Identification of proteins associated with bast fiber growth of ramie by differential proteomic analysis

Background Ramie is an important fiber-producing crop in China, and its fibers are widely used as textile materials. Fibers contain specialized secondary cellular walls that are mainly composed of cellulose, hemicelluloses, and lignin. Understanding the mechanism underlying the secondary wall biosynthesis of fibers will benefit the improvement of fiber yield and quality in ramie. Results Here, we performed a proteomic analysis of the bark from the top and middle parts of the stem, where fiber growth is at different stages. We identified 6971 non-redundant proteins from bast bark. Proteomic comparison revealed 983 proteins with differential expression between the two bark types. Of these 983 proteins, 46 were identified as the homolog of known secondary wall biosynthetic proteins of Arabidopsis, indicating that they were potentially associated with fiber growth. Then, we proposed a molecular model for the secondary wall biosynthesis of ramie fiber. Furthermore, interaction analysis of 46 candidate proteins revealed two interacting networks that consisted of eight cellulose biosynthetic enzymes and seven lignin biosynthetic proteins, respectively. Conclusion This study sheds light on the proteomic basis underlying bast fiber growth in ramie, and the identification of many candidates associated with fiber growth provides important basis for understanding the fiber growth in this crop.

A mean-field approach to self-interacting networks, convergence and regularity

The propagation of chaos property for a system of interacting particles, describing the spatial evolution of a network of interacting filaments is studied. The creation of a network of mycelium is analyzed as representative case, and the generality of the modeling choices are discussed. Convergence of the empirical density for the particle system to its mean-field limit is proved, and a result of regularity for the solution is presented.

Synchronization through uncorrelated noise in excitatory-inhibitory networks

Gamma rhythms play a major role in many different processes in the brain, such as attention, working memory and sensory processing. While typically considered detrimental, counterintuitively noise can sometimes have beneficial effects on communication and information transfer. Recently, Meng and Riecke showed that synchronization of interacting networks of inhibitory neurons increases while synchronization within these networks decreases when neurons are subject to uncorrelated noise. However, experimental and modelling studies point towards an important role of the pyramidal-interneuronal network gamma (PING) mechanism in the cortex. Therefore, we investigated the effect of uncorrelated noise on the communication between excitatory-inhibitory networks producing gamma oscillations via a PING mechanism. Our results suggest that synaptic noise can have a supporting role in facilitating inter-regional communication and that noise-induced synchronization between networks is generated via a different mechanism than when synchronization is mediated by strong synaptic coupling. Noise-induced synchronization is achieved by lowering synchronization within networks which allows the respective other network to impose its own gamma rhythm resulting in synchronization between networks.

The landscape of PDK1 in Breast Cancer

Given that 3-Phosphoinositide-dependent kinase 1 (PDK1) plays a crucial role in malignant biological behaviors of a wide-range of cancers, we further review the influence of PDK1 in breast cancer (BC). First, we describe the power of PDK1 in cellular behaviors and extensively demonstrate the interacting networks of PDK1 via PI3K-dependent/ PI3K-independent pathway. Then we enlighten the roles of PDK1 in carcinogenesis, growth and survival, metastasis, and chemoresistance in BC cells. More important, we sort the current preclinical or clinical trials of PDK1 targeted therapy in BC and find that even though at present no selective PDK1 inhibitor is available for BC therapy, but the combination trials of PDK1 targeted therapy and other agents have demonstrated some benefit. Thus, there is increasing anticipations that PDK1 targeted therapy will have its space in future therapeutic concepts of BC, and we hope to feature PDK1 in BC to the clinic and bring the new promising to patients for targeted therapies.

Emergence of a smooth interface from growth of a dendritic network against a mechanosensitive contractile material

Structures and machines require smoothening of raw materials. Self-organized smoothening guides cell and tissue morphogenesis, and is relevant to advanced manufacturing. Across the syncytial Drosophila embryo surface, smooth interfaces form between expanding Arp2/3-based actin caps and surrounding actomyosin networks, demarcating the circumferences of nascent dome-like compartments used for pseudo-cleavage. We found that forming a smooth and circular boundary of the surrounding actomyosin domain requires Arp2/3 in vivo. To dissect the physical basis of this requirement, we reconstituted the interacting networks using node-based models. In simulations of actomyosin networks with local clearances in place of Arp2/3 domains, rough boundaries persisted when myosin contractility was low. With addition of expanding Arp2/3 network domains, myosin domain boundaries failed to smoothen, but accumulated myosin nodes and tension. After incorporating actomyosin mechanosensitivity, Arp2/3 network growth locally induced a surrounding contractile actomyosin ring that smoothened the interface between the cytoskeletal domains, an effect also evident in vivo. In this way, a smooth structure can emerge from the lateral interaction of irregular active materials.

