1,458 research outputs found
Control of complex networks requires both structure and dynamics
The study of network structure has uncovered signatures of the organization
of complex systems. However, there is also a need to understand how to control
them; for example, identifying strategies to revert a diseased cell to a
healthy state, or a mature cell to a pluripotent state. Two recent
methodologies suggest that the controllability of complex systems can be
predicted solely from the graph of interactions between variables, without
considering their dynamics: structural controllability and minimum dominating
sets. We demonstrate that such structure-only methods fail to characterize
controllability when dynamics are introduced. We study Boolean network
ensembles of network motifs as well as three models of biochemical regulation:
the segment polarity network in Drosophila melanogaster, the cell cycle of
budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in
Arabidopsis thaliana. We demonstrate that structure-only methods both
undershoot and overshoot the number and which sets of critical variables best
control the dynamics of these models, highlighting the importance of the actual
system dynamics in determining control. Our analysis further shows that the
logic of automata transition functions, namely how canalizing they are, plays
an important role in the extent to which structure predicts dynamics.Comment: 15 pages, 6 figure
Modularity and the spread of perturbations in complex dynamical systems
We propose a method to decompose dynamical systems based on the idea that
modules constrain the spread of perturbations. We find partitions of system
variables that maximize 'perturbation modularity', defined as the
autocovariance of coarse-grained perturbed trajectories. The measure
effectively separates the fast intramodular from the slow intermodular dynamics
of perturbation spreading (in this respect, it is a generalization of the
'Markov stability' method of network community detection). Our approach
captures variation of modular organization across different system states, time
scales, and in response to different kinds of perturbations: aspects of
modularity which are all relevant to real-world dynamical systems. It offers a
principled alternative to detecting communities in networks of statistical
dependencies between system variables (e.g., 'relevance networks' or
'functional networks'). Using coupled logistic maps, we demonstrate that the
method uncovers hierarchical modular organization planted in a system's
coupling matrix. Additionally, in homogeneously-coupled map lattices, it
identifies the presence of self-organized modularity that depends on the
initial state, dynamical parameters, and type of perturbations. Our approach
offers a powerful tool for exploring the modular organization of complex
dynamical systems
Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions
Background. Drug-drug interaction (DDI) is a major cause of morbidity and
mortality. [...] Biomedical literature mining can aid DDI research by
extracting relevant DDI signals from either the published literature or large
clinical databases. However, though drug interaction is an ideal area for
translational research, the inclusion of literature mining methodologies in DDI
workflows is still very preliminary. One area that can benefit from literature
mining is the automatic identification of a large number of potential DDIs,
whose pharmacological mechanisms and clinical significance can then be studied
via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We
implemented a set of classifiers for identifying published articles relevant to
experimental pharmacokinetic DDI evidence. These documents are important for
identifying causal mechanisms behind putative drug-drug interactions, an
important step in the extraction of large numbers of potential DDIs. We
evaluate performance of several linear classifiers on PubMed abstracts, under
different feature transformation and dimensionality reduction methods. In
addition, we investigate the performance benefits of including various
publicly-available named entity recognition features, as well as a set of
internally-developed pharmacokinetic dictionaries. Results. We found that
several classifiers performed well in distinguishing relevant and irrelevant
abstracts. We found that the combination of unigram and bigram textual features
gave better performance than unigram features alone, and also that
normalization transforms that adjusted for feature frequency and document
length improved classification. For some classifiers, such as linear
discriminant analysis (LDA), proper dimensionality reduction had a large impact
on performance. Finally, the inclusion of NER features and dictionaries was
found not to help classification.Comment: Pacific Symposium on Biocomputing, 201
Actionable models of control in complex systems for the biomedical domain
Modern artificial intelligence (AI) and machine learning (ML) techniques predict system
behavior from previous observations. However, in biology, and in true complex systems
in general, profound transformation in system behavior can occur from never before
seen observations. Moreover, AI/ML techniques are often unable to provide an explanation
for their predictions. I will argue that complex systems modeling, while using AI/
ML techniques to estimate parameters, needs to produce actionable, dynamical models
that can predict and explain system behavior for rare or unseen control events. This
is particularly true for understanding biochemical regulation under dynamical perturbations
from environmental and evolutionary events. Towards that goal, I will present
our recent work uncovering effective control pathways in the canalizing dynamics of
biochemical regulation. Our methodology centers on removing redundancy from systems biology models of development, cell cycle, and cancer response, as well as in
models of cortical networks cultured from mouse brains. Removing the large amounts of
redundancy in these models, reveals preferred pathways for the spread of perturbations
and the building blocks of dynamical response, leading to the prediction of actionable
control interventions
City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions
The occurrence of drug-drug-interactions (DDI) from multiple drug
dispensations is a serious problem, both for individuals and health-care
systems, since patients with complications due to DDI are likely to reenter the
system at a costlier level. We present a large-scale longitudinal study (18
months) of the DDI phenomenon at the primary- and secondary-care level using
electronic health records (EHR) from the city of Blumenau in Southern Brazil
(pop. ). We found that 181 distinct drug pairs known to
interact were dispensed concomitantly to 12\% of the patients in the city's
public health-care system. Further, 4\% of the patients were dispensed drug
pairs that are likely to result in major adverse drug reactions (ADR)---with
costs estimated to be much larger than previously reported in smaller studies.
The large-scale analysis reveals that women have a 60\% increased risk of DDI
as compared to men; the increase becomes 90\% when considering only DDI known
to lead to major ADR. Furthermore, DDI risk increases substantially with age;
patients aged 70-79 years have a 34\% risk of DDI when they are dispensed two
or more drugs concomitantly. Interestingly, a statistical null model
demonstrates that age- and female-specific risks from increased polypharmacy
fail by far to explain the observed DDI risks in those populations, suggesting
unknown social or biological causes. We also provide a network visualization of
drugs and demographic factors that characterize the DDI phenomenon and
demonstrate that accurate DDI prediction can be included in healthcare and
public-health management, to reduce DDI-related ADR and costs
- …