Entropy production and fluctuation theorems on complex networks.

Entropy production (EP) is a fundamental quantity useful for understanding irreversible process. In stochastic thermodynamics, EP is more evident in probability density functions of trajectories of a particle in the state space. Here, inspired by a previous result that complex networks can serve as state spaces, we consider a data packet transport problem on complex networks. EP is generated owing to the complexity of pathways as the packet travels back and forth between two nodes along the same pathway. The total EPs are exactly enumerated along all possible shortest paths between every pair of nodes, and the functional form of the EP distribution is proposed based on our numerical results. We confirm that the EP distribution satisfies the detailed and integral fluctuation theorems. Our results should be pedagogically helpful for understanding trajectory-dependent EP in stochastic processes and exploring nonequilibrium fluctuations associated with the entanglement of dividing and merging among the shortest pathways in complex networks.

ATAD5 promotes replication restart by regulating RAD51 and PCNA in response to replication stress.

Maintaining stability of replication forks is important for genomic integrity. However, it is not clear how replisome proteins contribute to fork stability under replication stress. Here, we report that ATAD5, a PCNA unloader, plays multiple functions at stalled forks including promoting its restart. ATAD5 depletion increases genomic instability upon hydroxyurea treatment in cultured cells and mice. ATAD5 recruits RAD51 to stalled forks in an ATR kinase-dependent manner by hydroxyurea-enhanced protein-protein interactions and timely removes PCNA from stalled forks for RAD51 recruitment. Consistent with the role of RAD51 in fork regression, ATAD5 depletion inhibits slowdown of fork progression and native 5-bromo-2'-deoxyuridine signal induced by hydroxyurea. Single-molecule FRET showed that PCNA itself acts as a mechanical barrier to fork regression. Consequently, DNA breaks required for fork restart are reduced by ATAD5 depletion. Collectively, our results suggest an important role of ATAD5 in maintaining genome integrity during replication stress.

Interevent time distribution, burst, and hybrid percolation transition.

Understanding of a hybrid percolation transitions (HPTs) induced by cluster coalescence, exhibiting a jump in the giant cluster size and a critical behavior of finite clusters, is fundamental and intriguing. Here, we uncover the underlying mechanism using the so-called restricted-random network model, in which clusters are ranked by size and partitioned into small- and large-cluster sets. As clusters are merged and their rankings are updated, they may move back and forth across the set boundary. The intervals of these crossings exhibit a self-organized critical (SOC) behavior with two power-law exponents. During this process, a bump is formed and eliminated in the cluster size distribution, characterizing the criticality of the HPT. This SOC behavior is in contrast to the critical branching process, which governs the avalanche dynamics of the HPT in the pruning process. Finally, we find that a burst of such crossing events occurs and signals the upcoming abrupt transition.

Targeting metastatic breast cancer with peptide epitopes derived from autocatalytic loop of Prss14/ST14 membrane serine protease and with monoclonal antibodies.

BACKGROUND: In order to develop a new immunotherapeutic agent targeting metastatic breast cancers, we chose to utilize autocatalytic feature of the membrane serine protease Prss14/ST14, a specific prognosis marker for ER negative breast cancer as a target molecule. METHODS: The study was conducted using three mouse breast cancer models, 4 T1 and E0771 mouse breast cancer cells into their syngeneic hosts, and an MMTV-PyMT transgenic mouse strain was used. Prss14/ST14 knockdown cells were used to test function in tumor growth and metastasis, peptides derived from the autocatalytic loop for activation were tested as preventive metastasis vaccine, and monoclonal and humanized antibodies to the same epitope were tested as new therapeutic candidates. ELISA, immunoprecipitation, Immunofluorescent staining, and flow cytometry were used to examine antigen binding. The functions of antibodies were tested in vitro for cell migration and in vivo for tumor growth and metastasis. RESULTS: Prss14/ST14 is critically involved in the metastasis of breast cancer and poor survival rather than primary tumor growth in two mouse models. The epitopes derived from the specific autocatalytic loop region of Prss14/ST14, based on structural modeling acted as efficient preventive metastasis vaccines in mice. A new specific monoclonal antibody mAb3F3 generated against the engineered loop structure could reduce cell migration, eliminate metastasis in PyMT mice, and can detect the Prss14/ST14 protein expressed in various human cancer cells. Humanized antibody huAb3F3 maintained the specificity and reduced the migration of human breast cancer cells in vitro. CONCLUSION: Our study demonstrates that Prss14/ST14 is an important target for modulating metastasis. Our newly developed hybridoma mAbs and humanized antibody can be further developed as new promising candidates for the use in diagnosis and in immunotherapy of human metastatic breast cancer.

