The Experts below are selected from a list of 11523 Experts worldwide ranked by ideXlab platform
Jason E Shoemaker - One of the best experts on this subject based on the ideXlab platform.
-
a dual Controllability Analysis of influenza virus host protein protein interaction networks for antiviral drug target discovery
BMC Bioinformatics, 2019Co-Authors: Emily E Ackerman, John F Alcorn, Takeshi Hase, Jason E ShoemakerAbstract:Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through Controllability Analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part Controllability Analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, Controllability analyses aim to identify key regulating host factors of the infected cell’s progression. This knowledge can be utilized in further biological Analysis to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and Controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in Controllability, therefore playing important roles in cell behavior during infection. Functional Analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, Controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network Analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and Controllability analyses may be valuable for future efforts in drug target discovery.
-
a dual Controllability Analysis of influenza virus host protein protein interaction networks for antiviral drug target discovery
bioRxiv, 2018Co-Authors: Emily E Ackerman, John F Alcorn, Takeshi Hase, Jason E ShoemakerAbstract:Host factors of influenza virus replication often are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through Controllability Analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here we complete a two-part Controllability Analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and Controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in Controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell.
Junichi Imura - One of the best experts on this subject based on the ideXlab platform.
-
Controllability Analysis of biosystems based on piecewise affine systems approach
IEEE Transactions on Circuits and Systems I-regular Papers, 2009Co-Authors: Shunichi Azuma, E Yanagisawa, Junichi ImuraAbstract:This paper discusses the Controllability problem of biosystems based on the piecewise-affine system model representation. First, we consider what kind of Controllability problems are useful for analyzing and controlling biosystems, and then the controllable set problem, that is, a problem of finding a state set in which each state can be driven to a given target state, is formulated. It is shown that this problem will be very useful for many kinds of problems on control of biosystems such as the input allocation problem and the stabilization problem. Next, based on our previous probabilistic Controllability Analysis technique for hybrid dynamical systems, a more sophisticated method for solving the above complex problem in an approximated and suitable way for biosystem Analysis is proposed. Finally, the proposed framework is applied to the quorum sensing system of the pathogen Pseudomonas aeruginosa for explaining how it is formulated and what solutions are obtained.
-
polynomial time probabilistic Controllability Analysis of discrete time piecewise affine systems
IEEE Transactions on Automatic Control, 2007Co-Authors: Shunichi Azuma, Junichi ImuraAbstract:This paper proposes a probabilistic approach to the Controllability Analysis for discrete-time piecewise affine (PWA) systems. Three kinds of randomized algorithms, which are based on random sampling of the mode sequence and/or the initial state, for determining with a probabilistic accuracy if the system is controllable are presented: a positive one-sided error algorithm, a negative one-sided error algorithm, and a two-sided error algorithm. It is proven that these are polynomial-time algorithms with respect to several variables of the problem. It is also shown with some examples, for which it is hopeless to check the Controllability in a deterministic way, that these algorithms are efficient.
Emily E Ackerman - One of the best experts on this subject based on the ideXlab platform.
-
a dual Controllability Analysis of influenza virus host protein protein interaction networks for antiviral drug target discovery
BMC Bioinformatics, 2019Co-Authors: Emily E Ackerman, John F Alcorn, Takeshi Hase, Jason E ShoemakerAbstract:Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through Controllability Analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part Controllability Analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, Controllability analyses aim to identify key regulating host factors of the infected cell’s progression. This knowledge can be utilized in further biological Analysis to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and Controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in Controllability, therefore playing important roles in cell behavior during infection. Functional Analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, Controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network Analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and Controllability analyses may be valuable for future efforts in drug target discovery.
-
a dual Controllability Analysis of influenza virus host protein protein interaction networks for antiviral drug target discovery
bioRxiv, 2018Co-Authors: Emily E Ackerman, John F Alcorn, Takeshi Hase, Jason E ShoemakerAbstract:Host factors of influenza virus replication often are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through Controllability Analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here we complete a two-part Controllability Analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and Controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in Controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell.
Jinsong Zhao - One of the best experts on this subject based on the ideXlab platform.
