME) is a key driver of
therapy response in cancer. In this talk I will discuss two complementary
modeling studies that explore how the TME modulates therapy outcome\, and
how this impacts dosing strategies. First\, I will discuss chemically se
lf-assembled nanorings\, a novel class of multivalent bispecific T cell en
gagers\, which facilitate the recruitment of a patient's T cells to kill t
umor cells. Using a model calibrated to in vitro experiments with human ep
idermoid carcinoma cells\, we will explore the key drivers of patient resp
onse heterogeneity\, discuss a dosing threshold that influences response
variability\, and propose a biomarker of therapeutic response. Second\, I
will discuss drug-induced stromal-mediated resistance in colorectal cancer
\, and examine how the drug-stroma-tumor interaction results in complex do
se-response relationships and their implications for dosing protocols. To
gether these studies illustrate how mechanistic modeling can lead to actio
nable dosing principles from complex TME interactions.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/60/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Guillermo Lorenzo (University of A Coruña)
DTSTART:20260223T160000Z
DTEND:20260223T170000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/62
DESCRIPTION:Title: Patient-specific forecasting of prostate cancer growth and ra
diotherapy response using biomechanistic models and hybrid classifiers
\nby Guillermo Lorenzo (University of A Coruña) as part of Mathematical a
nd Computational Biology Seminar Series\n\n\nAbstract\nThe current clinica
l protocols to manage prostate cancer (PCa) enable the detection and succe
ssful treatment of these tumors at an early stage. However\, recent studie
s suggest that many PCa patients are being overtreated\, and hence prone t
o potential treatment side-effects (e.g.\, incontinence\, impotence) that
can adversely impact their quality of life without improving longevity. Fu
rthermore\, undertreatment of PCa is another important clinical challenge\
, as it may lead to rapid growth of aggressive tumors\, treatment failure\
, and reduced survival. The overtreatment and undertreatment of PCa have t
he same origin: the limited individualization and observational nature of
the clinical management of these tumors. In this talk\, I propose to addre
ss these critical\, unresolved issues by using patient-specific forecasts
of PCa growth and treatment response\, along with hybrid classifiers that
take biomechanistic inputs to predict the occurrence of clinical events of
interest. I will present the application of this predictive framework in
two scenarios where longitudinal data are collected as part of the standar
d-of-care management of PCa: active surveillance of lower-risk tumors befo
re primary treatment\, and the post-treatment monitoring of patients after
radiotherapy. For each application\, I will show how a biomechanistic mod
el can be built\, calibrated\, and validated to obtain personalized predic
tions of tumor growth and therapeutic response. Then\, logistic classifier
s will be trained with biomechanistic model outputs to identify tumors pro
gressing towards higher-risk disease during active surveillance or develop
ing a recurrence after radiotherapy. Finally\, although further developmen
t and validation over larger cohorts are needed\, I will posit that the te
chnologies presented in this talk can contribute to advance the observatio
nal\, population-based standards in clinical oncology towards a predictive
\, personalized paradigm.\n\nREFERENCES\n1) G. Lorenzo\, J.S. Heiselman\,
M.A. Liss\, M.I. Miga\, H. Gomez\, T.E. Yankeelov\, A. Reali\, T.J.R. Hugh
es (2024). A pilot study on patient-specific computational forecasting of
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G. Lorenzo\, N. di Muzio\, C.L. Deantoni\, C. Cozzarini\, A. Fodor\, A. B
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1)\, 105430. \nDOI: https://doi.org/10.1016/j.isci.2022.105430\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/62/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yiwei Wang (University of California\, Riverside)
DTSTART:20251020T150000Z
DTEND:20251020T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/63
DESCRIPTION:Title: Energetic Variational Modeling of Active Nematics: A Toner–
Tu Model with ATP Hydrolysis\nby Yiwei Wang (University of California\
, Riverside) as part of Mathematical and Computational Biology Seminar Ser
ies\n\n\nAbstract\nActive biological materials\, such as cytoskeletal fila
ments and motor proteins\, convert chemical energy into mechanical work th
rough nonequilibrium processes like ATP hydrolysis. In this talk\, we pres
ent a thermodynamically consistent energetic variational model that captur
es this chemo-mechanical coupling in active nematic systems. Extending the
classical Toner–Tu framework\, the model integrates reaction kinetics\,
self-advection\, and polarization dynamics within a unified energy–diss
ipation structure derived from the first principles. The reaction rate dep
ends explicitly on local mechanical states\, revealing how chemical and me
chanical feedback jointly regulate pattern formation and active transport.
This variational formulation not only preserves consistency with nonequil
ibrium thermodynamics but also provides a transparent pathway for modeling
energy transduction and regulation mechanisms in living matter.\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/63/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Suzanne Sindi (University of California\, Merced)
DTSTART:20260413T150000Z
DTEND:20260413T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/64
DESCRIPTION:by Suzanne Sindi (University of California\, Merced) as part o
f Mathematical and Computational Biology Seminar Series\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/64/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Zhiliang Xu (University of Notre Dame)
DTSTART:20260504T150000Z
DTEND:20260504T160000Z
DTSTAMP:20260404T094122Z
UID:UMassMathBio/65
DESCRIPTION:by Zhiliang Xu (University of Notre Dame) as part of Mathemati
cal and Computational Biology Seminar Series\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/UMassMathBio/65/
END:VEVENT
END:VCALENDAR