Two golden times in two-step contagion models: A nonlinear map approach.

The two-step contagion model is a simple toy model for understanding pandemic outbreaks that occur in the real world. The model takes into account that a susceptible person either gets immediately infected or weakened when getting into contact with an infectious one. As the number of weakened people increases, they eventually can become infected in a short time period and a pandemic outbreak occurs. The time required to reach such a pandemic outbreak allows for intervention and is often called golden time. Understanding the size-dependence of the golden time is useful for controlling pandemic outbreak. Using an approach based on a nonlinear mapping, here we find that there exist two types of golden times in the two-step contagion model, which scale as O(N^{1/3}) and O(N^{ζ}) with the system size N on Erdos-Rényi networks, where the measured ζ is slightly larger than 1/4. They are distinguished by the initial number of infected nodes, o(N) and O(N), respectively. While the exponent 1/3 of the N-dependence of the golden time is universal even in other models showing discontinuous transitions induced by cascading dynamics, the measured ζ exponents are all close to 1/4 but show model-dependence. It remains open whether or not ζ reduces to 1/4 in the asymptotically large-N limit. Our method can be applied to several models showing a hybrid percolation transition and gives insight into the origin of the two golden times.

SHPRH as a new player in ribosomal RNA transcription and its potential role in homeostasis of ribosomal DNA repeats.

There are hundreds of copies of rDNA repeats in mammalian chromosomes and the ratio of active, poised, or inactive rDNA is regulated in epigenetic manners. Recent studies demonstrated that a post-DNA replication repair enzyme, SHPRH affects rRNA transcription by recognizing epigenetic markers on rDNA promoters and unveiled potential links between DNA repair and ribosome biogenesis. This study suggests that SHPRH could be a link between mTOR-mediated epigenetic regulations and rRNA transcription, while concomitantly affecting genomic integrity.

Critical behavior of a two-step contagion model with multiple seeds.

A two-step contagion model with a single seed serves as a cornerstone for understanding the critical behaviors and underlying mechanism of discontinuous percolation transitions induced by cascade dynamics. When the contagion spreads from a single seed, a cluster of infected and recovered nodes grows without any cluster merging process. However, when the contagion starts from multiple seeds of O(N) where N is the system size, a node weakened by a seed can be infected more easily when it is in contact with another node infected by a different pathogen seed. This contagion process can be viewed as a cluster merging process in a percolation model. Here we show analytically and numerically that when the density of infectious seeds is relatively small but O(1), the epidemic transition is hybrid, exhibiting both continuous and discontinuous behavior, whereas when it is sufficiently large and reaches a critical point, the transition becomes continuous. We determine the full set of critical exponents describing the hybrid and the continuous transitions. Their critical behaviors differ from those in the single-seed case.

Universal mechanism for hybrid percolation transitions.

