93 research outputs found
Outer-totalistic cellular automata on graphs
We present an intuitive formalism for implementing cellular automata on
arbitrary topologies. By that means, we identify a symmetry operation in the
class of elementary cellular automata. Moreover, we determine the subset of
topologically sensitive elementary cellular automata and find that the overall
number of complex patterns decreases under increasing neighborhood size in
regular graphs. As exemplary applications, we apply the formalism to complex
networks and compare the potential of scale-free graphs and metabolic networks
to generate complex dynamics.Comment: 5 pages, 4 figures, 1 table. To appear in Physics Letters
Reply to ''Comment on 'Regularizing Capacity of Metabolic Networks' ''
In a recent paper [C. Marr, M. Mueller-Linow, and M.-T. Huett, Phys. Rev. E
75, 041917 (2007)] we discuss the pronounced potential of real metabolic
network topologies, compared to randomized counterparts, to regularize complex
binary dynamics. In their comment [P. Holme and M. Huss, arXiv:0705.4084v1],
Holme and Huss criticize our approach and repeat our study with more realistic
dynamics, where stylized reaction kinetics are implemented on sets of pairwise
reactions. The authors find no dynamic difference between the reaction sets
recreated from the metabolic networks and randomized counterparts. We reproduce
the author's observation and find that their algorithm leads to a dynamical
fragmentation and thus eliminates the topological information contained in the
graphs. Hence, their approach cannot rule out a connection between the topology
of metabolic networks and the ubiquity of steady states.Comment: 2 pages, 2 figure
Similar impact of topological and dynamic noise on complex patterns
Shortcuts in a regular architecture affect the information transport through
the system due to the severe decrease in average path length. A fundamental new
perspective in terms of pattern formation is the destabilizing effect of
topological perturbations by processing distant uncorrelated information,
similarly to stochastic noise. We study the functional coincidence of rewiring
and noisy communication on patterns of binary cellular automata.Comment: 8 pages, 7 figures. To be published in Physics Letters
Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
Regulation of quiescence and cell cycle entry is pivotal for the maintenance of stem cell populations. Regulatory mechanisms, however, are poorly understood. In particular, it is unclear how the activity of single stem cells is coordinated within the population or if cells divide in a purely random fashion. We addressed this issue by analyzing division events in an adult neural stem cell (NSC) population of the zebrafish telencephalon. Spatial statistics and mathematical modeling of over 80,000 NSCs in 36 brain hemispheres revealed weakly aggregated, nonrandom division patterns in space and time. Analyzing divisions at 2 time points allowed us to infer cell cycle and S-phase lengths computationally. Interestingly, we observed rapid cell cycle reentries in roughly 15% of newly born NSCs. In agent-based simulations of NSC populations, this redividing activity sufficed to induce aggregated spatiotemporal division patterns that matched the ones observed experimentally. In contrast, omitting redivisions leads to a random spatiotemporal distribution of dividing cells. Spatiotemporal aggregation of dividing stem cells can thus emerge solely from the cell's history
MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
Background: Biological data often originate from samples containing mixtures
of subpopulations, corresponding e.g. to distinct cellular phenotypes. However,
identification of distinct subpopulations may be difficult if biological
measurements yield distributions that are not easily separable. Results: We
present Multiresolution Correlation Analysis (MCA), a method for visually
identifying subpopulations based on the local pairwise correlation between
covariates, without needing to define an a priori interaction scale. We
demonstrate that MCA facilitates the identification of differentially regulated
subpopulations in simulated data from a small gene regulatory network, followed
by application to previously published single-cell qPCR data from mouse
embryonic stem cells. We show that MCA recovers previously identified
subpopulations, provides additional insight into the underlying correlation
structure, reveals potentially spurious compartmentalizations, and provides
insight into novel subpopulations. Conclusions: MCA is a useful method for the
identification of subpopulations in low-dimensional expression data, as
emerging from qPCR or FACS measurements. With MCA it is possible to investigate
the robustness of covariate correlations with respect subpopulations,
graphically identify outliers, and identify factors contributing to
differential regulation between pairs of covariates. MCA thus provides a
framework for investigation of expression correlations for genes of interests
and biological hypothesis generation.Comment: BioVis 2014 conferenc
A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes
Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial
for clinical decision-making and patient care. In this study, we investigate
the potential presence of age and sex bias in AML subtype classification using
Multiple Instance Learning (MIL) architectures. To that end, we train multiple
MIL models using different levels of sex imbalance in the training set and
excluding certain age groups. To assess the sex bias, we evaluate the
performance of the models on male and female test sets. For age bias, models
are tested against underrepresented age groups in the training data. We find a
significant effect of sex and age bias on the performance of the model for AML
subtype classification. Specifically, we observe that females are more likely
to be affected by sex imbalance dataset and certain age groups, such as
patients with 72 to 86 years of age with the RUNX1::RUNX1T1 genetic subtype,
are significantly affected by an age bias present in the training data.
