%% Papers Published
@article{fds376235,
Author = {Ciocanel, M-V and Ding, L and Mastromatteo, L and Reichheld, S and Cabral, S and Mowry, K and Sandstede, B},
Title = {Parameter Identifiability in PDE Models of Fluorescence
Recovery After Photobleaching.},
Journal = {Bulletin of mathematical biology},
Volume = {86},
Number = {4},
Pages = {36},
Year = {2024},
Month = {March},
url = {http://dx.doi.org/10.1007/s11538-024-01266-4},
Abstract = {Identifying unique parameters for mathematical models
describing biological data can be challenging and often
impossible. Parameter identifiability for partial
differential equations models in cell biology is especially
difficult given that many established in vivo measurements
of protein dynamics average out the spatial dimensions.
Here, we are motivated by recent experiments on the binding
dynamics of the RNA-binding protein PTBP3 in RNP granules of
frog oocytes based on fluorescence recovery after
photobleaching (FRAP) measurements. FRAP is a widely-used
experimental technique for probing protein dynamics in
living cells, and is often modeled using simple
reaction-diffusion models of the protein dynamics. We show
that current methods of structural and practical parameter
identifiability provide limited insights into
identifiability of kinetic parameters for these PDE models
and spatially-averaged FRAP data. We thus propose a pipeline
for assessing parameter identifiability and for learning
parameter combinations based on re-parametrization and
profile likelihoods analysis. We show that this method is
able to recover parameter combinations for synthetic FRAP
datasets and investigate its application to real
experimental data.},
Doi = {10.1007/s11538-024-01266-4},
Key = {fds376235}
}
@article{fds374285,
Author = {Topaz, CM and Ning, S and Ciocanel, MV and Bushway,
S},
Title = {Federal criminal sentencing: race-based disparate impact and
differential treatment in judicial districts},
Journal = {Humanities and Social Sciences Communications},
Volume = {10},
Number = {1},
Year = {2023},
Month = {December},
url = {http://dx.doi.org/10.1057/s41599-023-01879-5},
Abstract = {Race-based inequity in federal criminal sentencing is widely
acknowledged, and yet our understanding of it is far from
complete. Inequity may arise from several sources, including
direct bias of courtroom actors and structural bias that
produces racially disparate impacts. Irrespective of these
sources, inequity may also originate from different loci
within the federal system. We bring together the questions
of the sources and loci of inequity. The purpose of our
study is to quantify race-based disparate impact and
differential treatment at the national level and at the
level of individual federal judicial districts. We analyze
over one-half million sentencing records publicly available
from the United States Sentencing Commission database,
spanning the years 2006 to 2020. At the system-wide level,
Black and Hispanic defendants receive average sentences that
are approximately 19 months longer and 5 months longer,
respectively. Demographic factors and sentencing guideline
elements account for nearly 17 of the 19 months for Black
defendants and all five of the months for Hispanic
defendants, demonstrating the disparate impact of the system
at the national level. At the individual district level,
even after controlling for each district’s unique
demographics and implementation of sentencing factors, 14
districts show significant differences for minoritized
defendants as compared to white ones. These unexplained
differences are evidence of possible differential treatment
by judges, prosecutors, and defense attorneys.},
Doi = {10.1057/s41599-023-01879-5},
Key = {fds374285}
}
@article{fds374286,
Author = {Nelson, AC and Rolls, MM and Ciocanel, M-V and McKinley,
SA},
Title = {Minimal Mechanisms of Microtubule Length Regulation in
Living Cells},
Year = {2023},
Month = {October},
Key = {fds374286}
}
@article{fds374287,
Author = {Ciocanel, M-V and Goldrosen, N and Topaz, C},
Title = {Quantifying Federal Sentence Disparities with Inferred
Sentencing Records},
Journal = {SIAM News Blogs},
Year = {2023},
Month = {September},
Key = {fds374287}
}
@article{fds372207,
Author = {Ciocanel, M-V and Ding, L and Mastromatteo, L and Reichheld, S and Cabral, S and Mowry, K and Sandstede, B},
Title = {Parameter identifiability in PDE models of fluorescence
recovery after photobleaching},
Year = {2023},
Month = {July},
Key = {fds372207}
}
@article{fds374288,
Author = {Benson, J and Bessonov, M and Burke, K and Cassani, S and Ciocanel, M-V and Cooney, DB and Volkening, A},
Title = {How do classroom-turnover times depend on lecture-hall
size?},
Journal = {Mathematical biosciences and engineering :
MBE},
Volume = {20},
Number = {5},
Pages = {9179-9207},
Year = {2023},
Month = {March},
url = {http://dx.doi.org/10.3934/mbe.2023403},
Abstract = {Academic spaces in colleges and universities span classrooms
for 10 students to lecture halls that hold over 600 people.
During the break between consecutive classes, students from
the first class must leave and the new class must find their
desks, regardless of whether the room holds 10 or 600
people. Here we address the question of how the size of
large lecture halls affects classroom-turnover times,
focusing on non-emergency settings. By adapting the
established social-force model, we treat students as
individuals who interact and move through classrooms to
reach their destinations. We find that social interactions
and the separation time between consecutive classes strongly
influence how long it takes entering students to reach their
desks, and that these effects are more pronounced in larger
lecture halls. While the median time that individual
students must travel increases with decreased separation
time, we find that shorter separation times lead to shorter
classroom-turnover times overall. This suggests that the
effects of scheduling gaps and lecture-hall size on
classroom dynamics depends on the perspective-individual
student or whole class-that one chooses to
take.},
Doi = {10.3934/mbe.2023403},
Key = {fds374288}
}
@article{fds360122,
Author = {Smith, CM and Goldrosen, N and Ciocanel, M-V and Santorella, R and Topaz, CM and Sen, S},
Title = {Racial Disparities in Criminal Sentencing Vary Considerably
across Federal Judges},
Journal = {Journal of Institutional and Theoretical
Economics},
Volume = {179},
Publisher = {Mohr Siebeck},
Year = {2023},
Month = {January},
url = {http://dx.doi.org/10.1628/jite-2023-0005},
Doi = {10.1628/jite-2023-0005},
Key = {fds360122}
}
@article{fds368023,
Author = {Dawson, M and Dudley, C and Omoma, S and Tung, H-R and Ciocanel,
M-V},
Title = {Characterizing emerging features in cell dynamics using
topological data analysis methods.},
Journal = {Mathematical biosciences and engineering :
MBE},
Volume = {20},
Number = {2},
Pages = {3023-3046},
Publisher = {American Institute of Mathematical Sciences
(AIMS)},
Year = {2023},
Month = {January},
url = {http://dx.doi.org/10.3934/mbe.2023143},
Abstract = {Filament-motor interactions inside cells play essential
roles in many developmental as well as other biological
processes. For instance, actin-myosin interactions drive the
emergence or closure of ring channel structures during wound
healing or dorsal closure. These dynamic protein
interactions and the resulting protein organization lead to
rich time-series data generated by using fluorescence
imaging experiments or by simulating realistic stochastic
models. We propose methods based on topological data
analysis to track topological features through time in cell
biology data consisting of point clouds or binary images.
The framework proposed here is based on computing the
persistent homology of the data at each time point and on
connecting topological features through time using
established distance metrics between topological summaries.
The methods retain aspects of monomer identity when
analyzing significant features in filamentous structure
data, and capture the overall closure dynamics when
assessing the organization of multiple ring structures
through time. Using applications of these techniques to
experimental data, we show that the proposed methods can
describe features of the emergent dynamics and
quantitatively distinguish between control and perturbation
experiments.},
Doi = {10.3934/mbe.2023143},
Key = {fds368023}
}
@article{fds364208,
Author = {Ciocanel, M-V},
Title = {Applications of PDEs and Stochastic Modeling to Protein
Transport in Cell Biology},
Journal = {Notices of the American Mathematical Society},
Publisher = {American Mathematical Society},
Year = {2022},
Month = {December},
url = {http://dx.doi.org/10.1090/noti2577},
Abstract = {Intracellular transport processes are essential to the
healthy development of many organisms as well as more
generally to healthy cellular function. The complex dynamics
and interactions between protein molecules and filaments on
different time and spatial scales generate many
opportunities for mathematical modeling and analysis that
can provide insights into protein sorting and overall
cellular organization. Systems of advection-reaction-diffusion
partial differential equations and stochastic
state-switching particle models are especially useful in
understanding spatiotemporal protein dynamics inside
cells.},
Doi = {10.1090/noti2577},
Key = {fds364208}
}
@article{fds367914,
Author = {Ciocanel, M-V and Nardini, J},
Title = {Online and In-Person Interviewing for Tenure-Track
Positions},
Journal = {Notices of the American Mathematical Society},
Year = {2022},
Month = {August},
url = {http://dx.doi.org/10.1090/noti2516},
Abstract = {Early Career Collection},
Doi = {10.1090/noti2516},
Key = {fds367914}
}
@article{fds362816,
Author = {Ciocanel, M-V and Chandrasekaran, A and Mager, C and Ni, Q and Papoian,
GA and Dawes, A},
Title = {Simulated actin reorganization mediated by motor
proteins.},
Journal = {PLoS computational biology},
Volume = {18},
Number = {4},
Pages = {e1010026},
Year = {2022},
Month = {April},
url = {http://dx.doi.org/10.1371/journal.pcbi.1010026},
Abstract = {Cortical actin networks are highly dynamic and play critical
roles in shaping the mechanical properties of cells. The
actin cytoskeleton undergoes significant reorganization in
many different contexts, including during directed cell
migration and over the course of the cell cycle, when
cortical actin can transition between different
configurations such as open patched meshworks, homogeneous
distributions, and aligned bundles. Several types of myosin
motor proteins, characterized by different kinetic
parameters, have been involved in this reorganization of
actin filaments. Given the limitations in studying the
interactions of actin with myosin in vivo, we propose
stochastic agent-based models and develop a set of data
analysis measures to assess how myosin motor proteins
mediate various actin organizations. In particular, we
identify individual motor parameters, such as motor binding
rate and step size, that generate actin networks with
different levels of contractility and different patterns of
myosin motor localization, which have previously been
observed experimentally. In simulations where two motor
populations with distinct kinetic parameters interact with
the same actin network, we find that motors may act in a
complementary way, by tuning the actin network organization,
or in an antagonistic way, where one motor emerges as
dominant. This modeling and data analysis framework also
uncovers parameter regimes where spatial segregation between
motor populations is achieved. By allowing for changes in
kinetic rates during the actin-myosin dynamic simulations,
our work suggests that certain actin-myosin organizations
may require additional regulation beyond mediation by motor
proteins in order to reconfigure the cytoskeleton network on
experimentally-observed timescales.},
Doi = {10.1371/journal.pcbi.1010026},
Key = {fds362816}
}
@article{fds361354,
Author = {Ciocanel, M-V and Chandrasekaran, A and Mager, C and Ni, Q and Papoian,
G and Dawes, A},
Title = {Actin reorganization throughout the cell cycle mediated by
motor proteins},
Year = {2021},
Month = {July},
Abstract = {Cortical actin networks are highly dynamic and play critical
roles in shaping the mechanical properties of cells. The
actin cytoskeleton undergoes significant reorganization over
the course of the cell cycle, when cortical actin
transitions between open patched meshworks, homogeneous
distributions, and aligned bundles. Several types of myosin
motor proteins, characterized by different kinetic
parameters, have been involved in this reorganization of
actin filaments. Given the limitations in studying the
interactions of actin with myosin in vivo, we propose
stochastic agent-based model simulations and develop a set
of data analysis measures to assess how myosin motor
proteins mediate various actin organizations. In particular,
we identify individual motor parameters, such as motor
binding rate and step size, that generate actin networks
with different levels of contractility and different
patterns of myosin motor localization. In simulations where
two motor populations with distinct kinetic parameters
interact with the same actin network, we find that motors
may act in a complementary way, by tuning the actin network
organization, or in an antagonistic way, where one motor
emerges as dominant. This modeling and data analysis
framework also uncovers parameter regimes where spatial
segregation between motor populations is achieved. By
allowing for changes in kinetic rates during the
actin-myosin dynamic simulations, our work suggests that
certain actin-myosin organizations may require additional
regulation beyond mediation by motor proteins in order to
reconfigure the cytoskeleton network on experimentally-observed
timescales.},
Key = {fds361354}
}
@article{fds360201,
Author = {Ciocanel, M-V and Juenemann, R and Dawes, AT and McKinley,
SA},
Title = {Topological Data Analysis Approaches to Uncovering the
Timing of Ring Structure Onset in Filamentous
Networks},
Journal = {Bulletin of Mathematical Biology},
Volume = {83},
Number = {3},
Publisher = {Springer Science and Business Media LLC},
Year = {2021},
Month = {March},
url = {http://dx.doi.org/10.1007/s11538-020-00847-3},
Abstract = {<jats:title>Abstract</jats:title><jats:p>In developmental
biology as well as in other biological systems, emerging
structure and organization can be captured using time-series
data of protein locations. In analyzing this time-dependent
data, it is a common challenge not only to determine whether
topological features emerge, but also to identify the timing
of their formation. For instance, in most cells, actin
filaments interact with myosin motor proteins and organize
into polymer networks and higher-order structures. Ring
channels are examples of such structures that maintain
constant diameters over time and play key roles in processes
such as cell division, development, and wound healing. Given
the limitations in studying interactions of actin with
myosin in vivo, we generate time-series data of protein
polymer interactions in cells using complex agent-based
models. Since the data has a filamentous structure, we
propose sampling along the actin filaments and analyzing the
topological structure of the resulting point cloud at each
time. Building on existing tools from persistent homology,
we develop a topological data analysis (TDA) method that
assesses effective ring generation in this dynamic data.
This method connects topological features through time in a
path that corresponds to emergence of organization in the
data. In this work, we also propose methods for assessing
whether the topological features of interest are significant
and thus whether they contribute to the formation of an
emerging hole (ring channel) in the simulated protein
interactions. In particular, we use the MEDYAN simulation
platform to show that this technique can distinguish between
the actin cytoskeleton organization resulting from distinct
motor protein binding parameters.</jats:p>},
Doi = {10.1007/s11538-020-00847-3},
Key = {fds360201}
}
@article{fds360121,
Author = {Gandhi, P and Ciocanel, MV and Niklas, K and Dawes,
AT},
Title = {Identification of approximate symmetries in biological
development},
Journal = {Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences},
Volume = {379},
Number = {2213},
Publisher = {The Royal Society},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1098/rsta.2020.0273},
Abstract = {Virtually all forms of life, from single-cell eukaryotes to
complex, highly differentiated multicellular organisms,
exhibit a property referred to as symmetry. However, precise
measures of symmetry are often difficult to formulate and
apply in a meaningful way to biological systems, where
symmetries and asymmetries can be dynamic and transient, or
be visually apparent but not reliably quantifiable using
standard measures from mathematics and physics. Here, we
present and illustrate a novel measure that draws on
concepts from information theory to quantify the degree of
symmetry, enabling the identification of approximate
symmetries that may be present in a pattern or a biological
image. We apply the measure to rotation, reflection and
translation symmetries in patterns produced by a Turing
model, as well as natural objects (algae, flowers and
leaves). This method of symmetry quantification is unbiased
and rigorous, and requires minimal manual processing
compared to alternative measures. The proposed method is
therefore a useful tool for comparison and identification of
symmetries in biological systems, with potential future
applications to symmetries that arise during development, as
observed in vivo or as produced by mathematical models. This
article is part of the theme issue 'Recent progress and open
frontiers in Turing's theory of morphogenesis'.},
Doi = {10.1098/rsta.2020.0273},
Key = {fds360121}
}
@article{fds355297,
Author = {Mallory, K and Rubin Abrams and J and Schwartz, A and Ciocanel, M-V and Volkening, A and Sandstede, B},
Title = {Influenza spread on context-specific networks lifted from
interaction-based diary data.},
Journal = {Royal Society open science},
Volume = {8},
Number = {1},
Pages = {191876},
Publisher = {The Royal Society},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1098/rsos.191876},
Abstract = {Studying the spread of infections is an important tool in
limiting or preventing future outbreaks. A first step in
understanding disease dynamics is constructing networks that
reproduce features of real-world interactions. In this
paper, we generate networks that maintain some features of
the partial interaction networks that were recorded in an
existing diary-based survey at the University of Warwick. To
preserve realistic structure in our artificial networks, we
use a context-specific approach. In particular, we propose
different algorithms for producing larger home, work and
social networks. Our networks are able to maintain much of
the interaction structure in the original diary-based survey
and provide a means of accounting for the interactions of
survey participants with non-participants. Simulating a
discrete susceptible-infected-recovered model on the full
network produces epidemic behaviour which shares
characteristics with previous influenza seasons. Our
approach allows us to explore how disease transmission and
dynamic responses to infection differ depending on
interaction context. We find that, while social interactions
may be the first to be reduced after influenza infection,
limiting work and school encounters may be significantly
more effective in controlling the overall severity of the
epidemic.},
Doi = {10.1098/rsos.191876},
Key = {fds355297}
}
@article{fds353550,
Author = {Ciocanel, M-V and Topaz, CM and Santorella, R and Sen, S and Smith, CM and Hufstetler, A},
Title = {JUSTFAIR: Judicial System Transparency through Federal
Archive Inferred Records.},
Journal = {PloS one},
Volume = {15},
Number = {10},
Pages = {e0241381-e0241381},
Year = {2020},
Month = {October},
url = {http://dx.doi.org/10.1371/journal.pone.0241381},
Abstract = {In the United States, the public has a constitutional right
to access criminal trial proceedings. In practice, it can be
difficult or impossible for the public to exercise this
right. We present JUSTFAIR: Judicial System Transparency
through Federal Archive Inferred Records, a database of
criminal sentencing decisions made in federal district
courts. We have compiled this data set from public sources
including the United States Sentencing Commission, the
Federal Judicial Center, the Public Access to Court
Electronic Records system, and Wikipedia. With nearly
600,000 records from the years 2001-2018, JUSTFAIR is the
first large scale, free, public database that links
information about defendants and their demographic
characteristics with information about their federal crimes,
their sentences, and, crucially, the identity of the
sentencing judge.},
Doi = {10.1371/journal.pone.0241381},
Key = {fds353550}
}
@article{fds353551,
Author = {Ciocanel, M-V and Fricks, J and Kramer, PR and McKinley,
SA},
Title = {Renewal Reward Perspective on Linear Switching Diffusion
Systems in Models of Intracellular Transport.},
Journal = {Bulletin of mathematical biology},
Volume = {82},
Number = {10},
Pages = {126},
Year = {2020},
Month = {September},
url = {http://dx.doi.org/10.1007/s11538-020-00797-w},
Abstract = {In many biological systems, the movement of individual
agents is characterized having multiple qualitatively
distinct behaviors that arise from a variety of biophysical
states. For example, in cells the movement of vesicles,
organelles, and other intracellular cargo is affected by
their binding to and unbinding from cytoskeletal filaments
such as microtubules through molecular motor proteins. A
typical goal of theoretical or numerical analysis of models
of such systems is to investigate effective transport
properties and their dependence on model parameters. While
the effective velocity of particles undergoing switching
diffusion dynamics is often easily characterized in terms of
the long-time fraction of time that particles spend in each
state, the calculation of the effective diffusivity is more
complicated because it cannot be expressed simply in terms
of a statistical average of the particle transport state at
one moment of time. However, it is common that these systems
are regenerative, in the sense that they can be decomposed
into independent cycles marked by returns to a base state.
Using decompositions of this kind, we calculate effective
transport properties by computing the moments of the
dynamics within each cycle and then applying renewal reward
theory. This method provides a useful alternative large-time
analysis to direct homogenization for linear
advection-reaction-diffusion partial differential equation
models. Moreover, it applies to a general class of
semi-Markov processes and certain stochastic differential
equations that arise in models of intracellular transport.
Applications of the proposed renewal reward framework are
illustrated for several case studies such as mRNA transport
in developing oocytes and processive cargo movement by teams
of molecular motor proteins.},
Doi = {10.1007/s11538-020-00797-w},
Key = {fds353551}
}
@article{fds374289,
Author = {Ciocanel, M-V and Jung, P and Brown, A},
Title = {A mechanism for neurofilament transport acceleration through
nodes of Ranvier.},
Journal = {Molecular biology of the cell},
Volume = {31},
Number = {7},
Pages = {640-654},
Year = {2020},
Month = {March},
url = {http://dx.doi.org/10.1091/mbc.e19-09-0509},
Abstract = {Neurofilaments are abundant space-filling cytoskeletal
polymers in axons that are transported along microtubule
tracks. Neurofilament transport is accelerated at nodes of
Ranvier, where axons are locally constricted. Strikingly,
these constrictions are accompanied by sharp decreases in
neurofilament number, no decreases in microtubule number,
and increases in the packing density of these polymers,
which collectively bring nodal neurofilaments closer to
their microtubule tracks. We hypothesize that this leads to
an increase in the proportion of time that the filaments
spend moving and that this can explain the local
acceleration. To test this, we developed a stochastic model
of neurofilament transport that tracks their number, kinetic
state, and proximity to nearby microtubules in space and
time. The model assumes that the probability of a
neurofilament moving is dependent on its distance from the
nearest available microtubule track. Taking into account
experimentally reported numbers and densities for
neurofilaments and microtubules in nodes and internodes, we
show that the model is sufficient to explain the local
acceleration of neurofilaments within nodes of Ranvier. This
suggests that proximity to microtubule tracks may be a key
regulator of neurofilament transport in axons, which has
implications for the mechanism of neurofilament accumulation
in development and disease.},
Doi = {10.1091/mbc.e19-09-0509},
Key = {fds374289}
}
@article{fds353552,
Author = {Topaz, C and Ciocanel, M-V and Cohen, P and Ott, M and Rodriguez,
N},
Title = {Institute for the Quantitative Study of Inclusion,
Diversity, and Equity (QSIDE)},
Journal = {Notices of the American Mathematical Society},
Volume = {67},
Number = {2},
Pages = {223-227},
Publisher = {American Mathematical Society},
Year = {2020},
Month = {February},
url = {http://dx.doi.org/10.1090/noti2019},
Doi = {10.1090/noti2019},
Key = {fds353552}
}
@article{fds353553,
Author = {Ciocanel, M-V and Jung, P and Brown, A},
Title = {A Mechanism for Neurofilament Transport Acceleration through
Nodes of Ranvier},
Journal = {Molecular Biology of the Cell},
Volume = {31},
Number = {7},
Publisher = {American Society for Cell Biology},
Year = {2020},
Month = {January},
url = {http://dx.doi.org/10.1101/806786},
Abstract = {<jats:title><jats:bold>Abstract</jats:bold></jats:title><jats:p>Neurofilaments
are abundant space-filling cytoskeletal polymers in axons
that are transported along microtubule tracks. Neurofilament
transport is accelerated at nodes of Ranvier, where axons
are locally constricted. Strikingly, these constrictions are
accompanied by a sharp decrease in neurofilament number but
no decrease in microtubule number, bringing neurofilaments
closer to their microtubule tracks. We hypothesize this
leads to an increase in the proportion of the time that the
filaments spend moving and that this can explain the local
acceleration. To test this, we developed a stochastic model
of neurofilament transport that tracks their number, kinetic
state and proximity to nearby microtubules in space and
time. The model assumes that the probability of a
neurofilament moving is dependent on its distance from the
nearest available microtubule track. Taking into account
experimentally reported numbers and densities for
neurofilaments and microtubules in nodes and internodes, we
show that the model is sufficient to explain the local
acceleration of neurofilaments across nodes of Ranvier. This
suggests that proximity to microtubule tracks may be a key
regulator of neurofilament transport in axons, which has
implications for the mechanism of neurofilament accumulation
in development and disease.</jats:p>},
Doi = {10.1101/806786},
Key = {fds353553}
}
@article{fds353554,
Author = {Adams, H and Ciocanel, M-V and Topaz, C and Ziegelmeier,
L},
Title = {Topological Data Analysis of Collective Motion},
Journal = {SIAM News},
Publisher = {SIAM News},
Year = {2020},
Month = {January},
Key = {fds353554}
}
@article{fds353555,
Author = {Panaggio, MJ and Ciocanel, M-V and Lazarus, L and Topaz, CM and Xu,
B},
Title = {Model reconstruction from temporal data for coupled
oscillator networks.},
Journal = {Chaos (Woodbury, N.Y.)},
Volume = {29},
Number = {10},
Pages = {103116},
Year = {2019},
Month = {October},
url = {http://dx.doi.org/10.1063/1.5120784},
Abstract = {In a complex system, the interactions between individual
agents often lead to emergent collective behavior such as
spontaneous synchronization, swarming, and pattern
formation. Beyond the intrinsic properties of the agents,
the topology of the network of interactions can have a
dramatic influence over the dynamics. In many studies,
researchers start with a specific model for both the
intrinsic dynamics of each agent and the interaction network
and attempt to learn about the dynamics of the model. Here,
we consider the inverse problem: given data from a system,
can one learn about the model and the underlying network? We
investigate arbitrary networks of coupled phase oscillators
that can exhibit both synchronous and asynchronous dynamics.
We demonstrate that, given sufficient observational data on
the transient evolution of each oscillator, machine learning
can reconstruct the interaction network and identify the
intrinsic dynamics.},
Doi = {10.1063/1.5120784},
Key = {fds353555}
}
@article{fds353556,
Author = {Ciocanel, M-V and Docken, SS and Gasper, RE and Dean, C and Carlson, BE and Olufsen, MS},
Title = {Cardiovascular regulation in response to multiple
hemorrhages: analysis and parameter estimation.},
Journal = {Biological cybernetics},
Volume = {113},
Number = {1-2},
Pages = {105-120},
Year = {2019},
Month = {April},
url = {http://dx.doi.org/10.1007/s00422-018-0781-y},
Abstract = {Mathematical models can provide useful insights explaining
behavior observed in experimental data; however, rigorous
analysis is needed to select a subset of model parameters
that can be informed by available data. Here we present a
method to estimate an identifiable set of parameters based
on baseline left ventricular pressure and volume time series
data. From this identifiable subset, we then select, based
on current understanding of cardiovascular control,
parameters that vary in time in response to blood
withdrawal, and estimate these parameters over a series of
blood withdrawals. These time-varying parameters are first
estimated using piecewise linear splines minimizing the mean
squared error between measured and computed left ventricular
pressure and volume data over four consecutive blood
withdrawals. As a final step, the trends in these splines
are fit with empirical functional expressions selected to
describe cardiovascular regulation during blood withdrawal.
Our analysis at baseline found parameters representing
timing of cardiac contraction, systemic vascular resistance,
and cardiac contractility to be identifiable. Of these
parameters, vascular resistance and cardiac contractility
were varied in time. Data used for this study were measured
in a control Sprague-Dawley rat. To our knowledge, this is
the first study to analyze the response to multiple blood
withdrawals both experimentally and theoretically, as most
previous studies focus on analyzing the response to one
large blood withdrawal. Results show that during each blood
withdrawal both systemic vascular resistance and
contractility decrease acutely and partially recover, and
they decrease chronically across the series of blood
withdrawals.},
Doi = {10.1007/s00422-018-0781-y},
Key = {fds353556}
}
@article{fds353557,
Author = {Ciocanel, MV and Stepien, TL and Sgouralis, I and Layton,
AT},
Title = {A multicellular vascular model of the renal myogenic
response},
Journal = {Processes},
Volume = {6},
Number = {7},
Year = {2018},
Month = {July},
url = {http://dx.doi.org/10.3390/PR6070089},
Abstract = {The myogenic response is a key autoregulatory mechanism in
the mammalian kidney. Triggered by blood pressure
perturbations, it is well established that the myogenic
response is initiated in the renal afferent arteriole and
mediated by alterations in muscle tone and vascular diameter
that counterbalance hemodynamic perturbations. The entire
process involves several subcellular, cellular, and vascular
mechanisms whose interactions remain poorly understood.
Here, we model and investigate the myogenic response of a
multicellular segment of an afferent arteriole. Extending
existing work, we focus on providing an accurate-but still
computationally tractable-representation of the coupling
among the involved levels. For individual muscle cells, we
include detailed Ca2+ signaling, transmembrane transport of
ions, kinetics of myosin light chain phosphorylation, and
contraction mechanics. Intercellular interactions are
mediated by gap junctions between muscle or endothelial
cells. Additional interactions are mediated by hemodynamics.
Simulations of time-independent pressure changes reveal
regular vasoresponses throughout the model segment and
stabilization of a physiological range of blood pressures
(80-180 mmHg) in agreement with other modeling and
experimental studies that assess steady autoregulation.
Simulations of time-dependent perturbations reveal irregular
vasoresponses and complex dynamics that may contribute to
the complexity of dynamic autoregulation observed in vivo.
The ability of the developed model to represent the myogenic
response in a multiscale and realistic fashion, under
feasible computational load, suggests that it can be
incorporated as a key component into larger models of
integrated renal hemodynamic regulation.},
Doi = {10.3390/PR6070089},
Key = {fds353557}
}
@article{fds353558,
Author = {Ciocanel, MV and Sandstede, B and Jeschonek, SP and Mowry,
KL},
Title = {Modeling Microtubule-Based Transport and Anchoring of
mRNA},
Journal = {SIAM Journal on Applied Dynamical Systems},
Volume = {17},
Number = {4},
Pages = {2855-2881},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1137/18M1186083},
Abstract = {Localization of messenger RNA (mRNA) at the vegetal cortex
plays an important role in the early development of Xenopus
laevis oocytes. While it is known that molecular motors are
responsible for the transport of mRNA cargo along
microtubules to the cortex, the mechanisms of localization
remain unclear. We model cargo transport along microtubules
using partial differential equations with spatially
dependent rates. A theoretical analysis of reduced versions
of our model predicts effective velocity and diffusion rates
for the cargo and shows that randomness of microtubule
networks enhances effective transport. A more complex model
using parameters estimated from fluorescence microscopy data
reproduces the time and spatial scales of mRNA localization
observed in Xenopus oocytes, corroborates experimental
hypotheses that anchoring may be necessary to achieve
complete localization, and shows that anchoring of mRNA
complexes actively transported to the cortex is most
effective in achieving robust accumulation at the
cortex.},
Doi = {10.1137/18M1186083},
Key = {fds353558}
}
@article{fds353559,
Author = {Ciocanel, M-V and Stepien, T and Edwards, A and Layton,
A},
Title = {Modeling Autoregulation of the Afferent Arteriole of the Rat
Kidney},
Journal = {Association for Women in Mathematics Series},
Volume = {8},
Publisher = {Springer, Cham},
Editor = {Miller, L},
Year = {2017},
Month = {August},
url = {http://dx.doi.org/10.1007/978-3-319-60304-9_5},
Abstract = {One of the key autoregulatory mechanisms that control blood
flow in the kidney is the myogenic response. Subject to
increased pressure, the renal afferent arteriole responds
with an increase in muscle tone and a decrease in diameter.
To investigate the myogenic response of an afferent
arteriole segment of the rat kidney, we extend a
mathematical model of an afferent arteriole cell. For each
cell, we include detailed Ca2+ signaling, transmembrane
transport of major ions, the kinetics of myosin light chain
phosphorylation, as well as cellular contraction and wall
mechanics. To model an afferent arteriole segment, a number
of cell models are connected in series by gap junctions,
which link the cytoplasm of neighboring cells. Blood flow
through the afferent arteriole is modeled using Poiseuille
flow. Simulation of an inflow pressure up-step leads to a
decrease in the diameter for the proximal part of the vessel
(vasoconstriction) and to an increase in proximal vessel
diameter (vasodilation) for an inflow pressure down-step.
Through its myogenic response, the afferent arteriole
segment model yields approximately stable outflow pressure
for a physiological range of inflow pressures
(100–160 mmHg), consistent with experimental
observations. The present model can be incorporated as a key
component into models of integrated renal hemodynamic
regulation.},
Doi = {10.1007/978-3-319-60304-9_5},
Key = {fds353559}
}
@article{fds353560,
Author = {Ciocanel, M-V and Kreiling, JA and Gagnon, JA and Mowry, KL and Sandstede, B},
Title = {Analysis of Active Transport by Fluorescence Recovery after
Photobleaching.},
Journal = {Biophysical journal},
Volume = {112},
Number = {8},
Pages = {1714-1725},
Publisher = {Elsevier BV},
Year = {2017},
Month = {April},
url = {http://dx.doi.org/10.1016/j.bpj.2017.02.042},
Abstract = {Fluorescence recovery after photobleaching (FRAP) is a
well-established experimental technique to study binding and
diffusion of molecules in cells. Although a large number of
analytical and numerical models have been developed to
extract binding and diffusion rates from FRAP recovery
curves, active transport of molecules is typically not
included in the existing models that are used to estimate
these rates. Here we present a validated numerical method
for estimating diffusion, binding/unbinding rates, and
active transport velocities using FRAP data that captures
intracellular dynamics through partial differential equation
models. We apply these methods to transport and localization
of mRNA molecules in Xenopus laevis oocytes, where active
transport processes are essential to generate developmental
polarity. By providing estimates of the effective velocities
and diffusion, as well as expected run times and lengths,
this approach can help quantify dynamical properties of
localizing and nonlocalizing RNA. Our results confirm the
distinct transport dynamics in different regions of the
cytoplasm, and suggest that RNA movement in both the animal
and vegetal directions may influence the timescale of RNA
localization in Xenopus oocytes. We also show that model
initial conditions extracted from FRAP postbleach
intensities prevent underestimation of diffusion, which can
arise from the instantaneous bleaching assumption. The
numerical and modeling approach presented here to estimate
parameters using FRAP recovery data is a broadly applicable
tool for systems where intracellular transport is a key
molecular mechanism.},
Doi = {10.1016/j.bpj.2017.02.042},
Key = {fds353560}
}
@article{fds353561,
Author = {Powrie, EA and Ciocanel, V and Kreiling, JA and Gagnon, JA and Sandstede, B and Mowry, KL},
Title = {Using in vivo imaging to measure RNA mobility in Xenopus
laevis oocytes},
Journal = {Methods},
Volume = {98},
Pages = {60-65},
Publisher = {Elsevier BV},
Year = {2016},
Month = {April},
url = {http://dx.doi.org/10.1016/j.ymeth.2015.11.003},
Doi = {10.1016/j.ymeth.2015.11.003},
Key = {fds353561}
}
@article{fds354092,
Author = {Ciocanel, V},
Title = {Modeling and Numerical Simulation of the Nonlinear Dynamics
of the Parametrically Forced String Pendulum},
Journal = {SIAM Undergraduate Research Online},
Volume = {5},
Pages = {95-115},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2012},
url = {http://dx.doi.org/10.1137/11s011444},
Doi = {10.1137/11s011444},
Key = {fds354092}
}
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Mathematics Department