Hybrid percolation transitions (HPTs) induced by cascading processes have been observed in diverse complex systems such as k-core percolation, breakdown on interdependent networks and cooperative epidemic spreading models. Here we present the microscopic universal mechanism underlying those HPTs. We show that the discontinuity in the order parameter results from two steps: a durable critical branching (CB) and an explosive, supercritical (SC) process, the latter resulting from large loops inevitably present in finite size samples. In a random network of N nodes at the transition the CB process persists for O(N 1/3) time and the remaining nodes become vulnerable, which are then activated in the short SC process. This crossover mechanism and scaling behavior are universal for different HPT systems. Our result implies that the crossover time O(N 1/3) is a golden time, during which one needs to take actions to control and prevent the formation of a macroscopic cascade, e.g., a pandemic outbreak.

Salinomycin Induces Reactive Oxygen Species and Apoptosis in Aggressive Breast Cancer Cells as Mediated with Regulation of Autophagy.

BACKGROUND/AIM: Chemotherapy is a critical option for cancer treatment. However, consistent exposure to chemotherapeutic drugs promotes chemoresistance in cancer cells through diverse mechanisms. Accordingly, we investigated whether salinomycin, a monocarboxylic ionophore, could induce apoptosis in aggressive breast cancer cells or not, as well as its underlying mechanism. MATERIALS AND METHODS: Using salinomycin on two breast cancer cell lines, MCF-7 cells and MDA-MB-231 cells, cell viability, annexin V/propidium iodide staining, acridine orange staining, caspase-3/9 activity, reactive oxygen species (ROS) and mitochondrial membrane potential (MMP) were assayed. RESULTS: In this study, salinomycin induced apoptosis and autophagy in MDA-MB-231 cells. Salinomycin-mediated ROS production led to mitochondrial dysfunction in MDA-MB-231 cells. Interestingly, treatment of N-acetyl-L-cysteine (NAC), a scavenger of ROS, attenuated salinomycin-induced apoptosis and autophagy. Moreover, autophagy inhibition is involved in acceleration of apoptosis induced by salinomycin. CONCLUSION: Salinomycin induced apoptosis and ROS production, that were blocked by autophagy, thus resulting in protecting cancer cells. This crosstalk of two different physiological responses (autophagy and apoptosis) induced by salinomycin might play pivotal roles in the relationship between autophagy and apoptosis of cancer cells.

SHPRH regulates rRNA transcription by recognizing the histone code in an mTOR-dependent manner.

Many DNA repair proteins have additional functions other than their roles in DNA repair. In addition to catalyzing PCNA polyubiquitylation in response to the stalling of DNA replication, SHPRH has the additional function of facilitating rRNA transcription by localizing to the ribosomal DNA (rDNA) promoter in the nucleoli. SHPRH was recruited to the rDNA promoter using its plant homeodomain (PHD), which interacts with histone H3 when the fourth lysine of H3 is not trimethylated. SHPRH enrichment at the rDNA promoter was inhibited by cell starvation, by treatment with actinomycin D or rapamycin, or by depletion of CHD4. SHPRH also physically interacted with the RNA polymerase I complex. Taken together, we provide evidence that SHPRH functions in rRNA transcription through its interaction with histone H3 in a mammalian target of rapamycin (mTOR)-dependent manner.

Mixed-order phase transition in a two-step contagion model with a single infectious seed.

Percolation is known as one of the most robust continuous transitions, because its occupation rule is intrinsically local. As one of the ways to break the robustness, occupation is allowed to more than one species of particles and they occupy cooperatively. This generalized percolation model undergoes a discontinuous transition. Here we investigate an epidemic model with two contagion steps and characterize its phase transition analytically and numerically. We find that even though the order parameter jumps at a transition point r_{c}, then increases continuously, it does not exhibit any critical behavior: the fluctuations of the order parameter do not diverge at r_{c}. However, critical behavior appears in mean outbreak size, which diverges at the transition point in a manner that the ordinary percolation shows. Such a type of phase transition is regarded as a mixed-order phase transition. We also obtain scaling relations of cascade outbreak statistics when the order parameter jumps at r_{c}.

Critical phenomena of a hybrid phase transition in cluster merging dynamics.

