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BEGIN:VEVENT
SUMMARY:Stanislav Shvartsman (Princeton University)
DTSTART:20200615T150000Z
DTEND:20200615T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/1
DESCRIPTION:Title: How to make a large cell\nby Stanislav Shvartsman (Princeton Univ
ersity) as part of Mathematical and Computational Biology Seminar Series\n
\n\nAbstract\nTo see a single cell\, one usually requires a microscope. Ho
wever\, some cells can be seen with the naked eye\; a chicken egg\, for ex
ample\, is a macroscopic object that contains just one cell. The largest h
uman cell\, at ~50 microns in diameter\, is also an egg - the oocyte - and
regularly features in popular science movies on in vitro fertilization an
d early stages of our development. Across species\, proper development of
an egg is critically dependent on auxiliary cells that nurse the oocyte\,
supplying it with components that cannot be synthesized by the oocyte itse
lf. Using the fruit fly\, Drosophila melanogaster as an experimental model
\, one that provides unrivaled opportunities for combining advanced geneti
c perturbations and high-resolution imaging of molecular and cellular proc
esses\, I will present data from our latest studies that suggest that grow
ing oocytes can control their own nursing by the auxiliary cells. Our expe
riments have also led us to an interesting class of mathematical models in
which limit cycle oscillators are coupled on tree-like networks. Computat
ional analysis of synchronized regimes in these models makes clear experim
ental predictions and moves us one step closer to understanding the mechan
isms that coordinate the growth and development of one of the animal’s l
argest cells.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Helen Byrne (University of Oxford)
DTSTART:20200629T150000Z
DTEND:20200629T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/2
DESCRIPTION:Title: Data-driven mathematical oncology: evolution\, revolution or co-evolu
tion?\nby Helen Byrne (University of Oxford) as part of Mathematical a
nd Computational Biology Seminar Series\n\n\nAbstract\nThe past twenty-fiv
e years have witnessed an unparalleled increase in understanding of cancer
biology. This transformation is exemplified by Hanahan and Weinberg's dec
ision in 2011 to expand their Hallmarks of Cancer from six traits to ten!
At the same time\, the prominence of mathematical modelling as a tool for
unravelling the complex processes that contribute to the initiation and pr
ogression of tumours has increased\, \n\nIn this talk\, I will revisit ear
ly models of avascular tumour growth\, angiogenesis and tumour blood flow.
Following Hanahan and Weinberg's lead\, I will reflect on how closer coll
aboration with cancer scientists and\, in particular\, access to experimen
tal data have driven extensions to these models which increase their abili
ty to generate qualitative and quantitative predictions about the growth a
nd response to treatment of solid tumours.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Natalia Komarova (University of California Irvine)
DTSTART:20200713T150000Z
DTEND:20200713T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/3
DESCRIPTION:Title: Mathematics of Evolution: mutations\, selection\, and random environm
ents\nby Natalia Komarova (University of California Irvine) as part of
Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nEvol
utionary dynamics permeates life and life-like systems. Mathematical metho
ds can be used to study evolutionary processes\, such as selection\, mutat
ion\, and drift\, and to make sense of many phenomena in life sciences. I
will present two very general types of evolutionary patterns\, loss-of-fun
ction and gain-of-function mutations\, and discuss scenarios of population
dynamics -- including stochastic tunneling and calculating the rate of e
volution. I will also talk about evolution in random environments. The pr
esence of temporal or spatial randomness significantly affects the competi
tion dynamics in populations and gives rise to some counterintuitive obser
vations. Applications include origins of cancer\, passenger and driver mut
ations\, and how aspirin might help prevent cancer.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Santiago Schnell (University of Michigan)
DTSTART:20200727T150000Z
DTEND:20200727T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/4
DESCRIPTION:Title: Developing models for the accurate measurement of enzyme kinetic para
meters\nby Santiago Schnell (University of Michigan) as part of Mathem
atical and Computational Biology Seminar Series\n\n\nAbstract\nThe conditi
ons under which the Michaelis–Menten equation accurately captures the st
eady-state kinetics of a simple enzyme-catalyzed reaction is contrasted wi
th the conditions under which the same equation is used to estimate kineti
c parameters in progress curve or initial rate experiments. A systematic a
nalysis of kinetic models shows that satisfaction of the underlying assump
tions leading to the Michaelis–Menten equation are necessary\, but not s
ufficient to guarantee accurate estimation of kinetic parameters. We prese
nt a detailed error analysis and numerical “experiments” to investigat
e experimental designs for accurate estimation of kinetic parameters in pr
ogress curve and initial rate experiments. Our analysis suggests some of t
he leading causes for reported large variance in error estimates of enzyme
activity between different laboratories.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:BREAK - no talks
DTSTART:20200810T150000Z
DTEND:20200810T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/5
DESCRIPTION:by BREAK - no talks as part of Mathematical and Computational
Biology Seminar Series\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:James Glazier (Indiana University)
DTSTART:20200824T150000Z
DTEND:20200824T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/6
DESCRIPTION:Title: Multiscale multicellular modeling of tissue function and disease usi
ng CompuCell3D: A simplified computer simulation of acute primary viral in
fection and immune response in an epithelial tissue\nby James Glazier
(Indiana University) as part of Mathematical and Computational Biology Sem
inar Series\n\n\nAbstract\nMultiscale multicellular models combine represe
ntations of subcellular biological networks\, cell behaviors\, tissue leve
l effects and whole body effects to describe tissue outcomes during develo
pment\, homeostasis and disease. I will briefly introduce these simulation
methodologies\, the CompuCell3D simulation environment and their applicat
ions\, then focus on a multiscale simulation of an acute primary infection
of an epithelial tissue infected by a virus like SARS-CoV-2\, a simplifie
d cellular immune response and viral and immune-induced tissue damage. The
model exhibits four basic parameter regimes: where the immune response f
ails to contain or significantly slow the spread of viral infection\, wher
e it significantly slows but fails to stop the spread of infection\, where
it eliminates all infected epithelial cells\, but reinfection occurs from
residual extracellular virus and where it clears the both infected cells
and extracellular virus\, leaving a substantial fraction of epithelial cel
ls uninfected. Even this simplified model can illustrate the effects of a
number of drug therapy concepts. Inhibition of viral internalization and f
aster immune-cell recruitment promote containment of infection. Fast viral
internalization and slower immune response lead to uncontrolled spread of
infection. Existing antivirals\, despite blocking viral replication\, sho
w reduced clinical benefit when given later during the course of infection
. Simulation of a drug which reduces the replication rate of viral RNA\, s
hows that a low dosage that provides only a relatively limited reduction o
f viral RNA replication greatly decreases the total tissue damage and extr
acellular virus when given near the beginning of infection. However\, even
a high dosage that greatly reduces the rate of RNA replication rapidly lo
ses efficacy when given later after infection. Many combinations of dosage
and treatment time lead to distinct stochastic outcomes\, with some repli
cas showing clearance or control of the virus (treatment success)\, while
others show rapid infection of all epithelial cells (treatment failure). T
his switch between a regime of frequent treatment success and frequent fai
lure occurs is quite abrupt as the time of treatment increases. The model
is open-source and modular\, allowing rapid development and extension of i
ts components by groups working in parallel.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alain Goriely (University of Oxford)
DTSTART:20200921T150000Z
DTEND:20200921T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/7
DESCRIPTION:Title: Modelling dementia\nby Alain Goriely (University of Oxford) as pa
rt of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\
nNeurodegenerative diseases such as Alzheimer’s or Parkinson’s are dev
astating conditions with poorly understood mechanisms and no known cure. Y
et a striking feature of these conditions is the characteristic pattern of
invasion throughout the brain\, leading to well-codified disease stages v
isible to neuropathology and associated with various cognitive deficits an
d pathologies. How can we use mathematical modelling to gain insight into
this process and\, doing so\, gain understanding about how the brain works
? In this talk\, I will show that by linking new methods of applied mathem
atics to recent progress in imaging\, we can unravel some of the universal
features associated with dementia and\, more generally\, brain functions.
