Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks

Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design

An Approach for Determining and Measuring Network Hierarchy Applied to Comparing the Phosphorylome and the Regulome

Many biological networks naturally form a hierarchy with a preponderance of downward information flow. In this study, we define a score to quantify the degree of hierarchy in a network and develop a simulated-annealing algorithm to maximize the hierarchical score globally over a network. We apply our algorithm to determine the hierarchical structure of the phosphorylome in detail and investigate the correlation between its hierarchy and kinase properties. We also compare it to the regulatory network, finding that the phosphorylome is more hierarchical than the regulome

Managing Systemic Risk in Legal Systems

The American legal system has proven remarkably robust even in the face of vast and often tumultuous political, social, economic, and technological change. Yet our system of law is not unlike other complex social, biological, and physical systems in exhibiting local fragility in the midst of its global robustness. Understanding how this “robust yet fragile” (RYF) dilemma operates in legal systems is important to the extent law is expected to assist in managing systemic risk—the risk of large local or even system-wide failures—in other social systems. Indeed, legal system failures have been blamed as partly responsible for disasters such as the recent financial system crisis and the Deepwater Horizon oil spill. If we cannot effectively manage systemic risk within the legal system, how can we expect the legal system to manage systemic risk elsewhere? This Article employs a complexity science model of the RYF dilemma to explore why systemic risk persists in legal systems and how to manage it. Part I defines complexity in the context of the institutions and instruments that make up the legal system. Part II defines the five dimensions of robustness that support functionality of the legal system: (1) reliability, (2) efficiency, (3) scalability, (4) modularity, and (5) evolvability. Part III then defines system fragility by examining the internal and external constraints that impede legal system robustness and the fail-safe system control strategies for managing their effects. With those basic elements of the RYF dilemma model in place, Part IV defines systemic risk and explores the paradoxical role of increasingly organized complexity brought about by fail-safe strategies as a source of legal system failure. There is no way around the RYF dilemma—some degree of systemic risk is inherent in any complex adaptive system—but the balance between robustness and fragility is something we can hope to influence. To explore how, Part V applies the RYF dilemma model to a concrete systemic risk management context—oil drilling in the deep Gulf of Mexico. The legal regime governing offshore oil exploration and extraction has been blamed as contributing to the set of failures that led to the catastrophic Deepwater Horizon spill and is at the center of reform initiatives. Using this case study, I argue that the RYF dilemma model provides valuable insights into how legal systems fail and how to manage legal systemic risk

Managing Systemic Risk in Legal Systems

The American legal system has proven remarkably robust even in the face vast and often tumultuous political, social, economic, and technological change. Yet our system of law is not unlike other complex social, biological, and physical systems in exhibiting local fragility in the midst of its global robustness. Understanding how this “robust yet fragile†(RYF) dilemma operates in legal systems is important to the extent law is expected to assist in managing systemic risk — the risk of large local or even system-wide failures — in other social systems. Indeed, legal system failures have been blamed as partly responsible for disasters such as the recent financial system crisis and the Deepwater Horizon oil spill. If we cannot effectively manage systemic risk within the legal system, however, how can we expect the legal system to manage systemic risk elsewhere? This Article employs a complexity science model of the RYF dilemma to explore why systemic risk persists in legal systems and how to manage it. Part I defines complexity in the context of the institutions and instruments that make up the legal system. Part II defines the five dimensions of robustness that support functionality of the legal system: (1) reliability; (2) efficiency; (3) scalability; (4) modularity, and (5) evolvability. Part III then defines system fragility, examining the internal and external constraints that impede legal system robustness and the fail-safe system control strategies for managing their effects. With those basic elements of the RYF dilemma model in place, Part IV defines systemic risk, exploring the paradoxical role of increasingly organized complexity brought about by fail-safe strategies as a source of legal system failure. There is no way around the RYF dilemma — some degree of systemic risk is inherent in any complex adaptive system — but the balance between robustness and fragility is something we can hope to influence. To explore how, Part V applies the RYF dilemma model to a concrete systemic risk management context — oil drilling in the deep Gulf of Mexico. The legal regime governing offshore oil exploration and extraction has been blamed as contributing to the set of failures that led to the catastrophic Deepwater Horizon spill and is at the center of reform initiatives. Using this case study, I argue that the RYF dilemma model provides valuable insights into how legal systems fail and how to manage legal systemic risk

