Poster Abstract: A Benchmark for Low-power Wireless Networking

International audienceExperimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution

DEPENDABLE LOW-POWER WIRELESS SENSOR NETWORKS

Ph.DDOCTOR OF PHILOSOPH

Mathematical modeling of normal and cancer prostate signaling pathways

Abstract The field of systems biology has become very popular as a means to deal with cancer as well as other complex biological issues. It enables scientists to gain an insight into difficult conditions through mathematical approaches that have been developed. Prostate cancer is the second leading cause of death among men after skin cancer and its heterogeneity makes it a complex disease. In this study we focus on three pathways known to play crucial roles in the formation of prostate cancer. By using a mathematical model that combines all of them we describe the interactions taking place during signal transduction in the prostate under normal and cancer conditions. Normal and cancer prostate signaling Popular science summary Sofia Stamouli Our body is made up of cells which are the structural and functional unit of life. Normal cells can grow, divide to form new cells, or possibly die. This process is the normal cycle of cells and especially in the early stage of our life, cells grow faster allowing the person to grow. Cells undergo changes due to the actions of the molecules involved in different biological pathways. They receive specific signals from the external environment and then trigger a series of biochemical reactions which in turn transmit the information, to the nucleus for instance. Based on the nature of output response (downstream signaling molecules), cells can proliferate or die. In cancer cells, the mechanism of cell cycle regulation is altered and the cells instead of dying continue to grow continuously. Cancer is a complex biological system and there are large number of cellular subsystems and parameters involved which are difficult to study in the laboratory. Therefore, efforts have been made to investigate the difficult conditions and understand the communication among molecules in the biological pathways by using computational approaches. In our study, we focused on prostate cancer which is the second disease, after skin cancer, causing death to men. Studies have shown that there are three main signaling pathways known to play critical role in the prostate cancer formation: the androgen receptor signaling pathway, the Ras-Raf-MEK-ERK pathway (MAPK), and the PI3K-AKT-mTOR pathway. By using a set of ordinary differential equations we simulated these three pathways by integrating all in one. Based on the nature of output response or downstream signaling molecules (transient/sustained), cell's fate is decided. Therefore, we have developed a mathematical model to understand the differences regarding cell fate decision both in normal prostate and cancer prostate pathways and we validated the model experimentally. According to this model, we will also be able to predict new possible interactions between the signaling molecules as we already know that identifying the molecules involved in prostate cancer can be a target for drug discovery in the future. Degree project in bioinformatics, 2015 (Examensarbete i bioinformatik 45 hp till masterexamen, 2015

Potential and Therapeutic Roles of Diosmin in Human Diseases

Because of their medicinal characteristics, effectiveness, and importance, plant-derived flavonoids have been a possible subject of research for many years, particularly in the last decade. Plants contain a huge number of flavonoids, and Diosmin, a flavone glycoside, is one of them. Numerous in-vitro and in-vivo studies have validated Diosmin’s extensive range of biological capabilities which present antioxidative, antihyperglycemic, anti-inflammatory, antimutagenic, and antiulcer properties. We have presented this review work because of the greater biological properties and influences of Diosmin. We have provided a brief overview of Diosmin, its pharmacology, major biological properties, such as anti-cancer, anti-diabetic, antibacterial, anticardiovascular, liver protection, and neuroprotection, therapeutic approach, potential Diosmin targets, and pathways that are known to be associated with it

Codecast: Supporting Data Driven In-Network Processing for Low-Power Wireless Sensor Networks

10.1109/ipsn.2018.000142018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)72-8

Role of ZNF143 and Its Association with Gene Expression Patterns, Noncoding Mutations, and the Immune System in Human Breast Cancer