\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mohit Kumar Jolly (Indian Institute of Science)
DTSTART:20200907T150000Z
DTEND:20200907T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/8
DESCRIPTION:Title: Multi-scale modeling of the dynamics of cancer metastasis: a computa
tional systems biology approach\nby Mohit Kumar Jolly (Indian Institut
e of Science) as part of Mathematical and Computational Biology Seminar Se
ries\n\n\nAbstract\nMetastasis – the spread of cancer cells from one org
an to another – causes above 90% of all cancer-related deaths. Despite
extensive ongoing efforts in cancer genomics\, no unique genetic or mutati
onal signature has emerged for metastasis. However\, a hallmark that has b
een observed in metastasis is adaptability or phenotypic plasticity – th
e ability of a cell to reversibly switch among different phenotypes (state
s) in response to various internal or external stimuli. This talk will des
cribe how the concepts of nonlinear dynamics can help (a) identify how can
cer cells can leverage this plasticity to drive cancer metastasis\, (b) in
terpret existing clinical data\, (c) guide the next set of crucial in vitr
o and in vivo experiments\, and (d) elucidate the role of non-mutational m
echanisms in cancer biology. Collectively\, my work highlights how an iter
ative crosstalk between mathematical modeling and experiments can both gen
erate novel insights into the multi-scale dynamics of phenotypic plasticit
y and uncover previously unknown accelerators of metastasis.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Heiko Enderling (Moffitt Cancer Center)
DTSTART:20201019T150000Z
DTEND:20201019T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/9
DESCRIPTION:Title: Mathematical modeling of cancer radiotherapy\; the past\, the present
\, and the future\nby Heiko Enderling (Moffitt Cancer Center) as part
of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nRa
diotherapy is the single most applied cancer treatment in the world. More
than half of all cancer patients will receive radiation at some point duri
ng their clinical care. Most clinical protocols are informed by the averag
e results of large prospective clinical studies. Thus\, most patients rec
eive the same total dose delivered in the same daily fractionation protoco
l. To date we have no reliable biomarkers to predict whether an individual
patient will be controlled by radiation or not. As the field of radiation
oncology is driven by medical physics\, mathematical modeling in radiothe
rapy has a long history. Here we discuss different novel mathematical mode
ling approaches to evaluate if tumor growth and treatment response dynamic
s can be used to personalize and dynamically adapt radiation on a per pati
ent basis. We will extend the modeling into studies of tumor-immune intera
ctions to identify the systemic consequences of local radiotherapy\, and h
ow to derive the optimal radiation dose to best harness radiation-induced
immune system activation.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mary Lou Zeeman (Bowdoin College)
DTSTART:20201005T150000Z
DTEND:20201005T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/10
DESCRIPTION:Title: A flow-kick framework for studying resilience\nby Mary Lou Zeema
n (Bowdoin College) as part of Mathematical and Computational Biology Semi
nar Series\n\n\nAbstract\nAs climate change and human activities deliver n
ew disturbance patterns to urban and ecological systems\, resilience quest
ions make us look at familiar mathematics through a new lens. Resilience i
s a slippery concept that has different meanings in different contexts. It
is often described as the ability of a system to absorb change and distur
bance while maintaining its basic structure and function. There is\, there
fore\, an inherent interplay between transient dynamics and perturbation i
n resilience questions\, especially when the perturbations are repeated. T
here is also an interplay between qualitative and quantitative data. If we
interpret the “structure” of a system as it’s dynamical behavior\,
then its “function” is more value-laden as there are typically “desi
rable” and “undesirable” regions of state space\, corresponding to d
esirable or undesirable properties of the system. \n\nIn this talk\, we su
bject the flow of an autonomous system of ODEs to regular shocks (“kicks
”) of constant size and direction\, representing repeated\, discrete dis
turbances. The resulting flow-kick systems occupy a surprisingly under-exp
lored area between deterministic and stochastic dynamics. We illustrate so
me of the dynamical properties of flow-kick systems in the context of resi
lience in ecological examples\, and describe some of the open mathematical
questions they raise.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thomas Hillen (University of Alberta)
DTSTART:20201102T160000Z
DTEND:20201102T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/11
DESCRIPTION:Title: Mathematical Modeling of the Immune-Mediated Theory of Metastasis\nby Thomas Hillen (University of Alberta) as part of Mathematical and Co
mputational Biology Seminar Series\n\n\nAbstract\nAccumulating experimenta
l and clinical evidence suggests that the immune response to\ncancer is no
t exclusively anti-tumor. In fact\, several pro-tumor effects of the immun
e system have been identified\, such as production of growth factors\, est
ablishment of angiogenesis\, inhibition of immune response\, initiation of
cell movement and metastasis\, and establishment of metastatic niches. \n
\nBased on experimental data\, we develop a mathematical model for the imm
une-mediated theory of metastasis\, which includes anti- and pro-tumor eff
ects of the immune system. The immune-mediated theory of metastasis can e
xplain dormancy of metastasis and metastatic blow-up after resection of t
he primary tumor. It can explain increased metastasis at sites of injury\,
and the relatively poor performance of Immunotherapies\, due to pro-tumor
effects of the immune system. \nOur results suggest that further work is
warranted to fully elucidate and control the pro-tumor effects of the immu
ne system in metastatic cancer. (with Adam Rhodes)\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Veronica Ciocanel (Duke University)
DTSTART:20201116T160000Z
DTEND:20201116T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/12
DESCRIPTION:Title: Modeling and data analysis for intracellular protein organization\nby Veronica Ciocanel (Duke University) as part of Mathematical and Comp
utational Biology Seminar Series\n\n\nAbstract\nActin filaments are protei
n polymers that interact with motor proteins inside cells and play importa
nt roles in cell motility\, shape\, and development. Depending on its func
tion\, this dynamic network of interacting proteins reshapes and organizes
in a variety of structures\, including bundles\, clusters\, and contracti
le rings.\nMotivated by observations from the reproductive system of the r
oundworm C. elegans\, we use an agent-based modeling framework to simulate
interactions between actin filaments and myosin motor proteins inside cel
ls. We also develop tools based on topological data analysis to understand
time-series data extracted from these filamentous network interactions. O
ur analysis suggests potential mechanistic differences between motor prote
ins that are believed to shape the organization of structures such as circ
ular rings. In addition\, we show that changes in actin filament treadmill
ing may significantly modulate the actin-myosin network organization durin
g cell cycle progression.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Leonid Hanin (Idaho State University)
DTSTART:20201130T160000Z
DTEND:20201130T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/13
DESCRIPTION:Title: Mathematical discovery of natural laws in biomedical sciences with a
pplication to metastasis\nby Leonid Hanin (Idaho State University) as
part of Mathematical and Computational Biology Seminar Series\n\n\nAbstrac
t\nMathematical modeling of systemic biomedical processes faces two princi
pal challenges: (1) enormous complexity of these processes and (2) variabi
lity and heterogeneity of individual characteristics of biological systems
and organisms. As a result\, in the grand scheme of things\, mathematical
models have so far played an auxiliary role in biomedical sciences. We pr
opose a new methodology of mathematical modeling that would allow mathemat
ics to give\, in certain cases\, definitive answers to important questions
that elude empirical resolution. The new methodology is based on two idea
s: (1) to employ mathematical models that are so general and flexible that
they can account for many possible mechanisms\, both known and unknown\,
of biomedical processes of interest\; (2) to find those model parameters w
hose optimal values are independent of observations. These universal param
eter values may reveal general regularities in biomedical processes (that
we call natural laws). Existence of such universal parameters presupposes
that the model does not meet the conditions required for consistency of th
e maximum likelihood estimator.\n\nWe illustrate this approach with the di
scovery of a natural law governing cancer metastasis. Specifically\, we fo
und that under minimal mathematical and biomedical assumptions the likelih
ood-maximizing scenario of metastatic cancer progression is always the sam
e: complete suppression of metastatic growth before primary tumor resectio
n followed by an abrupt growth acceleration after surgery. This scenario i
s widely observed in clinical practice\, represents a common knowledge amo
ng veterinarians\, and is supported by a wealth of experimental studies on
animals and clinical observations accumulated over the last 115 years. Fu
rthermore\, several biological mechanisms\, both hypothetical and experime
ntally verified\, have been proposed that could explain this natural law.
The above scenario does not preclude other possibilities that are also obs
erved in clinical practice. In particular\, metastases may surface before
surgery or may remain dormant thereafter.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jonathan Rubin (University of Pittsburgh)
DTSTART:20201214T160000Z
DTEND:20201214T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/14
DESCRIPTION:Title: Multiple roles of synaptic “inhibition” & how they arise in deci
sion-making pathways in the basal ganglia\nby Jonathan Rubin (Universi
ty of Pittsburgh) as part of Mathematical and Computational Biology Semina
r Series\n\n\nAbstract\nThis talk concerns topics in mathematical neurosci
ence but will not assume any specific knowledge of neuroscience. It shoul
d be of interest to anyone who would like to learn more about general idea
s of mathematical neuroscience or about certain specific topics: the role
of the basal ganglia in decision-making and action selection\, cortico-st
riatal synaptic plasticity\, integration of multiple streams of inhibition
in neural circuits\, and mechanisms of neural synchronization and oscilla
tions. \n \nThe phrase “inhibition” suggests a holding back or suppres
sion of activity. It has long been recognized that the roles of synaptic
inhibition in neuronal circuits can be more diverse\, however\, and includ
e promotion of activity through effects such as post-inhibitory rebound an
d disynaptic disinhibition. The basal ganglia (BG) is a hub for the rewar
d signal dopamine and is believed to be involved in decision-making and ac
tion selection. Interestingly\, most synaptic pathways within the BG invo
lve neurotransmitters that are traditionally inhibitory. In the first sec
tion of my talk\, I will introduce this circuitry and present modeling of
how these pathways can collaborate to produce reward-driven action. I wil
l also present joint work with Tim Verstynen\, Cati Vich and our trainees\
, which (1) introduces a way to map between biologically detailed models a
nd more abstract decision-making models and (2) suggests how different BG
inhibitory neurons serve different roles in terms of evidence accumulation
and decision thresholds. In the second section of my talk\, I will prese
nt work with postdoc Ryan Phillips and our collaborator Aryn Gittis in whi
ch we model the integration of two inhibitory pathways by BG output neuron
s. Our modeling takes into account chloride dynamics and its impact on sy
naptic reversal potentials and shows how these pathways can actually induc
e excitatory effects\, can contribute to synchronization and oscillations\
, and can affect action selection\, in ways that may be related to Parkins
on’s disease.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Leah Edelstein-Keshet (University of British Columbia)
DTSTART:20210322T150000Z
DTEND:20210322T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/15
DESCRIPTION:Title: Mathematical and computational models: from sub cellular to multicel
lular behaviour\nby Leah Edelstein-Keshet (University of British Colum
bia) as part of Mathematical and Computational Biology Seminar Series\n\n\
nAbstract\nDepending on their internal structure (the cytoskeleton) animal
cells can take on many shapes: compact\, flat\, long\, polarized\, or ram
ified. Some cell types adhere tightly to one another\, forming sheet-like
tissue (epithelia)\, while other types\, such as white blood cells (neutro
phils)\, migrate\, seeking pathogens to destroy. In this talk\, I will des
cribe how we use mathematical and computational models to address a number
of biological questions about cell shape and motility\, including the fol
lowing: What mechanisms account for directed migration of neutrophils? How
does the cell environment (extracellular matrix\, ECM) affect cell migrat
ion? How can we understand more complex cell migration patterns\, includin
g oscillations and internal waves of activity? How do we bridge from an un
derstanding of single cells to that of multicellular collective migration?