Recently, a hybrid percolation transition (HPT) that exhibits both a discontinuous transition and critical behavior at the same transition point has been observed in diverse complex systems. While the HPT induced by avalanche dynamics has been studied extensively, the HPT induced by cluster merging dynamics (HPT-CMD) has received little attention. Here, we aim to develop a theoretical framework for the HPT-CMD. We find that two correlation-length exponents are necessary for characterizing the giant cluster and finite clusters separately. The conventional formula of the fractal dimension in terms of the critical exponents is not valid. Neither the giant nor finite clusters are fractals, but they have fractal boundaries. A finite-size scaling method for the HPT-CMD is also introduced.

Hybrid phase transition into an absorbing state: Percolation and avalanches.

Interdependent networks are more fragile under random attacks than simplex networks, because interlayer dependencies lead to cascading failures and finally to a sudden collapse. This is a hybrid phase transition (HPT), meaning that at the transition point the order parameter has a jump but there are also critical phenomena related to it. Here we study these phenomena on the Erdos-Rényi and the two-dimensional interdependent networks and show that the hybrid percolation transition exhibits two kinds of critical behaviors: divergence of the fluctuations of the order parameter and power-law size distribution of finite avalanches at a transition point. At the transition point global or "infinite" avalanches occur, while the finite ones have a power law size distribution; thus the avalanche statistics also has the nature of a HPT. The exponent ß_{m} of the order parameter is 1/2 under general conditions, while the value of the exponent γ_{m} characterizing the fluctuations of the order parameter depends on the system. The critical behavior of the finite avalanches can be described by another set of exponents, ß_{a} and γ_{a}. These two critical behaviors are coupled by a scaling law: 1-ß_{m}=γ_{a}.

Critical behavior of k-core percolation: Numerical studies.

k-core percolation has served as a paradigmatic model of discontinuous percolation for a long time. Recently it was revealed that the order parameter of k-core percolation of random networks additionally exhibits critical behavior. Thus k-core percolation exhibits a hybrid phase transition. Unlike the critical behaviors of ordinary percolation that are well understood, those of hybrid percolation transitions have not been thoroughly understood yet. Here, we investigate the critical behavior of k-core percolation of Erdos-Rényi networks. We find numerically that the fluctuations of the order parameter and the mean avalanche size diverge in different ways. Thus, we classify the critical exponents into two types: those associated with the order parameter and those with finite avalanches. The conventional scaling relations hold within each set, however, these two critical exponents are coupled. Finally we discuss some universal features of the critical behaviors of k-core percolation and the cascade failure model on multiplex networks.

Quantifying discrepancies in opinion spectra from online and offline networks.

Online social media such as Twitter are widely used for mining public opinions and sentiments on various issues and topics. The sheer volume of the data generated and the eager adoption by the online-savvy public are helping to raise the profile of online media as a convenient source of news and public opinions on social and political issues as well. Due to the uncontrollable biases in the population who heavily use the media, however, it is often difficult to measure how accurately the online sphere reflects the offline world at large, undermining the usefulness of online media. One way of identifying and overcoming the online-offline discrepancies is to apply a common analytical and modeling framework to comparable data sets from online and offline sources and cross-analyzing the patterns found therein. In this paper we study the political spectra constructed from Twitter and from legislators' voting records as an example to demonstrate the potential limits of online media as the source for accurate public opinion mining, and how to overcome the limits by using offline data simultaneously.

Forest-fire model as a supercritical dynamic model in financial systems.

Recently large-scale cascading failures in complex systems have garnered substantial attention. Such extreme events have been treated as an integral part of self-organized criticality (SOC). Recent empirical work has suggested that some extreme events systematically deviate from the SOC paradigm, requiring a different theoretical framework. We shed additional theoretical light on this possibility by studying financial crisis. We build our model of financial crisis on the well-known forest fire model in scale-free networks. Our analysis shows a nontrivial scaling feature indicating supercritical behavior, which is independent of system size. Extreme events in the supercritical state result from bursting of a fat bubble, seeds of which are sown by a protracted period of a benign financial environment with few shocks. Our findings suggest that policymakers can control the magnitude of financial meltdowns by keeping the economy operating within reasonable duration of a benign environment.