Optimal resilience of modular interacting networks

Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly. We find both analytically and numerically that the location of the percolation phase transition varies nonmonotonically with the fraction of interconnected nodes when the total number of interconnecting links remains fixed. Furthermore, there exists an optimal fraction r* of interconnected nodes where the system becomes optimally resilient and is able to withstand more damage. Our results suggest that, although the exact location of the optimal r* varies based on the coupling patterns, for all coupling patterns, there exists such an optimal point. Our findings provide a deeper understanding of network resilience and show how networks can be optimized based on their specific coupling patterns.

Constraints in Clinical Cardiology and Personalized Medicine: Interrelated Concepts in Clinical Cardiology

Systems biology is established as an integrative computational analysis methodology with practical and theoretical applications in clinical cardiology. The integration of genetic and molecular components of a disease produces interacting networks, modules and phenotypes with clinical applications in complex cardiovascular entities. With the holistic principle of systems biology, some of the features of complexity and natural progression of cardiac diseases are approached and explained. Two important interrelated holistic concepts of systems biology are described; the emerging field of personalized medicine and the constraint-based thinking with downward causation. Constraints in cardiovascular diseases embrace three scientific fields related to clinical cardiology: biological and medical constraints; constraints due to limitations of current technology; and constraints of general resources for better medical coverage. Systems healthcare and personalized medicine are connected to the related scientific fields of: ethics and legal status; data integration; taxonomic revisions; policy decisions; and organization of human genomic data.

Ontology-Based Process Modelling-with Examples of Physical Topologies

Reductionism and splitting application domain into disciplines and identify the smallest required model-granules, termed ”basic entity” combined with systematic construction of the behaviour equations for the basic entities, yields a systematic approach to process modelling. We do not aim toward a single modelling domain, but we enable to address specific application domains and object inheritance. We start with reductionism and demonstrate how the basic entities are depending on the targeted application domain. We use directed graphs to capture process models, and we introduce a new concept, which we call ”tokens” that enables us to extend the context beyond physical systems. The network representation is hierarchical so as to capture complex systems. The interacting basic entities are defined in the leave nodes of the hierarchy, making the overall model the interacting networks in the leave nodes. Multi-disciplinary and multi-scale models result in a network of networks. We identify two distinct network communication ports, namely ports that exchange tokens and ports that transfer information of tokens in accumulators. An ontology captures the structural elements and the applicable rules and defines the syntax to establish the behaviour equations. Linking the behaviours to the basic entities defines the alphabet of a graphical language. We use this graphical language to represent processes, which has proven to be efficient and valuable. A set of three examples demonstrates the power of the graphical language. The Process Modelling framework (ProMo) implements the ontology-centred approach to process modelling and uses the graphical language to construct process models.

HighAltitudeOmicsDB: An integrated resource for High-Altitude associated genes and proteins, interacting networks and semantic-similarities.

Millions of people worldwide visit, live, or work in the hypoxic environment encountered at high altitudes and it is important to understand the biomolecular responses to this stress. This would help design mitigation strategies for High Altitude Illnesses (HAIs). Inspite of a number of studies spanned over 100 years, complex mechanisms controlling acclimatization to hypoxia remain largely unknown. Some biomolecules, though, have been proposed as potential diagnostic, therapeutic and predictive markers for HA stress. HighAltitudeOmicsDB is a unique resource that provides a comprehensive, curated, user- friendly and detailed compilation of various genes/proteins which have been experimentally validated to be associated with various HA conditions; their Protein Protein Interactions (PPI) and Gene Ontology (GO) semantic similarities. For each database entry, HighAltitudeOmicsDB stores the level of regulation (up/down-regulation), fold change, control (low landers or high landers), duration and altitude of exposure, tissue of expression, source organism, level of hypoxia, experimental validation, place/country of study, ethnicity, geographical location, link to respective publication etc.Tthe database also collates information on disease and drug association, Gene Ontology and KEGG pathway associations. The web resource is a unique server platform which constructs PPI networks and extracts GO semantic similarities among the interactors. These unique features help to offer mechanistic insights in disease pathology. Hence, HighAltitudeOmicsDB is a unique platform for researchers working in this area to explore, fetch, compare and analyze HA associated gens/proteins, their PPI networks and GO semantic similarities. The database is available at http://www.altitudeomicsdb.in