I will argue that we can use computational modeling as a tool in biologic
al discovery\, both to test hypotheses\, to probe systems that are not eas
ily measured experimentally\, and to gain insights that would otherwise be
obscure.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Philip Maini (University of Oxford)
DTSTART:20210125T160000Z
DTEND:20210125T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/16
DESCRIPTION:Title: Modelling collective cell movement in biology and medicine\nby P
hilip Maini (University of Oxford) as part of Mathematical and Computation
al Biology Seminar Series\n\n\nAbstract\nCollective cell movement occurs t
hroughout biology and medicine and there\nare many common features shared
across different areas. I will review\nwork we have carried out over the p
ast few years on\n(i) systematically deriving a PDE model for tumour angio
genesis from a discrete\nformulation and comparing this model with the cla
ssical\, phenomenological snail-trail\nmodel\;\n(ii) agent-based models fo
r cranial neural crest cell migration in a collaboration with\nexperimenta
l biologists that has revealed a number of new biological insights.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mark Chaplain (University of St Andrews)
DTSTART:20210222T160000Z
DTEND:20210222T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/17
DESCRIPTION:Title: A Mathematical Framework for Modelling the Metastatic Spread of Canc
er\nby Mark Chaplain (University of St Andrews) as part of Mathematica
l and Computational Biology Seminar Series\n\n\nAbstract\nInvasion and met
astasis are two of the hallmarks of cancer and are intimately connected pr
ocesses. Invasion\, as the name suggests\, involves cancer cells spreading
out from the main cancerous mass into the surrounding tissue\, through pr
oduction and secretion of matrix degrading enzymes. Metastatic spread is t
he process whereby invasive cancer cells enter nearby blood vessels (or ly
mph vessels)\, are carried around the body in the main circulatory system
and then succeed in escaping from the circulatory system at distant second
ary sites where the growth of the cancer starts again. It is this metast
atic spread that is responsible for around 90% of deaths from cancer. To s
hed light on the metastatic process\, we present a mathematical modelling
framework that captures for the first time the interconnected processes of
invasion and metastatic spread of individual cancer cells in a spatially
explicit manner—a multigrid\, hybrid\, individual-based approach. This f
ramework accounts for the spatiotemporal evolution of mesenchymal- and epi
thelial-like cancer cells\, membrane-type-1 matrix metalloproteinase (MT1-
MMP) and the diffusible matrix metalloproteinase-2 (MMP-2)\, and for their
interactions with the extracellular matrix. Using computational simulatio
ns\, we demonstrate that our model captures all the key steps of the invas
ion-metastasis cascade\, i.e. invasion by both heterogeneous cancer cell c
lusters and by single mesenchymal-like cancer cells\; intravasation of the
se 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 associa
ted risk of cell death and disaggregation of clusters\; extravasation of c
lusters and single cells\; and metastatic growth at distant secondary site
s in the body.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Olivia Prosper (University of Tennessee\, Knoxville)
DTSTART:20210208T160000Z
DTEND:20210208T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/18
DESCRIPTION:Title: Modeling within-mosquito dynamics of the malaria parasite\nby Ol
ivia Prosper (University of Tennessee\, Knoxville) as part of Mathematical
and Computational Biology Seminar Series\n\n\nAbstract\nThe malaria paras
ite Plasmodium falciparum requires a vertebrate host and a female Anophele
s mosquito to complete a full life cycle\, with sexual reproduction occurr
ing in the mosquito. While parasite dynamics within the vertebrate host\,
such as humans\, has been extensively studied\, less is understood about d
ynamics within the mosquito\, a critical component of malaria transmission
dynamics. This sexual stage of the parasite life cycle allows for the pro
duction of genetically novel parasites. In the meantime\, a mosquito’s b
iology creates bottlenecks in the infecting parasites’ development. We d
eveloped a two-stage stochastic model of the generation of parasite divers
ity within a mosquito and were able to demonstrate the importance of heter
ogeneity amongst parasite dynamics across a population of mosquitoes on es
timates of parasite diversity. A key epidemiological parameter related to
the timing of onward transmission from mosquito to vertebrate host is the
extrinsic incubation period (EIP). Using simple models of within-mosquito
parasite dynamics fitted to empirical data\, we investigated factors influ
encing the EIP.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Miranda Teboh-Ewungkem (Lehigh University)
DTSTART:20210308T160000Z
DTEND:20210308T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/19
DESCRIPTION:Title: Malaria and Mathematics as Viewed from the Lens of the Transmitting
Mosquitoes\nby Miranda Teboh-Ewungkem (Lehigh University) as part of M
athematical and Computational Biology Seminar Series\n\n\nAbstract\nMalari
a is a disease caused by Plasmodium parasites and transmitted from human t
o human via a bite from an infectious blood feeding female Anopheles sp mo
squito. Successful transmission of the parasite to humans requires that a
susceptible female mosquito feeds on two distinct humans – one infected
with the parasite and the other susceptible\, at two distinct sequential t
ime points. In addition\, the parasite must be in its transmissible form i
n the mosquito at the latter feeding. The bottlenecks involved in the proc
ess illuminates how the parasite\, driven by the need to survive\, has cap
tured the evolutionary and reproductive needs of the mosquito to ensure th
e parasite’s survivability. Thus\, understanding the disease through the
lens of the transmitting mosquitoes\, driven by the evolutionary need to
survive\, has shown that interesting dynamics can be observed even under s
imple mass action assumptions. Moreover\, it allows for the incorporation
of mosquito gonotrophic cycles and how these cycles contribute to mosquito
abundance that can directly and indirectly affect malaria transmissibilit
y and intensity. It also illuminates how a mosquito’s age is linked to d
isease transmissibility success when the parasite dynamics is incorporated
into an interactive model that captures the interaction of mosquitoes\, h
umans and the malaria causing parasite. A by-product of explicitly incorpo
rating the mosquitoes’ gonotrophic cycles is the implicit embedding of t
he incubation period of the disease within the mosquito population in the
modelling framework. In this talk\, I will present a series of results tha
t have been obtained when malaria disease transmissibility is studied via
the lens of the transmitting mosquito.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Víctor M. Pérez García (Universidad de Castilla-La Mancha)
DTSTART:20210405T150000Z
DTEND:20210405T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/20
DESCRIPTION:Title: Scaling laws and evolutionary dynamics in cancer: Recent results and
open mathematical problems.\nby Víctor M. Pérez García (Universida
d de Castilla-La Mancha) as part of Mathematical and Computational Biology
Seminar Series\n\n\nAbstract\nMost physical and other natural systems are
complex entities that are composed of a large number of interacting indiv
idual elements. It is a surprising fact that they often obey the so-called
scaling laws that relate an observable quantity to a measure of the size
of the system [1]. In this talk I will describe the discovery of universal
scaling laws in human cancers [2] and how that implies the increase of tu
mor aggressiveness that leads to an explosive growth as the disease progre
sses. The observations can be understood using different types of biologic
ally inspired mathematical models. The most complex ones are discrete and
recapitulate the variety of clonal populations emerging within neoplasms a
nd their interactions [3]. However\, most of the observed phenomena can be
described using different types of nonlocal partial differential equation
s. The mathematical approaches lead to the definition of different biomark
ers of the disease aggressiveness that have been validated using cancers i
maging data [1\,3].\n\nI will also discuss several open mathematical probl
ems of relevance arising in the context of this research.\n[1] West G\, Sc
ale: The Universal Laws of Life and Death in Organisms\, Cities and Compan
ies. Penguin (2018).\n\n[2] V. M. Pérez-García et al\, Universal scaling
laws rule explosive growth in human cancers\, Nature Physics 16\, 1232-12
37 (2020).\n\n[3] J. Jiménez-Sánchez\, A. Martínez-Rubio\, A. Popov\, J
. Pérez-Beteta\, Y. Azimzade\, D. Molina-García\, J. Belmonte-Beitia\, G
. F. Calvo\, V. M. Pérez-García. A mesoscopic simulator to uncover heter
ogeneity and evolutionary dynamics in tumors. PLOS Computational Biology (
2021).\n\n[4] J. Jiménez-Sánchez\, J. J. Bosque\, G. A. Jiménez-Londoñ
o\, 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 reveals met
abolic imaging biomarkers. Proceedings of the National Academy of Sciences
118(6) e2018110118 (2021).\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mark Lewis (University of Alberta)
DTSTART:20210419T150000Z
DTEND:20210419T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/21
DESCRIPTION:Title: Population Dynamics in Changing Environments\nby Mark Lewis (Uni
versity of Alberta) as part of Mathematical and Computational Biology Semi
nar Series\n\n\nAbstract\nClassical population dynamics problems assume co
nstant unchanging environments. However\, realistic environments fluctuate
in both space and time. My lecture will focus on the analysis of populati
on dynamics in environments that shift spatially\, due either to advective
flow (eg.\, river population dynamics) or to changing environmental condi
tions (eg.\, climate change). The emphasis will be on the analysis of nonl
inear advection-diffusion-reaction equations and related models in the cas
e where there is strong advection and environments are heterogeneous. I wi
ll use methods of spreading speed analysis and "inside dynamics" to unders
tand qualitative outcomes. Applications will be made to river populations
and to the genetic structure of populations subject to climate change.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Germán Enciso (University of California Irvine)
DTSTART:20210503T150000Z
DTEND:20210503T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/22
DESCRIPTION:Title: Stochastic Modeling of Nucleosome Dynamics and Gene Expression\n
by Germán Enciso (University of California Irvine) as part of Mathematica
l and Computational Biology Seminar Series\n\n\nAbstract\nDNA is tightly p
ackaged around histone proteins in order to increase its density inside ce
lls\, and a potential mechanism for DNA expression regulation is to contro
l DNA-histone interactions. In this talk I will present recent models of
this behavior\, including a novel ultrasensitive\, noncooperative mechanis
m for DNA packaging\, as well as a collaboration to study time-dependent N
FkB inputs in inflammatory signaling. Both models combine basic analysis
ideas with computational analysis to better understand the qualitative pri
nciples for gene regulation.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Tanveer Syeda-Mahmood (IBM Fellow\, IBM Research)
DTSTART:20210517T150000Z
DTEND:20210517T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/23
DESCRIPTION:Title: Multimodal Fusion Across Scales for Disease Understanding\nby Ta
nveer Syeda-Mahmood (IBM Fellow\, IBM Research) as part of Mathematical an
d Computational Biology Seminar Series\n\n\nAbstract\nIn a complex disease
such as cancer\, the interactions between the tumor and host can exist at
the molecular\, cellular\, tissue\, and organism levels. Thus evidence fo
r the disease and its evolution may be present in multiple modalities acro
ss scale such as clinical\, genomic\, molecular\, pathological and radiolo
gical imaging. Effective patient-tailored therapeutic guidance and plannin
g in the future will require bridging spatiotemporal scales through novel
multimodal fusion formalisms. In this talk\, I will present some of the la
test published work from our team in developing new deep learning algorith
ms for multimodal fusion. Specifically\, I will describe our work on fusin
g data from multiple information sources towards addressing many problems
in cancer and cardiovascular disease understanding.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Wenrui Hao (Pennsylvania State University)
DTSTART:20210531T150000Z
DTEND:20210531T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/24
DESCRIPTION:Title: Computational models of cardiovascular disease\nby Wenrui Hao (P
ennsylvania State University) as part of Mathematical and Computational Bi
ology Seminar Series\n\n\nAbstract\nIn this talk\, I will introduce severa
l computational models of cardiovascular disease\, including atheroscleros
is and aortic aneurysm growth to quantitatively predict long-term cardiova
scular risk. These models integrate both the multi-layered structure of th
e arterial wall and the aneurysm pathophysiology. The heterogeneous multi
scale 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 aortic ane
urysm growth based upon the patients' CT scan data is simulated to validat
e a medically reasonable long-term prediction.\n
LOCATION:https://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:20260422T212556Z
UID:UMassMathBio/25
