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Description
The power of science lies in its ability to infer and predict the
existence of objects from which no direct information can be obtained
experimentally or observationally. A well known example is to
ascertain the existence of black holes of various masses in different
parts of the universe from indirect evidence, such as X-ray emissions.
In the field of complex networks, the problem of detecting
hidden nodes can be stated, as follows. Consider a network whose
topology is completely unknown but whose nodes consist of two types:
one accessible and another inaccessible from the outside world. The
accessible nodes can be observed or monitored, and it is assumed that time
series are available from each node in this group. The inaccessible
nodes are shielded from the outside and they are essentially
``hidden.'' The question is, based solely on the
available time series from the accessible nodes, can the existence and
locations of the hidden nodes be inferred? A completely data-driven,
compressive-sensing based method is developed to address this issue by utilizing
complex weighted networks of nonlinear oscillators, evolutionary game
and geospatial networks.
Both microbes and multicellular organisms actively regulate their cell
fate determination to cope with changing environments or to ensure
proper development. Here, the synthetic biology approaches are used to
engineer bistable gene networks to demonstrate that stochastic and
permanent cell fate determination can be achieved through initializing
gene regulatory networks (GRNs) at the boundary between dynamic
attractors. This is experimentally realized by linking a synthetic GRN
to a natural output of galactose metabolism regulation in yeast.
Combining mathematical modeling and flow cytometry, the
engineered systems are shown to be bistable and that inherent gene expression
stochasticity does not induce spontaneous state transitioning at
steady state. By interfacing rationally designed synthetic
GRNs with background gene regulation mechanisms, this work
investigates intricate properties of networks that illuminate possible
regulatory mechanisms for cell differentiation and development that
can be initiated from points of instability.
existence of objects from which no direct information can be obtained
experimentally or observationally. A well known example is to
ascertain the existence of black holes of various masses in different
parts of the universe from indirect evidence, such as X-ray emissions.
In the field of complex networks, the problem of detecting
hidden nodes can be stated, as follows. Consider a network whose
topology is completely unknown but whose nodes consist of two types:
one accessible and another inaccessible from the outside world. The
accessible nodes can be observed or monitored, and it is assumed that time
series are available from each node in this group. The inaccessible
nodes are shielded from the outside and they are essentially
``hidden.'' The question is, based solely on the
available time series from the accessible nodes, can the existence and
locations of the hidden nodes be inferred? A completely data-driven,
compressive-sensing based method is developed to address this issue by utilizing
complex weighted networks of nonlinear oscillators, evolutionary game
and geospatial networks.
Both microbes and multicellular organisms actively regulate their cell
fate determination to cope with changing environments or to ensure
proper development. Here, the synthetic biology approaches are used to
engineer bistable gene networks to demonstrate that stochastic and
permanent cell fate determination can be achieved through initializing
gene regulatory networks (GRNs) at the boundary between dynamic
attractors. This is experimentally realized by linking a synthetic GRN
to a natural output of galactose metabolism regulation in yeast.
Combining mathematical modeling and flow cytometry, the
engineered systems are shown to be bistable and that inherent gene expression
stochasticity does not induce spontaneous state transitioning at
steady state. By interfacing rationally designed synthetic
GRNs with background gene regulation mechanisms, this work
investigates intricate properties of networks that illuminate possible
regulatory mechanisms for cell differentiation and development that
can be initiated from points of instability.
ContributorsSu, Ri-Qi (Author) / Lai, Ying-Cheng (Thesis advisor) / Wang, Xiao (Thesis advisor) / Bliss, Daniel (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and the most irreversible driver. Meantime, in the face of global climate change, extreme climatic events, such as extreme precipitations, heatwaves, droughts, etc., are projected to be more frequent, more intense, and longer in duration. These nonlinear responses in climate dynamics from tipping points to extreme events pose serious threats to human society on a large scale. Understanding the physical mechanisms behind them has potential to reduce related risks through different ways. The overarching objective of this dissertation is to quantify complex interactions, detect tipping points, and explore propagations of extreme events in the hydroclimate system from a new structure-based perspective, by integrating climate dynamics, causal inference, network theory, spectral analysis, and machine learning. More specifically, a network-based framework is developed to find responses of hydroclimate system to potential critical transitions in climate. Results show that system-based early warning signals towards tipping points can be located successfully, demonstrated by enhanced connections in the network topology. To further evaluate the long-term nonlinear interactions among the U.S. climate regions, causality inference is introduced and directed complex networks are constructed from climatology records over one century. Causality networks reveal that the Ohio valley region acts as a regional gateway and mediator to the moisture transport and thermal transfer in the U.S. Furthermore, it is found that cross-regional causality variability manifests intrinsic frequency ranging from interannual to interdecadal scales, and those frequencies are associated with the physics of climate oscillations. Besides the long-term climatology, this dissertation also aims to explore extreme events from the system-dynamic perspective, especially the contributions of human-induced activities to propagation of extreme heatwaves in the U.S. cities. Results suggest that there are long-range teleconnections among the U.S. cities and supernodes in heatwave spreading. Findings also confirm that anthropogenic activities contribute to extreme heatwaves by the fact that causality during heatwaves is positively associated with population in megacities.
ContributorsYang, Xueli (Author) / Yang, Zhihua (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Li, Qi (Committee member) / Xu, Tianfang (Committee member) / Zeng, Ruijie (Committee member) / Arizona State University (Publisher)
Created2023