-
systematic Controllability Analysis for chemical processes
Aiche Journal, 2012Co-Authors: Zhihong Yuan, Nan Zhang, Bingzhen Chen, Jinsong ZhaoAbstract:Modern chemical industrial processes are becoming more and more integrated and consist of multiple interconnected nonlinear process units. These strong interactions profoundly complicate a system's inherent properties and further alter the plant-wide process dynamics. This may lead to a poor control performance and cause plant-wide operability problems. To ensure entire processes run robustly and safely, with considerable profitability, it is crucial to recognize the inherent characteristics that can jeopardize Controllability and process behavior at the early design stage. With a focus on inherently safer designs, from a plant-wide perspective, a systematic method for chemical processes Controllability Analysis is addressed in this study. In the proposed framework, based on open-loop stability/instability and minimum/nonminimum-phase behavior, the entire operating zone of the process can be categorized into distinct subregions with different inherent properties. Variations in the inherent characteristics of a plant-wide process with the operation and design conditions, over the feasible operation region, can be probed and analyzed. An attempt of this framework is made to illustrate how to clarify the roots of the poor Controllability that arise in the design and operation of a large scale chemical process, and the results can provide guidance for both deciding the optimal operation conditions and selecting the most suitable control structure. Singularity theory is also applied in the framework to improve the computational efficiency. The framework is illustrated with two case studies. One involves a reactor-external heat exchanger network and the other a more complex plant-wide process, comprising a reactor, an extractor, and a distillation column. © 2012 American Institute of Chemical Engineers AIChE J, 58: 3096–3109, 2012
-
Controllability Analysis for the liquid phase catalytic oxidation of toluene to benzoic acid
Chemical Engineering Science, 2011Co-Authors: Zhihong Yuan, Bingzhen Chen, Jinsong ZhaoAbstract:Abstract The liquid-phase catalytic oxidation of toluene is the main industrial commercial process for producing benzoic acid. It is a strongly exothermic and highly nonlinear process that exhibits poor Controllability characteristics with input/output multiplicity and non-minimum phase behavior. Focusing on inherent safety, this study explores the open and closed-loop Controllability of this process. The framework contains two parts. First, an approach for analyzing the stability of zero dynamics of the system is used to investigate the phase behavior of the process and is selected as an open-loop indicator for Controllability. Second, based on the model predictive controller, closed-loop dynamic simulation, including set-point tracking and disturbances rejection, is performed to illustrate the dynamic performance in various sub-regions with different Controllability characteristics. The results of the dynamic simulation confirm everything predicted by the open-loop Controllability Analysis. The outcomes are expected to guide realistic industrial operation and process control system design. An attempt is made with the help of this liquid-phase oxidation process to show how to clarify and deal with the causes of the complex phenomena that arise in the operation and control of the chemical processes.
-
an overview on Controllability Analysis of chemical processes
Aiche Journal, 2011Co-Authors: Zhihong Yuan, Bingzhen Chen, Jinsong ZhaoAbstract:Controllability is one of the most important aspects of chemical process operability, because it can be used to assess the attainable operation of a given process and improve its dynamic performance. The purpose of this article is to outline the main methodologies that have been developed to deal with the assessment of process Controllability and the improvement of its Controllability characteristics. Several existing Controllability assessment methods are reviewed and discussed. For improving the Controllability characteristic of a process, there are two main design methods: the optimization-based method and the Controllability indices-based anticipating sequential method. Advantages and disadvantages of these techniques are discussed. It has been emphasized that bifurcation Analysis, as a powerful nonlinear Analysis tool, could provide important guidance for making processes more controllable by eliminating or avoiding some undesirable behaviors of processes. Further challenges and developments in the field of process Controllability are identified. © 2010 American Institute of Chemical Engineers AIChE J, 2010
Jose C Nacher - One of the best experts on this subject based on the ideXlab platform.
-
probabilistic critical Controllability Analysis of protein interaction networks integrating normal brain ageing gene expression profiles
International Journal of Molecular Sciences, 2021Co-Authors: Eimi Yamaguchi, Tatsuya Akutsu, Jose C NacherAbstract:Recently, network Controllability studies have proposed several frameworks for the control of large complex biological networks using a small number of life molecules. However, age-related changes in the brain have not been investigated from a Controllability perspective. In this study, we compiled the gene expression profiles of four normal brain regions from individuals aged 20–99 years and generated dynamic probabilistic protein networks across their lifespan. We developed a new algorithm that efficiently identified critical proteins in probabilistic complex networks, in the context of a minimum dominating set Controllability model. The results showed that the identified critical proteins were significantly enriched with well-known ageing genes collected from the GenAge database. In particular, the enrichment observed in replicative and premature senescence biological processes with critical proteins for male samples in the hippocampal region led to the identification of possible new ageing gene candidates.
-
network Controllability Analysis of intracellular signalling reveals viruses are actively controlling molecular systems
Scientific Reports, 2019Co-Authors: Vandana Ravindran, Jose C Nacher, Tatsuya Akutsu, Masayuki Ishitsuka, Adrian Osadcenco, V Sunitha, Ganesh Bagler, Jeanmarc Schwartz, David RobertsonAbstract:In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network Controllability Analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high ‘control centrality’ of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.
-
critical Controllability Analysis of directed biological networks using efficient graph reduction
Scientific Reports, 2017Co-Authors: Masayuki Ishitsuka, Tatsuya Akutsu, Jose C NacherAbstract:Network science has recently integrated key concepts from control theory and has applied them to the Analysis of the Controllability of complex networks. One of the proposed frameworks uses the Minimum Dominating Set (MDS) approach, which has been successfully applied to the identification of cancer-related proteins and in analyses of large-scale undirected networks, such as proteome-wide protein interaction networks. However, many real systems are better represented by directed networks. Therefore, fast algorithms are required for the application of MDS to directed networks. Here, we propose an algorithm that utilises efficient graph reduction to identify critical control nodes in large-scale directed complex networks. The algorithm is 176-fold faster than existing methods and increases the computable network size to 65,000 nodes. We then applied the developed algorithm to metabolic pathways consisting of 70 plant species encompassing major plant lineages ranging from algae to angiosperms and to signalling pathways from C. elegans, D. melanogaster and H. sapiens. The Analysis not only identified functional pathways enriched with critical control molecules but also showed that most control categories are largely conserved across evolutionary time, from green algae and early basal plants to modern angiosperm plant lineages.