DESCRIPTION:Title: Inference of crossbridge properties from A.V. Hill's description of
the heat of shortening and force-velocity relation of skeletal muscle\
nby Charles S. Peskin (Courant Institute of Mathematical Sciences New York
University) as part of Mathematical and Computational Biology Seminar Ser
ies\n\n\nAbstract\nWe set up and solve an inverse problem\, in which micro
scopic properties of myosin motors in skeletal muscle are derived from the
macroscopic mechanical and thermal properties of muscle that were\ndiscov
erd by A.V. Hill in 1938. The solution is made unique by imposing a finit
e range condition on crossbridge deformation. Results are in surprisingly
good agreement with 21st-century data.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ami Radunskaya (Pomona College)
DTSTART:20211115T160000Z
DTEND:20211115T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/27
DESCRIPTION:Title: DCs\, Doses and Drugs: mathematical models for tumor treatments over
the past 20 years.\nby Ami Radunskaya (Pomona College) as part of Mat
hematical and Computational Biology Seminar Series\n\n\nAbstract\nIn this
talk I will trace a trajectory of mathematical models used to inform cance
r treatments. The mathematical tools used include systems of differential
equations\, heuristic optimization\, hybrid cellular automata and netwo
rk complexity. This story highlights the power of flexibility and collab
oration\, and illustrates how current mysteries and available data can dri
ve the modeling process.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Paul Macklin (Indiana University)
DTSTART:20211101T150000Z
DTEND:20211101T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/28
DESCRIPTION:Title: Using agent-based models to explore complex multicellular systems\nby Paul Macklin (Indiana University) as part of Mathematical and Comput
ational Biology Seminar Series\n\n\nAbstract\nMulticellular biological sys
tems are driven by the nonlinear interactions of cells in their dynamical
microenvironments. Agent-based models explore these systems by simulating
each cell as a discrete agent with an independent state and behavioral rul
es\, while coupling with partial differential equation models of the chemi
cal microenvironment. Individual agents may also incorporate reaction kine
tics networks\, dynamic flux models\, or Boolean networks to model intrace
llular processes that drive cell behaviors. After introducing cell-based m
odeling\, we will introduce PhysiCell: an open source\, cross-platform age
nt-based modeling systems for multicellular systems biology. We will demon
strate applications in cancer biology\, immunotherapy\, and infectious dis
eases including COVID-19. We will close with a brief look at how methods d
eveloped for our COVID-19 project are now driving new work in cancer immun
ology and cancer patient digital twins. This talk will also present how ag
ent-based modeling\, high performance computing\, and machine learning can
be combined to enhance discovery.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chris Sander (Harvard Medical School)
DTSTART:20211018T150000Z
DTEND:20211018T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/29
DESCRIPTION:Title: Machine learning for hard biological problems - three examples\n
by Chris Sander (Harvard Medical School) as part of Mathematical and Compu
tational Biology Seminar Series\n\n\nAbstract\nExamples are: \n- computati
onal models of cell biological processes from systematic perturbation-resp
onse experiments\n- identifying high risk of pancreatic cancer from real-w
orld clinical records\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chun Liu (Illinois Institute of Technology)
DTSTART:20211004T150000Z
DTEND:20211004T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/30
DESCRIPTION:Title: Energetic Variational Approaches (EnVarA) for Active Materials and R
eactive Fluids\nby Chun Liu (Illinois Institute of Technology) as part
of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nA
ctive/reactive fluids convert and transduce energy from their surrounding
into a motion and other mechanical activities. These systems are usually o
ut of mechanical or even thermodynamic equilibrium. One can find such exa
mples in almost all biological systems. In this talk I will develop a gene
ral theory for active fluids which convert chemical energy into various ty
pes of mechanical energy. This is the extension of the classical energetic
variational approaches for mechanical systems. The methods will cover a w
ide range of both chemical reaction kenetics and mechanical processes. Thi
s is a joint work with Yiwei Wang.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/30/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ruth Baker (University of Oxford)
DTSTART:20220131T160000Z
DTEND:20220131T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/31
DESCRIPTION:Title: Mathematical and computational challenges in interdisciplinary biosc
ience: efficient approaches for simulating and calibrating stochastic mode
ls of biological processes.\nby Ruth Baker (University of Oxford) as p
art of Mathematical and Computational Biology Seminar Series\n\n\nAbstract
\nSimple mathematical models have had remarkable successes in biology\, fr
aming how we understand a host of mechanisms and processes. However\, with
the advent of a host of new experimental technologies\, the last ten year
s has seen an explosion in the amount and types of data now being generate
d. Increasingly larger and more complicated processes are now being explor
ed\, including large signalling or gene regulatory networks\, and the deve
lopment\, dynamics and disease of entire cells and tissues. As such\, the
mechanistic\, mathematical models developed to interrogate these processes
are also necessarily growing in size and complexity. These detailed model
s have the potential to provide vital insights where data alone cannot\, b
ut to achieve this goal requires meeting significant mathematical challeng
es in efficiently simulating models and calibrating them to experimental d
ata. In this talk\, I will outline some of these challenges\, and recent s
teps we have taken in addressing them.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Raymond Goldstein (University of Cambridge)
DTSTART:20220214T160000Z
DTEND:20220214T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/32
DESCRIPTION:Title: Cytoplasmic Streaming and the Swirling Instability of the Microtubul
e Cytoskeleton\nby Raymond Goldstein (University of Cambridge) as part
of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nC
ytoplasmic streaming is the persistent circulation of the fluid contents o
f large eukaryotic cells\, driven by the action of molecular motors moving
along cytoskeletal filaments\, entraining fluid. Discovered in 1774 by Bo
naventura Corti\, it is now recognized as a common phenomenon in a very b
road range of model organisms\, from plants to flies and worms. This talk
will discuss physical approaches to understanding this phenomenon through
a combination of experiments (on aquatic \nplants\, Drosophila\, and other
active matter systems)\, theory\, and computation. A particular focus wi
ll be on streaming in the Drosophila oocyte\, for which I will describe a
recently discovered “swirling instability” of the microtubule cytoskel
eton.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Belinda Akpa (Department of Energy)
DTSTART:20211129T160000Z
DTEND:20211129T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/33
DESCRIPTION:Title: Bridging the gaps: Multiscale modeling in 'tiny data' biology\nb
y Belinda Akpa (Department of Energy) as part of Mathematical and Computat
ional Biology Seminar Series\n\n\nAbstract\nAt a time when many are wrangl
ing with biological 'big data'\, there remain important problems that are
fundamentally data limited – often physiological questions for which the
re is little quantitative data\, and further data collection may be hamper
ed by limited resources\, ethical constraints\, or simply a lack of clarit
y as to which measurements are most likely to shed light on mechanisms of
interest. Mathematical modeling can make impactful contributions in these
contexts by maximizing the value of the existing biological literature and
operationalizing data from disparate studies to build quantitative models
. In this presentation\, I will describe how multiscale mathematical model
s can be built using 'tiny data'.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael Reed (Duke University)
DTSTART:20211213T160000Z
DTEND:20211213T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/34
DESCRIPTION:Title: Serotonin\, Histamine\, and Depression\nby Michael Reed (Duke Un
iversity) as part of Mathematical and Computational Biology Seminar Series
\n\n\nAbstract\nA long-term collaboration between Parry Hashemi\, an elect
rochemist (Imperial College)\, H. Fredrik Nijhout\, a biologist at Duke\,
Janet Best\, a mathematician at Ohio State and\nthe speaker will be descri
bed. Hashemi can measure the time courses of serotonin and histamine (in v
ivo in mouse) in the extracellular space in the brain after stimulation of
serotonin and histamine neurons. The modelers have helped Hashemi interpr
et her data and the data has shown where the models are right or wrong. Ne
w results on autoreceptors and serotonin reuptake transporters will be des
cribed. Recent work on the interaction between histamine and serotonin hav
e led to a new hypothesis on the causative mechanisms of depression and ha
s explained why select serotonin reuptake inhibitors have proven to be not
oriously unreliable therapeutic agents for Depression.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anita Layton (University of Toronto)
DTSTART:20220328T150000Z
DTEND:20220328T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/36
DESCRIPTION:Title: His or Her Mathematical Models --- Modeling Kidney Physiology and Be
yond\nby Anita Layton (University of Toronto) as part of Mathematical
and Computational Biology Seminar Series\n\n\nAbstract\nImagine someone ha
ving a heart attack. Do you visualize the dramatic Hollywood portrayal of
a heart attack\, in which a man collapses\, grabbing his chest in agony? E
ven though heart disease is the leading killer of women worldwide\, the mi
sconception that heart disease is a men’s disease has persisted. A dange
rous misconceptions and risks women ignoring their own symptoms. Gender bi
ases and false impressions are by no means limited to heart attack symptom
s. Such prejudices exist throughout our healthcare system\, from scientifi
c research to disease diagnosis and treatment strategies. A goal of our re
search program is to address this gender equity\, by identifying and disse
minating insights into sex differences in health and disease\, using compu
tational modeling tools.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adriana Dawes (The Ohio State University)
DTSTART:20220314T150000Z
DTEND:20220314T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/37
DESCRIPTION:Title: Experimental and mathematical approaches to investigate dynein local
ization and pronuclear movement in the early C. elegans embryo\nby Adr
iana Dawes (The Ohio State University) as part of Mathematical and Computa
tional Biology Seminar Series\n\n\nAbstract\nAsymmetric cell division\, wh
ere daughter cells inherit unequal amounts of specific factors\, is critic
al for development and cell fate specification. In polarized cells\, where
specific factors are segregated to opposite ends of the cell\, asymmetric
cell division occurs as a result of dynein-mediated centrosome positionin
g along the polarity axis. Early embryos of the nematode worm C. elegans p
olarize in response to fertilization and rely on proper centrosome positio
ning for cell fate specification and development. Depletion of certain pro
teins results in defective movement of centrosomes and the associated pron
uclear complex. We developed a novel measure to characterize and quantify
the oscillatory nature of these movement defects\, revealing a common mov
ement defect induced by the loss of seemingly unrelated proteins. We furth
er demonstrated in vivo that dynein localization is not impaired in the pr
esence of this oscillatory movement\, suggesting that the proteins identif
ied by our measure play a role in regulating dynein activity. Current work
integrates mathematical modeling with quantitative imaging of the centros
ome and pronuclear complex movement to identify the signaling networks and
physical mechanisms responsible for the impaired movement.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sunčica Čanić (University of California\, Berkeley)
DTSTART:20220411T150000Z
DTEND:20220411T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/38
DESCRIPTION:Title: Mathematical and computational modeling of a bioartificial pancreas<
/a>\nby Sunčica Čanić (University of California\, Berkeley) as part of
Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nThe w
ork reported here has been motivated by the design of lab-grown organs\, s
uch as a bioartificial pancreas. The design of lab-grown organs relies on
using biocompatible materials\, typically poroelastic hydrogels\, to gener
ate scaffolds to support seeded cells of different organs. Additionally\,
to prevent the patient's own immune cells from attacking the transplanted
organ\, the hydrogel containing seeded cells is encapsulated between two
semi-permeable\, nano-pore size membranes/plates and connected to the pati
ent's vascular system via a tube (anastomosis graft). The semi-permeable m
embranes are designed to prevent the patient's own immune cells from attac
king the transplant\, while permitting oxygen and nutrients carrying blood
plasma (Newtonian fluid) to reach the cells for long-term cell viability.