The function of noncoding sequence variations at ZNF143 binding sites in breast cancer cells is currently not well understood. Distal elements and promoters, also known as cis-regulatory elements, control the expression of genes. They may be identified by functional genomic techniques and sequence conservation, and they frequently show cell- and tissue-type specificity. The creation, destruction, or modulation of TF binding and function may be influenced by genetic modifications at TF binding sites that affect the binding affinity. Therefore, noncoding mutations that affect the ZNF143 binding site may be able to alter the expression of some genes in breast cancer. In order to understand the relationship among ZNF143, gene expression patterns, and noncoding mutations, we adopted an integrative strategy in this study and paid close attention to putative immunological signaling pathways. The immune system-related pathways ErbB, HIF1a, NF-kB, FoxO, JAK-STAT, Wnt, Notch, cell cycle, PI3K–AKT, RAP1, calcium signaling, cell junctions and adhesion, actin cytoskeleton regulation, and cancer pathways are among those that may be significant, according to the overall analysis

Simulated evolution of signal transduction networks.

Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually transduce the signal to the nucleus. The main objective of our work is to develop a theoretical approach which will help to better understand the behavior of signal transduction networks due to changes in kinetic parameters and network topology. By using an evolutionary algorithm, we designed a mathematical model which performs basic signaling tasks similar to the signaling process of living cells. We use a simple dynamical model of signaling networks of interacting proteins and their complexes. We study the evolution of signaling networks described by mass-action kinetics. The fitness of the networks is determined by the number of signals detected out of a series of signals with varying strength. The mutations include changes in the reaction rate and network topology. We found that stronger interactions and addition of new nodes lead to improved evolved responses. The strength of the signal does not play any role in determining the response type. This model will help to understand the dynamic behavior of the proteins involved in signaling pathways. It will also help to understand the robustness of the kinetics of the output response upon changes in the rate of reactions and the topology of the network

NF-kB in Signaling Patterns and Its Temporal Dynamics Encode/Decode Human Diseases

Defects in signaling pathways are the root cause of many disorders. These malformations come in a wide variety of types, and their causes are also very diverse. Some of these flaws can be brought on by pathogenic organisms and viruses, many of which can obstruct signaling processes. Other illnesses are linked to malfunctions in the way that cell signaling pathways work. When thinking about how errors in signaling pathways might cause disease, the idea of signalosome remodeling is helpful. The signalosome may be conveniently divided into two types of defects: phenotypic remodeling and genotypic remodeling. The majority of significant illnesses that affect people, including high blood pressure, heart disease, diabetes, and many types of mental illness, appear to be caused by minute phenotypic changes in signaling pathways. Such phenotypic remodeling modifies cell behavior and subverts normal cellular processes, resulting in illness. There has not been much progress in creating efficient therapies since it has been challenging to definitively confirm this connection between signalosome remodeling and illness. The considerable redundancy included into cell signaling systems presents several potential for developing novel treatments for various disease conditions. One of the most important pathways, NF-κB, controls several aspects of innate and adaptive immune responses, is a key modulator of inflammatory reactions, and has been widely studied both from experimental and theoretical perspectives. NF-κB contributes to the control of inflammasomes and stimulates the expression of a number of pro-inflammatory genes, including those that produce cytokines and chemokines. Additionally, NF-κB is essential for controlling innate immune cells and inflammatory T cells’ survival, activation, and differentiation. As a result, aberrant NF-κB activation plays a role in the pathogenesis of several inflammatory illnesses. The activation and function of NF-κB in relation to inflammatory illnesses was covered here, and the advancement of treatment approaches based on NF-κB inhibition will be highlighted. This review presents the temporal behavior of NF-κB and its potential relevance in different human diseases which will be helpful not only for theoretical but also for experimental perspectives

Mutation of kinetics parameters.

<p> represents the active form of the protein , another inactive protein molecule, is the complex formed during the reaction between and . is the active form of . , , and are the rates (interaction strength) of the reactions. A mutation of the reaction alters any of the rates, e.g., (top) adopts the new value (bottom).</p

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes’/proteins’ connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein–protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways’ components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment