Wang, Qi

Zhao, Jia

Elsevier - Publisher Connector 

Computational Modeling of Biofilm Structure and Functions with Bacteria Motility Feature and Experiment Validation 

Wednesday, February 19, 2014 643aPlatform: Computational Systems Biology and
Cellular Network
3249-Plat
Integrated 3D Simulation of Cardiomyocyte Revealed the Distinct Func-
tional Characteristics between Subsarcolemmal and Interfibrillar Mito-
chondria
Asuka Hatano, Jun-ichi Okada, Takumi Washio, Toshiaki Hisada,
Seiryo Sugiura.
The University of Tokyo, Tokyo, Japan.
It has been reported that two subpopulations of mitochondria exist in cardio-
myocyte: subsarcolemmal mitochondria (SSM) and interfibrillar mitochondria
(IFM). However, their functional characteristics have yet to be clarified, due to
the experimental difficulties in differential isolation and assessment techniques.
We have already developed a 3-D computational model of cardiomyocyte with
subcellular structure integrating electrophysiology, metabolism, and me-
chanics. In this study, this model was further improved to include the intracel-
lular gradient of oxygen and myoglobin distribution to test the hypothesis that
the difference in location within the cell can introduce significant changes in
mitochondrial metabolism. For this purpose, all mitochondria in the model
were made to have the same membrane permeability and enzymatic activities.
When compared the responses of [Ca2þ], TCA activity, [NADH] and mito-
chondrial inner membrane potential to an abrupt changes in pacing frequency
(0.25 Hz to 2 Hz) between SSM and IFM, IFM reached higher plateau levels in
all of these parameters. These differences seemed to be related to the intracel-
lular gradient in [Ca2þ]. We also examined the effect of reduction in [O2]. Un-
der normal condition intracellular [O2] is much higher than the half saturation
concentration for oxidative phosphorylation even for the IFM located in the
core region of the cell. However under limited extracellular [O2] environment,
[NADH] and inner membrane potential gradually decreased in IFM compared
to SSM reflecting the intracellular [O2] gradient from the cell surface to the
core. Although further validations are required, the current simulation results
suggested that only the difference in intracellular location can cause the
different functional characteristics of mitochondrial metabolism in
cardiomyocytes.
3250-Plat
Pysb: A Modeling Framework to Explore Biochemical Signaling Processes
and Cell-Decisions
Carlos F. Lopez, Shawn P. Garbett.
Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN,
USA.
Experiments often result in observations that suggest conflicting biochemical
mechanisms in signaling networks. Mathematical modeling of biological sys-
tems could be used to probe knowledge derived from experimental observa-
tions. However, probing multiple mechanistic hypotheses in biological
modeling often involves the instantiation of complex systems of equations
that can make model revision, extension, and sharing challenging. To address
these modeling barriers, we have developed a modeling framework that brings
a program-based approach to biological modeling. In our approach, biological
models are written as Python programs that encode biological functions as
executable code. I will discuss the development and implementation of these
methods to explore intracellular signaling pathway crosstalk and response to
external cues in the context of programmed cell death and life/death decision
processes in cancer biology will be presented. Our approach to model calibra-
tion to experimental data, extracting important knowledge from biochemical
signaling networks, and developing tools to relate models to experiment will
be discussed. We will also highlight how
a programming language for biological
modeling facilitates model tracking,
sharing, and dissemination.3251-Plat
The Evolution of Crosstalk in Signaling Networks
Eric J. Deeds1, Michael A. Rowland2.
1Center for Bioinformatics and Department of Molecular Biosciences, The
Univerisity of Kansas, Lawrence, KS, USA, 2Center for Bioinformatics, The
Univerisity of Kansas, Lawrence, KS, USA.
The degree of crosstalk observed in signaling networks varies widely across
evolution. In eukaryotes, crosstalk is widespread, with some kinases acting
on hundreds of downstream targets. In bacteria, however, signaling pathways
are essentially completely isolated from one another. It is currently unclear
what pressures have driven the evolution of these vastly different topologies.
The basic "building block" of eukaryotic signaling networks is a pair ofenzymes, one that modifies a substrate (e.g. a kinase) and one that undoes
this modification (e.g. a phosphatase). We recently used mathematical models
to show that adding crosstalk to this type of system can increase ultrasensitiv-
ity and couple signal responses, behaviors that could yield phenotypic benefits
for eukaryotic cells. In contrast, bacterial networks utilize Two-Component
Signaling (TCS), in which a single enzyme (the sensor Histidine Kinase, or
HK) acts as both kinase and phosphatase for its downstream Response Regu-
lator (RR). We found that crosstalk always reduces signal response in TCS,
providing an explanation for the experimental observation that engineering
crosstalk into bacterial cells dramatically reduces their fitness. The pressure
to maintain signaling responses is also sufficient to quantitatively explain
the kinetic preference of HKs for their cognate substrates. Using our models,
we characterized a set of ‘‘near-neutral’’ evolutionary pathways that would
allow bacteria to minimize the impact of crosstalk as they evolve new HK-
RR pairs through duplication and divergence of existing pathways. Analysis
of HK sequences confirmed that most TCS pathways evolve via the trajec-
tories we predicted. Our work thus indicates that the different topologies of
eukaryotic and bacterial signaling networks likely arise from fundamental dif-
ferences in the behavior of the motifs from which the networks themselves are
constructed. These differences have important consequences for both the func-
tion and the evolutionary dynamics of information processing systems within
cells.
3252-Plat
Computational Modeling of Biofilm Structure and Functions with Bacteria
Motility Feature and Experiment Validation
Jia Zhao, Qi Wang.
Mathematics, University of South Carolina, Columbia, SC, USA.
Biofilm is ubiquitous in our daily life, such as in dental plaque and sewage
pipes. It is a microorganism, where bacteria stick together by secreting extra-
cellular polymeric substances (EPS), a complex biophysical world. Biofilm is
the main factor for much chronic disease, thus understanding the mechanics
in biofilm development is critical for therapeutic treatment of biofilm.
In this talk, our work on computational hydrodynamic modeling of biofilm
would be presented. Both kinetic approaches from bacteria collision effects,
and macroscopic view of mixing free energy will been discussed. Using
GPGPU, 3D numerical solver has been developed analyzing biofilm structure
and functions, such as heterogeneous structure formation, antimicrobial persis-
tent and quorum sensing features. Bacteria motility modeling in biofilm will be
studied, as well, from both microscopic and macroscopic view.
Besides, our model has been validated by our collaborators’ experiment results
for varying biofilm persistence to antibiotics at different age and biofilm relapse
after the cease of dosing. Persuasively, our model agrees quantitatively well.
Hence it turns out to be an effective tool for analyzing the mechanism of biofilm
formation and antimicrobial persistence, as well as therapeutic treatment of
biofilm.
3253-Plat
Identifying Active Neurons from In Vivo 2-Photon Calcium Imaging of the
Brain via Pixel Correlation Analysis and Region-Growing Segmentation
Jean-Francois Desjardins1, Loı¨s S. Miraucourt2, Edward S. Ruthazer2,
Paul W. Wiseman3.
1Department of Physics, McGill University, Montreal, QC, Canada,
2Montreal Neurological Institute, McGill University, Montreal, QC, Canada,
3Department of Physics,Department of Chemistry, McGill University,
Montreal, QC, Canada.
Calcium imaging of neurons is a powerful tool to measure in vivo neuronal ac-
tivity where one of the key challenges for calcium spike train characterization is
its requirement for rapid temporal sampling to resolve fluorescent transients.
However, to distinguish nearby cells that have only small differences in their
firing patterns remains a challenge. Current automated image segmentation
techniques which rely on the spatial analysis of image intensity, fail to accu-
rately identify single-cell regions of interest (ROI). We present a cell parsing
technique that combines a region-growing method and correlation analysis of
pixel stacks to find and optimize the ROI boundaries of single cells in calcium
imaging times series. While a spatio-temporal correlation function gives a
signature of the calcium activity, a region-growing method gathers correlated
activity into ROIs. In computer simulations of calcium imaging data sets, the
algorithm was able to generate appropriate ROIs including those with irregular
morphologies. The algorithm was also applied to in vivo 2-photon calcium im-
aging of Oregon Green BAPTA-1 in the Xenopus laevis optic tectum and
retina, in both cases effectively identifying active single cell ROIs correspond-
ing to confirmed cell boundaries. These results demonstrate that the combina-
tion of a region-growing method with correlation analysis for segmentation
permits robust automated cell segmentation which has the potential to identify


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Computational Modeling of Biofilm Structure and Functions with Bacteria Motility Feature and Experiment Validation

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