A key challenge is to design a hydrogel with ``roadways'' for blood plas
ma to carry oxygen and nutrients to the transplanted cells. \nWe present a
complex\, multi-scale model\, and a first well-posedness result in the ar
ea of fluid-poroelastic structure interaction (FPSI) with multi-layered st
ructures 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 multi-laye
red poroelastic medium consisting of a thin poroelastic plate and a thick
poroelastic medium modeled by a Biot model. Numerical simulations of the u
nderlying problem showing optimal design of a bioartificial pancreas\, wil
l be presented. This is a joint work with bioengineer Shuvo Roy (UCSF)\, a
nd mathematicians Yifan Wang (UCI)\, Lorena Bociu (NCSU)\, Boris Muha (Uni
versity of Zagreb)\, and Justin Webster (University of Maryland\, Baltimor
e County).\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Omar Saucedo (Virginia Tech University)
DTSTART:20220228T160000Z
DTEND:20220228T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/39
DESCRIPTION:Title: Host movement\, transmission hot spots\, and vector-borne disease dy
namics on spatial networks\nby Omar Saucedo (Virginia Tech University)
as part of Mathematical and Computational Biology Seminar Series\n\n\nAbs
tract\nHuman movement plays a key part on how a disease can propagate thro
ugh a population as it enables a pathogen to invade a new environment and
helps the persistence of a disease in locations that would otherwise be is
olated. In this talk\, we explore how spatial heterogeneity combines with
mobility network structure to influence vector-borne disease dynamics. We
derive an approximation for the domain reproduction number for a n-patch
SIS-SI Ross-Macdonald model using a Laurent series expansion. Furthermore\
, we analyze the sensitivity equations with respect to the domain reproduc
tion number to determine which parameters should be targeted for intervent
ion strategies. To observe how these analytical results can be implemente
d in practice\, we conclude with a case study.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Helen Moore (University of Florida)
DTSTART:20220425T150000Z
DTEND:20220425T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/40
DESCRIPTION:Title: Systems Pharmacology Models in Drug Development\nby Helen Moore
(University of Florida) as part of Mathematical and Computational Biology
Seminar Series\n\n\nAbstract\nA wide variety of mathematical methods are u
sed to aid the drug development process. One example is the use of quantit
ative systems pharmacology (QSP) models. A QSP model is a mathematical\, m
echanistic representation of a patient’s disease and therapy dynamics. Q
SP models are typically systems of ordinary differential equations with a
dozen or more nonlinear equations\, and many more parameters. Although QSP
models have been used to save substantial time and money in drug developm
ent\, their use is not as widespread as might be expected from these benef
its. Lack of buy-in from stakeholders is a major hurdle to adoption and ca
n\, in part\, be attributed to lack of confidence in QSP models and their
predictions. In this talk\, I will make the case that standardization of s
ystems model evaluation methods\, either within the biotechnology/pharmace
utical (biopharma) community or more broadly\, would support more extensiv
e use of QSP models\, and would reduce the resources needed for drug devel
opment. Proposed model evaluation methods include sensitivity and identifi
ability analysis\, uncertainty quantification\, comparison to data\, and e
xternal review. I will share examples of evaluation methods that are being
applied to QSP models. I will also discuss how model credibility can supp
ort the use of optimal control and mathematical optimization of combinatio
n drug regimens. \n\nBraakman S\, Pathmanathan P\, Moore H. Evaluation fra
mework for systems models. CPT Pharmacometrics Syst Pharmacol. 2022\; 11:
264- 289. https://doi.org/10.1002/psp4.12755\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joel Brown
DTSTART:20220926T150000Z
DTEND:20220926T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/41
DESCRIPTION:Title: Using evolutionary game theory to treat cancer\nby Joel Brown as
part of Mathematical and Computational Biology Seminar Series\n\n\nAbstra
ct\n“You have cancer.” What unfortunate words. To the patient\, fami
ly and friends cancer brings a maelstrom of emotions including fear and ho
pe. It can be a horrific disease of genetic mutations and unregulated pro
liferation. But\, cancer is much more\, and knowing this can empower the
patient and suggest new therapies. Cancer cells inhabit a tumor ecosystem
where they experience much the same hazards and opportunities present in t
he ecology of any creature. Furthermore\, like nature\, they evolve adapt
ations to better acquire resources\, avoid the hazards of the immune syste
m\, and occupy new spaces and organs of the patient. The failure of thera
py happens when cancer cells evolve resistance. Evolutionary game theory i
s eminently suited for modelling cancer’s eco-evolutionary dynamics. As
a game\, cancer cells are the players\, their genetically and epigenetica
lly heritable traits are their strategies\, proliferation and survival are
their payoffs\, and the tumor microenvironment sets the rules. With ther
apy\, the physician becomes an additional player in this game. Understand
ing the game that goes on between treatment strategies and the cancer cell
s offers new insights and hope. Such therapies aim to use drugs more spar
ingly and judiciously. We can and should anticipate and steer the cancer
cells’ evolution. In this way\, otherwise incurable cancers may be mana
ged as a livable\, chronic disease\, or better yet cured by beating cancer
at its own ecological and evolutionary “chess” game. Here I will: 1)
model cancer as an evolutionary game\, 2) model cancer therapy as a leade
r-follower game\, and 3) present a game theory model and clinical trial of
adaptive therapy for men with incurable metastatic prostate cancer.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Vasileios Maroulas (University of Tennessee)
DTSTART:20221128T160000Z
DTEND:20221128T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/42
DESCRIPTION:Title: Grid Cell Navigation via Simplicial Convolutional Recurrent Neural N
etwork\nby Vasileios Maroulas (University of Tennessee) as part of Mat
hematical and Computational Biology Seminar Series\n\n\nAbstract\nImprovin
g flexibility and adaptability of next generation AI is possible by design
ing networks that generate abstract spatial representations in the same wa
y that mammals do. Complex spatial representation patterns\, as recorded
by neuroscience data\, may be uncovered through the discovery of their und
erlying manifolds. Such manifolds may be represented by a rich in informat
ion simplicial complex. Simplicial complexes form an important class of to
pological spaces that are frequently employed in various application areas
from materials science and chemistry to biology and neuroscience\, etc. f
or addressing supervised and unsupervised learning. In this talk\, we will
discuss our recent simplicial convolutional recurrent neural network (SCR
NN) and its application to automated navigation using grid cell data.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/42/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jude Kong (York University)
DTSTART:20221031T150000Z
DTEND:20221031T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/43
DESCRIPTION:Title: Leveraging AI for Clinical Public Health in the Global South\nby
Jude Kong (York University) as part of Mathematical and Computational Bio
logy Seminar Series\n\n\nAbstract\nDisease outbreaks are increasing both i
n terms of severity and frequency. Climate change is exacerbating existing
health and social inequities by increasing the vulnerability of climate
“hotspots” to the emergence and re-emergence of many infectious diseas
es such as malaria\, dengue fever and zika. Moreover\, a growing number of
these diseases are spread from animals to people\, due to factors such as
growing human encroachment into natural landscapes. Responding to the com
plex nature of these interactions in a timely way requires the ability to
analyze large data sets across multiple sectors. Artificial intelligence s
olutions and data science approaches are increasingly being used across th
e globe to identify risks\, conduct predictive modeling and provide eviden
ce-based recommendations for public health policy and action. My research
program represents a small step in this direction. In this talk\, I will p
rovide a comprehensive overview of the potential roles and applications of
AI in clinical public health in Africa. As a case study\, I will focus o
n the work that we have been doing in Africa.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/43/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Amber Smith (University of Tennessee Health Science Center)
DTSTART:20230227T160000Z
DTEND:20230227T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/44
DESCRIPTION:Title: Modeling the Complexity of Viral and Bacterial Coinfections with Inf
luenza\nby Amber Smith (University of Tennessee Health Science Center)
as part of Mathematical and Computational Biology Seminar Series\n\n\nAbs
tract\nSecondary viral and bacterial pathogens exacerbate influenza to cau
se significant morbidity and mortality. However\, the outcome is dependent
on the order and timing of each pathogen\, where protection from influenz
a is observed in some scenarios. While experimental methods can be used to
identify mechanisms of multi-pathogen infections\, mathematical models pr
ovide a unique lens to determine their contribution to susceptibility and
pathogenicity and define hidden mechanisms. I’ll discuss an integrative
model-experiment exchange that we used to disentangle pathogen-specific ef
fects on host immunity\, dissemination within the lung\, and disease sever
ity during bacterial or viral coinfections during influenza.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/44/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bard Ermentrout (Pittsburgh University)
DTSTART:20230410T150000Z
DTEND:20230410T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/45
DESCRIPTION:Title: Follow your Nose: The Dynamics of Olfactory Guided Search\nby Ba
rd Ermentrout (Pittsburgh University) as part of Mathematical and Computat
ional Biology Seminar Series\n\n\nAbstract\nOlfaction (the sense of smell)
is the oldest of our sensory modalities and has been used for millions of
years for animals to find mates\, find food\, avoid predators\, etc. In a
large multi-investigator collaboration\, we have begun to try to understa
nd the algorithms animals use to navigate complex odor landscapes. I will
describe several simple algorithms that use local spatial and temporal inf
ormation about the odor to locate its source. The algorithms fall into two
simple categories: differences between two sensors and differences betwee
n two different samples. With data from trail-following and spot finding
by mice\, I attempt to assess the different strategies and how parameters
in the strategies affect performance. I also test the algorithms on odor
plumes imaged by my collaborators and also in a mobile robot. \n\nUnderlyi
ng these simple algorithms are some interesting nonlinear dynamics. I will
discuss the continuous dynamics of binaral search where the organism uses
the concentration differences between two sensors to steer toward the sou
rce. Depending on the odor environment\, various types of complex dynamics
emerge including stable fixed points\, periodic orbits\, torii\, and chao
s.\n\nI will show the role of “noise” on improving the algorithms and
how it can be leveraged as a search strategy by exploring a first passage
time problem applied to spot finding. I will also show a kind of stochasti
c resonance can occur in real odor plumes.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/45/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Liu (Dana Farber Cancer Institute)
DTSTART:20230501T150000Z
DTEND:20230501T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/46
DESCRIPTION:Title: Computational approaches to enable precision medicine in cancer pati
ents\nby David Liu (Dana Farber Cancer Institute) as part of Mathemati
cal and Computational Biology Seminar Series\n\n\nAbstract\nThere is a bur
geoning amount of high-dimensional molecular data generated from patient c
linical samples\, including genomics\, transcriptional profiles\, spatial
imaging at bulk and single cell resolution. We illustrate some of the chal
lenges\, opportunities\, and ongoing work in developing and adapting compu
tational approaches to elucidate drivers of cancer therapy response\, prog
ression\, and metastasis towards developing biomarkers of therapy response
and novel therapeutic targets.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/46/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Paul K. Newton (University of Southern California)
DTSTART:20231016T150000Z
DTEND:20231016T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/47
DESCRIPTION:Title: Three problems in mathematical oncology\nby Paul K. Newton (Univ
ersity of Southern California) as part of Mathematical and Computational B
iology Seminar Series\n\n\nAbstract\nI will introduce three problems in ma
thematical oncology which involve dynamics\, forecasting\, longitudinal da
ta\, and control theory. In the first problem\, I will describe our work u
sing Markov chain models to forecast metastatic progression in 12 differen
t soft tissue cancers. The models treat progression as a (weighted) random
walk on a directed graph whose nodes are metastatic tumor locations. We e
stimate transition probabilities from site-to-site using historical autops
y data (untreated progression) and longitudinal patient data (treated prog
ression) from Memorial Sloan Kettering and MD Anderson Cancer Centers. We
characterize the inherent predictability of each cancer type using entropy
methods. In the second problem\, I will describe models (both determinist
ic and stochastic) that use evolutionary game theory (replicator dynamical
systems with frequency dependent selection) to design novel adaptive chem
otherapy schedules that mitigate chemoresistance by suppressing the ‘com
petitive release’ of resistant cells. The models make use of finding clo
sed evolutionary cycles in the frequency distribution of competing subpopu
lations of cells so that neither the resistant population nor the sensitiv
e population ever reach fixation. The third problem will describe our mode
l of Covid-19 vaccine uptake as a reinforcement learning dynamic between t
wo populations: the vaccine adopters\, and the vaccine hesitant. We use up
take data from the Center for Disease Control (CDC) to estimate the payoff
matrix governing the interaction between these two groups over time and s
how they are playing a Hawk-Dove evolutionary game with an internal evolut
ionarily stable Nash equilibrium. We then use the model\, along with optim
al control theory\, to test several hypotheses associated with the size an
d timing of incentive programs to improve vaccine uptake (shift the Nash e
quilibrium upward) as much as possible. The model shows diminishing return
s for larger incentive sizes.\n
LOCATION:https://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:20260422T212556Z
UID:UMassMathBio/48
DESCRIPTION:Title: Rummagene: Mining Gene Sets from Supporting Materials of PMC Publica
tions\nby Avi Ma’ayan (Icahn School of Medicine at Mount Sinai) as p
art of Mathematical and Computational Biology Seminar Series\n\n\nAbstract
\nEvery week thousands of biomedical research papers are published with a
portion of them containing supporting tables with data about genes\, trans
cripts\, variants\, and proteins. For example\, supporting tables may cont
ain differentially expressed genes and proteins from transcriptomics and p
roteomics assays\, targets of transcription factors from ChIP-seq experime
nts\, 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://rummage
ne.com\, is a web server application that provides access to hundreds of t
housands of human and mouse gene sets extracted from supporting materials
of publications listed on PubMed Central (PMC). To create Rummagene\, we f
irst developed a softbot that extracts human and mouse gene sets from supp
orting 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 se
ts. These gene sets are served for enrichment analysis\, free text\, and t
able title search. Users of Rummagene can submit their own gene sets to fi
nd matching gene sets ranked by their overlap with the input gene set. In
addition to providing the extracted gene sets for search\, we investigated
the massive corpus of these gene sets for statistical patterns. We show t
hat the number of gene sets reported in publications is rapidly increasing
\, containing both short sets that are highly enriched in highly studied g
enes\, and long sets from omics profiling. We also demonstrate that the ge
ne sets in Rummagene can be used for transcription factor and kinase enric
hment analyses\, and for gene function predictions. By combining gene set
similarity with abstract similarity\, Rummagene can be used to find surpri
sing relationships between unexpected biological processes\, concepts\, an
d named entities. Finally\, by overlaying the Rummagene gene set space wit
h the Enrichr gene set space we can discover areas of biological and biome
dical knowledge unique to each resource.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/48/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Daniel A. Cruz (University of Florida)
DTSTART:20230925T150000Z
DTEND:20230925T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/49
DESCRIPTION:Title: Topological data analysis of pattern formation in stem cell colonies
\nby Daniel A. Cruz (University of Florida) as part of Mathematical an
d Computational Biology Seminar Series\n\n\nAbstract\nConfocal microscopy
imaging provides valuable information about the current expression states
within in vitro cell cultures. However\, few tools exist to quantify the s
patial organization of the cells observed in these images. We present a mo
dular\, general-purpose pipeline that extracts cell-specific signal intens
ities from confocal microscopy images. The pipeline then assigns cell type
s based on corresponding intensities and quantifies spatial information am
ong cell types through topological data analysis (TDA). We provide an over
view of TDA and discuss biological insights which we may gain from applyin
g our pipeline to microscopy images. In particular\, we focus on studying
the pattern formation of human induced pluripotent stem cell (hiPSC) cultu
res\, which have become powerful\, patient-specific test beds for investig
ating the early stages of embryonic development. By applying our pipeline
to images of hiPSC colonies\, we are able to detect and quantify changes i
n pattern formation caused by cell-to-cell signaling and differentiation.
We also contextualize our pipeline within a larger effort toward developin
g quantitative tools for evaluating spatial models.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/49/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Guowei Wei (Michigan State University)
DTSTART:20240212T160000Z
DTEND:20240212T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/50
DESCRIPTION:Title: Mathematics in action: from pandemic to drug discovery\nby Guowe
i Wei (Michigan State University) as part of Mathematical and Computationa
l Biology Seminar Series\n\n\nAbstract\nMathematics underpins fundamental
theories in physics such as quantum mechanics\, general relativity\, and q
uantum field theory. Nonetheless\, its success in modern biology\, namely
cellular biology\, molecular biology\, chemical biology\, genomics\, and g
enetics\, has been quite limited. Artificial intelligence (AI) has fundame
ntally changed the landscape of science\, engineering\, and technology in
the past decade and holds a great future for discovering the rules of life
. However\, AI-based biological discovery encounters challenges arising fr
om the intricate complexity\, high dimensionality\, nonlinearity\, and mul
tiscale biological systems. We tackle these challenges by a mathematical A
I paradigm. We have introduced persistent cohomology\, persistent spectral
graphs\, persistent path Laplacians\, persistent sheaf Laplacians\, and e
volutionary de Rham-Hodge theory to significantly enhance AI's ability to
tackle biological challenges. Using our mathematical AI approaches\, my te
am has been the top winner in D3R Grand Challenges\, a worldwide annual co
mpetition series in computer-aided drug design and discovery for years. By
further integrating mathematical AI with millions of genomes isolated fro
m patients\, we discovered the mechanisms of SARS-CoV-2 evolution and accu
rately forecast emerging dominant SARS-CoV-2 variants months in advance.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/50/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Joceline Lega (University of Arizona)
DTSTART:20240415T150000Z
DTEND:20240415T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/51
DESCRIPTION:Title: Epidemics from the Eye of the Pathogen\nby Joceline Lega (Univer
sity of Arizona) as part of Mathematical and Computational Biology Seminar
Series\n\n\nAbstract\nWhen plotted in what I call ICC (Incidence versus C
umulative Cases) coordinates\, noisy disease data appear to fluctuate abou
t a mean curve with generic properties. In this talk\, I will recount the
discovery of such universality [1] and describe recent work aimed at eluci
dating this behavior [2\, 3]. In particular\, exact results will be provid
ed for the deterministic and stochastic SIR models. In addition\, I will e
xplain how identifying trends in the ICC plane can lead to short-term fore
casts 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 outbreak fo
recasting with a simple nonlinear growth model\, Epidemics 17\, 19–26 (2
016).\n\n[2] J. Lega\, Parameter estimation from ICC curves\, Journal of B
iological Dynamics 15\, 195-212 (2021).\n\n[3] F.D. Sahneh\, W. Fries\, J.