Ensuring inclusivity in the training data is thus essential for generating
reliable and equitable outcomes in AML genetic subtype classification,
ultimately benefiting diverse patient populations.Comment: Accepted for publication at workshop on Fairness of AI in Medical
Imaging in International Conference on Medical Image Computing and Computer
Assisted Intervention (MICCAI 2023
Two distinct logical types of network control in gene expression profiles
In unicellular organisms such as bacteria the same acquired mutations
beneficial in one environment can be restrictive in another. However, evolving
Escherichia coli populations demonstrate remarkable flexibility in adaptation.
The mechanisms sustaining genetic flexibility remain unclear. In E. coli the
transcriptional regulation of gene expression involves both dedicated
regulators binding specific DNA sites with high affinity and also global
regulators - abundant DNA architectural proteins of the bacterial chromoid
binding multiple low affinity sites and thus modulating the superhelical
density of DNA. The first form of transcriptional regulation is dominantly
pairwise and specific, representing digitial control, while the second form is
(in strength and distribution) continuous, representing analog control. Here we
look at the properties of effective networks derived from significant gene
expression changes under variation of the two forms of control and find that
upon limitations of one type of control (caused e.g. by mutation of a global
DNA architectural factor) the other type can compensate for compromised
regulation. Mutations of global regulators significantly enhance the digital
control; in the presence of global DNA architectural proteins regulation is
mostly of the analog type, coupling spatially neighboring genomic loci;
together our data suggest that two logically distinct types of control are
balancing each other. By revealing two distinct logical types of control, our
approach provides basic insights into both the organizational principles of
transcriptional regulation and the mechanisms buffering genetic flexibility. We
anticipate that the general concept of distinguishing logical types of control
will apply to many complex biological networks.Comment: 19 pages, 6 figure
The Regularizing Capacity of Metabolic Networks
Despite their topological complexity almost all functional properties of
metabolic networks can be derived from steady-state dynamics. Indeed, many
theoretical investigations (like flux-balance analysis) rely on extracting
function from steady states. This leads to the interesting question, how
metabolic networks avoid complex dynamics and maintain a steady-state behavior.
Here, we expose metabolic network topologies to binary dynamics generated by
simple local rules. We find that the networks' response is highly specific:
Complex dynamics are systematically reduced on metabolic networks compared to
randomized networks with identical degree sequences. Already small topological
modifications substantially enhance the capacity of a network to host complex
dynamic behavior and thus reduce its regularizing potential. This exceptionally
pronounced regularization of dynamics encoded in the topology may explain, why
steady-state behavior is ubiquitous in metabolism.Comment: 6 pages, 4 figure
Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology
Accurate morphological classification of white blood cells (WBCs) is an
important step in the diagnosis of leukemia, a disease in which nonfunctional
blast cells accumulate in the bone marrow. Recently, deep convolutional neural
networks (CNNs) have been successfully used to classify leukocytes by training
them on single-cell images from a specific domain. Most CNN models assume that
the distributions of the training and test data are similar, i.e., that the
data are independently and identically distributed. Therefore, they are not
robust to different staining protocols, magnifications, resolutions, scanners,
or imaging protocols, as well as variations in clinical centers or patient
cohorts. In addition, domain-specific data imbalances affect the generalization
performance of classifiers. Here, we train a robust CNN for WBC classification
by addressing cross-domain data imbalance and domain shifts. To this end, we
use two loss functions and demonstrate the effectiveness on out-of-distribution
(OOD) generalization. Our approach achieves the best F1 macro score compared to
other existing methods, and is able to consider rare cell types. This is the
first demonstration of imbalanced domain generalization in hematological
cytomorphology and paves the way for robust single cell classification methods
for the application in laboratories and clinics.Comment: Published as a ICLR 2023 workshop paper: What do we need for
successful domain generalization
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique
that enables high-resolution imaging of samples, experiences defocused image
quality caused by light scattering when photons propagate through thick
tissues. To circumvent this issue, dualview imaging is helpful. It allows
various sections of the specimen to be scanned ideally by viewing the sample
from opposing orientations. Recent image fusion approaches can then be applied
to determine in-focus pixels by comparing image qualities of two views locally
and thus yield spatially inconsistent focus measures due to their limited
field-of-view. Here, we propose BigFUSE, a global context-aware image fuser
that stabilizes image fusion in LSFM by considering the global impact of photon
propagation in the specimen while determining focus-defocus based on local
image qualities. Inspired by the image formation prior in dual-view LSFM, image
fusion is considered as estimating a focus-defocus boundary using Bayes
Theorem, where (i) the effect of light scattering onto focus measures is
included within Likelihood; and (ii) the spatial consistency regarding
focus-defocus is imposed in Prior. The expectation-maximum algorithm is then
adopted to estimate the focus-defocus boundary. Competitive experimental
results show that BigFUSE is the first dual-view LSFM fuser that is able to
exclude structured artifacts when fusing information, highlighting its
abilities of automatic image fusion.Comment: paper in MICCAI 202
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