HVDC transmission : technology review, market trends and future outlook

HVDC systems are playing an increasingly significant role in energy transmission due to their technical and economic superiority over HVAC systems for long distance transmission. HVDC is preferable beyond 300–800 km for overhead point-to-point transmission projects and for the cable based interconnection or the grid integration of remote offshore wind farms beyond 50–100 km. Several HVDC review papers exist in literature but often focus on specific geographic locations or system components. In contrast, this paper presents a detailed, up-to-date, analysis and assessment of HVDC transmission systems on a global scale, targeting expert and general audience alike. The paper covers the following aspects: technical and economic comparison of HVAC and HVDC systems; investigation of international HVDC market size, conditions, geographic sparsity of the technology adoption, as well as the main suppliers landscape; and high-level comparisons and analysis of HVDC system components such as Voltage Source Converters (VSCs) and Line Commutated Converters (LCCs), etc. The presented analysis are supported by practical case studies from existing projects in an effort to reveal the complex technical and economic considerations, factors and rationale involved in the evaluation and selection of transmission system technology for a given project. The contemporary operational challenges such as the ownership of Multi-Terminal DC (MTDC) networks are also discussed. Subsequently, the required development factors, both technically and regulatory, for proper MTDC networks operation are highlighted, including a future outlook of different HVDC system components. Collectively, the role of HVDC transmission in achieving national renewable energy targets in light of the Paris agreement commitments is highlighted with relevant examples of potential HVDC corridors

Active modules of bipartite metabolic network

The thesis investigates the problem of identifying active modules of bipartite metabolic network. We devise a method of motif projection, and the extraction of clusters from active modules based on the concentration of active-motifs in the network. Our results reveal the existence of hierarchical structure. We model regulation of metabolism as an interaction between a metabolic network and a gene regulatory network in the form of interconnected network. We devise two module detection algorithms for interconnected network to evaluate the molecular changes of activity that are associated with cellular responses. The first module detection algorithm is formulated based on information map of random walks that is capable of inferring modules based on topological and activity of nodes. The proposed algorithm has faster execution time and produces comparably close performance as previous work. The second algorithm takes into account of strong regulatory activities in the gene regulatory layer to support the active regions in the metabolic layer. The integration of gene information allows the formation of large modules with better recall. In conclusion, our findings indicate the importance of no longer modelling complex biological systems as a single network, but to view them as flow of information of multiple molecular spaces

Computational Labeling, Partitioning, and Balancing of Molecular Networks

Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional behaviors. We build three computational models to label, partition, and balance molecular networks using modern machine learning techniques. (1) Due to the incompleteness of protein functional annotation, we develop AptRank, an adaptive PageRank model for protein function prediction on bilayer networks. By integrating Gene Ontology (GO) hierarchy with protein-protein interaction network, our AptRank outperforms four state-of-the-art methods in a comprehensive evaluation using benchmark datasets. (2) We next extend our AptRank into a network partitioning method, BioSweeper, to identify functional network modules in which molecules share similar functions and also densely connect to each other. Compared to traditional network partitioning methods using only network connections, BioSweeper, which integrates the GO hierarchy, can automatically identify functionally enriched network modules. (3) Finally, we conduct a differential interaction analysis, namely difFBA, on protein-protein interaction networks by simulating protein fluxes using flux balance analysis (FBA). We test difFBA using quantitative proteomic data from colon cancer, and demonstrate that difFBA offers more insights into functional changes in molecular behavior than does protein quantity changes alone. We conclude that our integrative network model increases the observational dimensions of complex biological systems, and enables us to more deeply understand the causal relationships between genotypes and phenotypes