C. Watkins\, J. Lega\, Epidemics from the Eye of the Pathogen\, SIAM J. Ap
pl. Math. 82\, 2036-2056 (2022).\n\n[4] H. Biegel & J. Lega\, EpiCovDA: a
mechanistic COVID-19 forecasting model with data assimilation\, https://ar
xiv.org/abs/2105.05471 (2021).\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/51/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Zoi Rapti (University of Illinois at Urbana-Champaign)
DTSTART:20240513T150000Z
DTEND:20240513T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/52
DESCRIPTION:Title: Generalized Lotka-Volterra equations on graphs\nby Zoi Rapti (Un
iversity of Illinois at Urbana-Champaign) as part of Mathematical and Comp
utational Biology Seminar Series\n\n\nAbstract\nWe investigate the stabili
ty 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 nonzero\, na
mely all species persist. Our analytical findings are corroborated by nume
rical simulations and supplement published studies. We give a short proof
of the result that tree networks with amensalistic\, commensalistic and a
ntagonistic interactions are stable regardless of the interaction strength
\, while tree networks with amensalistic\, commensalistic\, mutualistic an
d competitive interactions can be made unstable by choosing any of the int
eraction strengths large enough. We also present findings on the types of
networks and interactions that are characterized by the largest real and i
maginary parts of the eigenvalues of their corresponding Jacobian matrices
. This is joint work with Lee DeVille and Shinhae Park.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/52/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Arman Rahmim (UBC Cancer Center)
DTSTART:20241021T150000Z
DTEND:20241021T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/53
DESCRIPTION:Title: Physiologically based Pharmacokinetic (PBPK) Modeling towards Creati
on of Theranostic Digital Twins for Cancer Patients\nby Arman Rahmim (
UBC Cancer Center) as part of Mathematical and Computational Biology Semin
ar Series\n\n\nAbstract\nIn this talk\, we emphasize that patient data\, i
ncluding images\, are not operable (clinically)\, but that digital twins a
re. Based on the former\, the latter can be created. Subsequently\, virtua
l clinical operations can be performed towards selection of optimal therap
ies. Digital twins are beginning to emerge in the field of medicine. We su
ggest that theranostic digital twins (TDTs) are amongst the most natural a
nd feasible flavors of digitals twins. We elaborate on the importance of T
DTs in a future where ‘one-size-fits-all’ therapeutic schemes will be
transcended\; e.g. in radiopharmaceutical therapies (RPTs). Personalized R
PTs can be deployed\, including optimized intervention parameters. Example
s include optimization of injected radioactivities\, sites of injection\,
injection intervals and profiles\, and combination therapies. Multi-modal
multi-scale images\, combined with other data and aided by artificial inte
lligence (AI) techniques\, can be utilized towards routine digital twinnin
g of our patients\, and will enable improved deliveries of RPTs and overal
l healthcare.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/53/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thomas Yankeelov (University of Texas at Austin)
DTSTART:20250224T160000Z
DTEND:20250224T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/54
DESCRIPTION:Title: Personalizing interventions through mechanism-based digital twins\nby Thomas Yankeelov (University of Texas at Austin) as part of Mathemat
ical and Computational Biology Seminar Series\n\n\nAbstract\nOur lab is fo
cused on developing tumor forecasting methods by integrating advanced imag
ing technologies with mathematical models to predict tumor growth and trea
tment response. In this presentation\, we will focus on how quantitative
magnetic resonance imaging (MRI) data can be employed to calibrate mathema
tical models built on first-order effects related to well-established “h
allmarks” of cancer including proliferation\, migration/invasion\, vascu
lar status\, and drug-related tumor growth inhibition and cell death. In
particular\, we will present some of our recent results on using these mod
els to build personalized digital twins that provide a rigorous\, but prac
tical\, methodology for optimizing therapeutic interventions on a patient-
specific basis.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/54/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adam MacLean (University of Southern California)
DTSTART:20241118T160000Z
DTEND:20241118T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/55
DESCRIPTION:Title: Modeling cell fate dynamics with single-cell genomics data\nby A
dam MacLean (University of Southern California) as part of Mathematical an
d Computational Biology Seminar Series\n\n\nAbstract\nCells make decisions
to enable multicellular life. Cell fate decision-making underlies develop
ment and homeostasis\, and goes awry as we age. Despite great promise\, we
have yet to harness the high-resolution information on cell states and fa
tes that single-cell genomics data offer to understand cell fate decisions
in development and aging. Nor do we know how these fate decisions are con
trolled by gene regulatory networks. I will describe our recent work const
ructing models of cell fate decisions in hematopoietic stem cells and canc
er. These models can be constrained using single-cell genomics data\, lead
ing to discovery of new network interactions that control decisions points
during cell state transitions.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/55/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yangyang Wang (Brandeis University)
DTSTART:20250324T150000Z
DTEND:20250324T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/56
DESCRIPTION:Title: Mixed-mode oscillations and flexible phase-locking in neural oscilla
tors\nby Yangyang Wang (Brandeis University) as part of Mathematical a
nd Computational Biology Seminar Series\n\n\nAbstract\nMixed-mode oscillat
ions (MMOs) are complex oscillatory behaviors of multiple-timescale dynami
cal systems in which there is an alternation of large-amplitude and small-
amplitude oscillations. In two-timescale systems\, MMOs can arise either f
rom a Canard mechanism associated with folded node singularities or a dela
yed Andronov-Hopf bifurcation (DHB) of the fast subsystem. While MMOs in t
wo-timescale systems have been extensively studied\, less is known regardi
ng MMOs emerging in three-timescale systems. In this work\, we examine the
mechanisms of MMOs in three-timescale neural oscillators and explore how
the interplay between Canard and DHB mechanisms can produce more robust MM
Os. Furthermore\, we examine the roles of these dynamics in facilitating f
lexible phase-locking in response to strong periodic inputs in neural osci
llators with applications to speech perception.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/56/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Rachel Kuske (Georgia Institute of Technology)
DTSTART:20250428T150000Z
DTEND:20250428T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/57
DESCRIPTION:Title: Dynamical insights in identifying new data-driven mechanistic microb
ial models\nby Rachel Kuske (Georgia Institute of Technology) as part
of Mathematical and Computational Biology Seminar Series\n\n\nAbstract\nWe
consider some recent model identification tools\, together with complemen
tary computations of dynamical characteristics that can often be necessary
to isolate relevant biological mechanisms based on data. In recent studie
s of microbial dynamics\, specifically community behavior of bacteria and
dynamics under antibiotic treatment\, the available data limits the extens
ive use of these tools. Nevertheless\, we illustrate their utility\, toget
her with critical dynamical features\, in identifying new biologically-re
levant models that allow for heterogeneity and state-dependent features th
at are ubiquitous in the ecology and evolution of microbial dynamics.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/57/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mihalis Kavousanakis (National Technical University of Athens)
DTSTART:20250512T150000Z
DTEND:20250512T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/58
DESCRIPTION:Title: Optimizing cancer treatment schedules: a computational study of comb
ined therapies on vascular tumors\nby Mihalis Kavousanakis (National T
echnical University of Athens) as part of Mathematical and Computational B
iology Seminar Series\n\n\nAbstract\nCancer treatment has significantly ad
vanced with therapies such as surgery\, chemotherapy\, radiotherapy\, immu
notherapy\, and hormonal therapy. However\, monotherapies face limitations
\, making combination therapies a widely adopted strategy in modern oncolo
gy. These combinations enhance efficacy through synergistic effects\, redu
ce resistance development\, lower toxicity\, and broaden treatment applica
bility. To explore and optimize combination treatments\, we adopt a multip
hase continuum modeling approach\, treating tissue as a mixture of interac
ting cellular/fluid phases\, including healthy cells\, cancer cells\, vasc
ulature\, and interstitial fluid. We solve mass and momentum balance equat
ions for phase evolution and reaction-diffusion equations for critical che
mical species like nutrients\, VEGF\, and therapeutic agents. Given the co
mputational complexity of such simulations\, we apply Bayesian optimizatio
n to efficiently identify optimal treatment protocols. Our results demonst
rate that concurrent administration of cytotoxic and anti-VEGF agents lead
s to improved outcomes\, in agreement with clinical data. We further exten
d this framework to optimize triple chemo-radiotherapy regimens\, integrat
ing the rapid cytotoxic effects of radiation therapy alongside the tumor-s
uppressive action of chemotherapy.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/58/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yang Kuang (Arizona State University)
DTSTART:20250915T150000Z
DTEND:20250915T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/59
DESCRIPTION:Title: Promising Bio-marks from Predictive Mathematical Models of Hormone T
reatment for Prostate Cancer\nby Yang Kuang (Arizona State University)
as part of Mathematical and Computational Biology Seminar Series\n\n\nAbs
tract\nProstate cancer is a serious public health concern. The primary obs
tacle to effective long-term\nmanagement for prostate cancer patients is t
he eventual development of treatment resistance.\nProstate specific antige
n (PSA) is the ubiquitous but inaccurate bio-marker used in mathematical\n
and artificial intelligence models of prostate cancer. The growth of prost
ate and cancer cells\nproduces PSA\, but their growth is usually dependent
on androgen. Clinically\, a drug that blocks\nthe production of androgen
is often applied continuously past the point of effectiveness\, thereby\nl