Efficient algorithm to compute mutually connected components in interdependent networks.

Mutually connected components (MCCs) play an important role as a measure of resilience in the study of interdependent networks. Despite their importance, an efficient algorithm to obtain the statistics of all MCCs during the removal of links has thus far been absent. Here, using a well-known fully dynamic graph algorithm, we propose an efficient algorithm to accomplish this task. We show that the time complexity of this algorithm is approximately O(N(1.2)) for random graphs, which is more efficient than O(N(2)) of the brute-force algorithm. We confirm the correctness of our algorithm by comparing the behavior of the order parameter as links are removed with existing results for three types of double-layer multiplex networks. We anticipate that this algorithm will be used for simulations of large-size systems that have been previously inaccessible.

A novel role for the mono-ADP-ribosyltransferase PARP14/ARTD8 in promoting homologous recombination and protecting against replication stress.

Genomic instability, a major hallmark of cancer cells, is caused by incorrect or ineffective DNA repair. Many DNA repair mechanisms cooperate in cells to fight DNA damage, and are generally regulated by post-translational modification of key factors. Poly-ADP-ribosylation, catalyzed by PARP1, is a post-translational modification playing a prominent role in DNA repair, but much less is known about mono-ADP-ribosylation. Here we report that mono-ADP-ribosylation plays an important role in homologous recombination DNA repair, a mechanism essential for replication fork stability and double strand break repair. We show that the mono-ADP-ribosyltransferase PARP14 interacts with the DNA replication machinery component PCNA and promotes replication of DNA lesions and common fragile sites. PARP14 depletion results in reduced homologous recombination, persistent RAD51 foci, hypersensitivity to DNA damaging agents and accumulation of DNA strand breaks. Our work uncovered PARP14 as a novel factor required for mitigating replication stress and promoting genomic stability.

Microscopic modelling circadian and bursty pattern of human activities.

Recent studies for a wide range of human activities such as email communication, Web browsing, and library visiting, have revealed the bursty nature of human activities. The distribution of inter-event times (IETs) between two consecutive human activities exhibits a heavy-tailed decay behavior and the oscillating pattern with a one-day period, reflective of the circadian pattern of human life. Even though a priority-based queueing model was successful as a basic model for understanding the heavy-tailed behavior, it ignored important ingredients, such as the diversity of individual activities and the circadian pattern of human life. Here, we collect a large scale of dataset which contains individuals' time stamps when articles are posted on blog posts, and based on which we construct a theoretical model which can take into account of both ignored ingredients. Once we identify active and inactive time intervals of individuals and remove the inactive time interval, thereby constructing an ad hoc continuous time domain. Therein, the priority-based queueing model is applied by adjusting the arrival and the execution rates of tasks by comparing them with the activity data of individuals. Then, the obtained results are transferred back to the real-time domain, which produces the oscillating and heavy-tailed IET distribution. This microscopic model enables us to develop theoretical understanding towards more empirical results.

Branching process approach for Boolean bipartite networks of metabolic reactions.

The branching process (BP) approach has been successful in explaining the avalanche dynamics in complex networks. However, its applications are mainly focused on unipartite networks, in which all nodes are of the same type. Here, motivated by a need to understand avalanche dynamics in metabolic networks, we extend the BP approach to a particular bipartite network composed of Boolean AND and OR logic gates. We reduce the bipartite network into a unipartite network by integrating out OR gates and obtain the effective branching ratio for the remaining AND gates. Then the standard BP approach is applied to the reduced network, and the avalanche-size distribution is obtained. We test the BP results with simulations on the model networks and two microbial metabolic networks, demonstrating the usefulness of the BP approach.