osing future potential treatment combination with other drugs to avoid or
delay resistance. We\npresent models of predicting treatment failure due t
o drug resistance. The models are built on an\nevolutionary interpretation
of Droop cell quota theory. We analyze our proposed methods using\npatien
t PSA and androgen data from a clinical trial of intermittent treatment wi
th androgen\ndeprivation therapy. Our results produce two indicators of tr
eatment failure which can serve as\naccurate and practical bio-markers in
clinical settings. The first indicator\, proposed from the\nevolutionary n
ature of the cancer population\, is calculated using our mathematical mode
l with\na predictive accuracy of 87.3% (sensitivity: 96.1%\, specificity:
65%). The second indicator\,\nconjectured from the implication of the firs
t indicator\, is calculated directly from serum androgen\nand PSA data wit
h a predictive accuracy of 88.7% (sensitivity: 90.2%\, specificity 85%). O
ur results\ndemonstrate the feasibility of using an evolutionary tumor dyn
amics model in combination with\npatient data to serve as digital twin to
aid in the adaptive management of prostate cancer.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/59/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jasmine Foo (University of Minnesota-Twin Cities)
DTSTART:20260323T150000Z
DTEND:20260323T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/60
DESCRIPTION:Title: Dosing in a Complex World: The Tumor Microenvironment as a Modulator
of Therapeutic Response\nby Jasmine Foo (University of Minnesota-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 modeli
ng studies that explore how the TME modulates therapy outcome\, and how th
is impacts dosing strategies. First\, I will discuss chemically self-asse
mbled nanorings\, a novel class of multivalent bispecific T cell engagers\
, which facilitate the recruitment of a patient's T cells to kill tumor ce
lls. Using a model calibrated to in vitro experiments with human epidermoi
d carcinoma cells\, we will explore the key drivers of patient response he
terogeneity\, discuss a dosing threshold that influences response variabi
lity\, and propose a biomarker of therapeutic response. Second\, I will di
scuss drug-induced stromal-mediated resistance in colorectal cancer\, and
examine how the drug-stroma-tumor interaction results in complex dose-resp
onse relationships and their implications for dosing protocols. Together
these studies illustrate how mechanistic modeling can lead to actionable d
osing principles from complex TME interactions.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/60/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Guillermo Lorenzo (University of A Coruña)
DTSTART:20260223T160000Z
DTEND:20260223T170000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/62
DESCRIPTION:Title: Patient-specific forecasting of prostate cancer growth and radiother
apy response using biomechanistic models and hybrid classifiers\nby Gu
illermo Lorenzo (University of A Coruña) as part of Mathematical and Comp
utational Biology Seminar Series\n\n\nAbstract\nThe current clinical proto
cols to manage prostate cancer (PCa) enable the detection and successful t
reatment of these tumors at an early stage. However\, recent studies sugge
st that many PCa patients are being overtreated\, and hence prone to poten
tial treatment side-effects (e.g.\, incontinence\, impotence) that can adv
ersely impact their quality of life without improving longevity. Furthermo
re\, undertreatment of PCa is another important clinical challenge\, as it
may lead to rapid growth of aggressive tumors\, treatment failure\, and r
educed survival. The overtreatment and undertreatment of PCa have the same
origin: the limited individualization and observational nature of the cli
nical management of these tumors. In this talk\, I propose to address thes
e critical\, unresolved issues by using patient-specific forecasts of PCa
growth and treatment response\, along with hybrid classifiers that take bi
omechanistic inputs to predict the occurrence of clinical events of intere
st. I will present the application of this predictive framework in two sce
narios where longitudinal data are collected as part of the standard-of-ca
re management of PCa: active surveillance of lower-risk tumors before prim
ary treatment\, and the post-treatment monitoring of patients after radiot
herapy. For each application\, I will show how a biomechanistic model can
be built\, calibrated\, and validated to obtain personalized predictions o
f tumor growth and therapeutic response. Then\, logistic classifiers will
be trained with biomechanistic model outputs to identify tumors progressin
g towards higher-risk disease during active surveillance or developing a r
ecurrence after radiotherapy. Finally\, although further development and v
alidation over larger cohorts are needed\, I will posit that the technolog
ies presented in this talk can contribute to advance the observational\, p
opulation-based standards in clinical oncology towards a predictive\, pers
onalized paradigm.\n\nREFERENCES\n1) G. Lorenzo\, J.S. Heiselman\, M.A. Li
ss\, M.I. Miga\, H. Gomez\, T.E. Yankeelov\, A. Reali\, T.J.R. Hughes (202
4). A pilot study on patient-specific computational forecasting of prostat
e cancer growth during active surveillance using an imaging-informed biome
chanistic model. Cancer Research Communications\, in press. Preprint avail
able in Arxiv. \nDOI: https://doi.org/10.48550/arXiv.2310.00060\n2) G. Lor
enzo\, N. di Muzio\, C.L. Deantoni\, C. Cozzarini\, A. Fodor\, A. Briganti
\, F. Montorsi\, V.M. Pérez-García\, H. Gomez\, A. Reali (2022). Patient
-specific forecasting of postradiotherapy prostate-specific antigen kineti
cs enables early prediction of biochemical relapse. iScience\, 25(11)\, 10
5430. \nDOI: https://doi.org/10.1016/j.isci.2022.105430\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/62/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yiwei Wang (University of California\, Riverside)
DTSTART:20251020T150000Z
DTEND:20251020T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/63
DESCRIPTION:Title: Energetic Variational Modeling of Active Nematics: A Toner–Tu Mode
l with ATP Hydrolysis\nby Yiwei Wang (University of California\, River
side) as part of Mathematical and Computational Biology Seminar Series\n\n
\nAbstract\nActive biological materials\, such as cytoskeletal filaments a
nd motor proteins\, convert chemical energy into mechanical work through n
onequilibrium processes like ATP hydrolysis. In this talk\, we present a t
hermodynamically consistent energetic variational model that captures this
chemo-mechanical coupling in active nematic systems. Extending the classi
cal Toner–Tu framework\, the model integrates reaction kinetics\, self-a
dvection\, and polarization dynamics within a unified energy–dissipation
structure derived from the first principles. The reaction rate depends ex
plicitly on local mechanical states\, revealing how chemical and mechanica
l feedback jointly regulate pattern formation and active transport. This v
ariational formulation not only preserves consistency with nonequilibrium
thermodynamics but also provides a transparent pathway for modeling energy
transduction and regulation mechanisms in living matter.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/63/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Suzanne Sindi (University of California\, Merced)
DTSTART:20260413T150000Z
DTEND:20260413T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/64
DESCRIPTION:Title: Mathematical Models as Engines of Discovery: Insights from Protein M
isfolding and Coagulation Disorders\nby Suzanne Sindi (University of C
alifornia\, Merced) as part of Mathematical and Computational Biology Semi
nar Series\n\n\nAbstract\nMathematical modeling provides a powerful framew
ork for dissecting complex biological systems\, enabling hypothesis genera
tion and deeper insight into emergent behaviors. In this talk\, I illustra
te this paradigm through two case studies: the self-propagating dynamics o
f protein aggregates and the biochemistry of blood coagulation.\n\nFirst\,
I discuss prions - infectious protein aggregates driving fatal neurodegen
erative diseases in humans and cattle\, while producing benign phenotypes
in yeast. To reconcile discrepancies between in vitro protein dynamics and
in vivo cellular behavior\, we developed a stochastic model integrating p
rion amplification with yeast cell division\, uncovering a previously unre
cognized structural distinction between prion variants and bridging a crit
ical gap in the field.\n\nSecond\, I turn to hemophilia A\, a bleeding dis
order marked by unpredictable clinical variability. Using a mechanistic mo
del of the coagulation cascade\, we constructed a computational synthetic
clinical trial to explore how varying clotting factor and inhibitor concen
trations shape bleeding outcomes\, revealing molecular interactions influe
ncing clotting behavior and identifying promising targets for therapeutic
intervention.\n\nTogether\, these examples illustrate how mathematical mod
els guide us toward innovative solutions for some of biology's most challe
nging questions.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/64/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Zhiliang Xu (University of Notre Dame)
DTSTART:20260504T150000Z
DTEND:20260504T160000Z
DTSTAMP:20260422T212556Z
UID:UMassMathBio/65
DESCRIPTION:Title: ENERGETIC VARIATIONAL NEURAL NETWORK DISCRETIZATIONS OF GRADIENT FLO
WS\nby Zhiliang Xu (University of Notre Dame) as part of Mathematical
and Computational Biology Seminar Series\n\n\nAbstract\nIn this talk\, I w
ill describe structure-preserving neural-network-based numerical schemes t
o solve both L2-gradient flows and generalized diffusions. By using neural
networks as tools for spatial discretization\, we introduce a structure-p
reserving Eulerian algorithm to solve L2-gradient flows and a structure-pr
eserving Lagrangian algorithm to solve generalized diffusions. The Lagrang
ian algorithm for a generalized diffusion evolves the “flow map" which d
etermines the dynamics of the system. This avoids the non-trivial task of
computing the Wasserstein distance between two probability functions. Unli
ke most existing methods that construct numerical discretizations based on
the strong or weak form of the underlying PDE\, our schemes are construct
ed using variational formulations of these PDEs for preserving their varia
tional structures. Instead of directly solving the obtained nonlinear syst
ems after temporal and spatial discretization\, the minimizing movement sc
heme is utilized to evolve the solutions. This guarantees the monotonic de
cay of the energy of the system\, and is crucial for the long-term stabili
ty of numerical computation. I will describe a few numerical experiments t
o demonstrate the accuracy and energy stability of the numerical schemes a
nd some recent developments.\n
LOCATION:https://researchseminars.org/talk/UMassMathBio/65/
END:VEVENT
END:VCALENDAR