Understanding Protein Function on a Genome-scale through the Analysis of Molecular Networks
Cornell Medical School, Physiology, Biophysics and Systems Biology (PBSB) graduate program, 2009.01.26, 16:00-17:00; [I:CORNELL-PBSB] (Long networks talk, incl. the following topics: why networks w. amsci*, funnygene*, net. prediction intro, memint*, tse*, essen*, sandy*, metagenomics*, netpossel*, tyna*+ topnet*, & pubnet* . Fits easily into 60’ w. 10’ questions. PPT works on mac & PC and has many photos w. EXIF tag kwcornellpbsb .)
Date Given: 01/26/2009
Network Biology: A paradigm for modeling biological complex systemsGanesh Bagler
These slides are part of the two lectures delivered at the as part of the 'National Workshop on Network Modelling and Graph Theory' (Dec 14-16, 2017) at Department of Mathematics, Dibrugarh University, Assam, India.
(1) Network Biology: A paradigm for integrative modeling of biological complex systems -- 14 Dec 2017, 3:30pm
(2) Applications of network modeling in biomedicine -- 15 Dec 2017, 9:00pm
Sponsored by UGC under SAP DRS (II)
(1) Workshop link: https://www.dibru.ac.in/upcoming-events/2981-national-workshop-on-network-modelling-and-graph-theory
(2) The Workshop Flyer: https://www.dibru.ac.in/images/uploaded_files/2017/Nov/National_Workshop_on_Network_Modelling_and_Graph_Theory.pdf
Deep learning for extracting protein-protein interactions from biomedical lit...Yifan Peng
State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.
Network Biology: A paradigm for modeling biological complex systemsGanesh Bagler
These slides are part of the two lectures delivered at the as part of the 'National Workshop on Network Modelling and Graph Theory' (Dec 14-16, 2017) at Department of Mathematics, Dibrugarh University, Assam, India.
(1) Network Biology: A paradigm for integrative modeling of biological complex systems -- 14 Dec 2017, 3:30pm
(2) Applications of network modeling in biomedicine -- 15 Dec 2017, 9:00pm
Sponsored by UGC under SAP DRS (II)
(1) Workshop link: https://www.dibru.ac.in/upcoming-events/2981-national-workshop-on-network-modelling-and-graph-theory
(2) The Workshop Flyer: https://www.dibru.ac.in/images/uploaded_files/2017/Nov/National_Workshop_on_Network_Modelling_and_Graph_Theory.pdf
Deep learning for extracting protein-protein interactions from biomedical lit...Yifan Peng
State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.
Introduction to graph databases and Neo4j for the bachelors student in Life sciences. Hands-on workshop for Neo4j and Cypher query language. The source of material for the hands-on training is: https://neo4j.com/graphacademy/online-training/introduction-to-neo4j/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A clonal based algorithm for the reconstruction of genetic network using s sy...eSAT Journals
Abstract Motivation: Gene regulatory network is the network based approach to represent the interactions between genes. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Gene microarray experiment provides the gene expression data for a particular condition and varying time periods. The expression of a particular gene depends upon the biological conditions and other genes. In this paper, we propose a new method for the analysis of microarray data. The proposed method makes use of S-system, which is a well-accepted model for the gene regulatory network reconstruction. Since the problem has multiple solutions, we have to identify an optimized solution. Evolutionary algorithms have been used to solve such problems. Though there are a number of attempts already been carried out by various researchers, the solutions are still not that satisfactory with respect to the time taken and the degree of accuracy achieved. Therefore, there is a need of huge amount further work in this topic for achieving solutions with improved performances. Results: In this work, we have proposed Clonal selection algorithm for identifying optimal gene regulatory network. The approach is tested on the real life data: SOS Ecoli DNA repairing gene expression data. It is observed that the proposed algorithm converges much faster and provides better results than the existing algorithms. Index Terms: Microarray analysis, Evolutionary Algorithm, Artificial Immune System, S-system, Gene Regulatory Network, SOS Ecoli DNA repairing, Clonal Selection Algorithm.
EVOLVING CONNECTION WEIGHTS FOR PATTERN STORAGE AND RECALL IN HOPFIELD MODEL ...ijsc
In this paper, implementation of a genetic algorithm has been described to store and later, recall of some
prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm
(mutation, cross-over, elitism etc) are used to evolve the population of optimal weight matrices for the
purpose of storing the patterns and then recalling of the patterns with induced noise was made, again using
a genetic algorithm. The optimal weight matrices obtained during the training are used as seed for starting
the GA in recalling, instead starting with random weight matrix. A detailed study of the comparison of
results thus obtained with the earlier results has been done. It has been observed that for Hopfield neural
networks, recall of patterns is more successful if evolution of weight matrices is applied for training
purpose also.
Project report: Investigating the effect of cellular objectives on genome-sca...Jarle Pahr
Report from a half-semester master-level project carried out at the department of biotechnology, Norwegian University of Science and Technology. Describes a MATLAB-based framework for comparing experimental metabolic flux data with model predictions and evaluating objective functions.
Genome-Scale Metabolic Models and Systems Medicine of Metabolic SyndromeNatal van Riel
workshop on 'The interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2 Diabetes', December 12 and 13, 2013, Eindhoven University of Technology
Introduction to graph databases and Neo4j for the bachelors student in Life sciences. Hands-on workshop for Neo4j and Cypher query language. The source of material for the hands-on training is: https://neo4j.com/graphacademy/online-training/introduction-to-neo4j/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A clonal based algorithm for the reconstruction of genetic network using s sy...eSAT Journals
Abstract Motivation: Gene regulatory network is the network based approach to represent the interactions between genes. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Gene microarray experiment provides the gene expression data for a particular condition and varying time periods. The expression of a particular gene depends upon the biological conditions and other genes. In this paper, we propose a new method for the analysis of microarray data. The proposed method makes use of S-system, which is a well-accepted model for the gene regulatory network reconstruction. Since the problem has multiple solutions, we have to identify an optimized solution. Evolutionary algorithms have been used to solve such problems. Though there are a number of attempts already been carried out by various researchers, the solutions are still not that satisfactory with respect to the time taken and the degree of accuracy achieved. Therefore, there is a need of huge amount further work in this topic for achieving solutions with improved performances. Results: In this work, we have proposed Clonal selection algorithm for identifying optimal gene regulatory network. The approach is tested on the real life data: SOS Ecoli DNA repairing gene expression data. It is observed that the proposed algorithm converges much faster and provides better results than the existing algorithms. Index Terms: Microarray analysis, Evolutionary Algorithm, Artificial Immune System, S-system, Gene Regulatory Network, SOS Ecoli DNA repairing, Clonal Selection Algorithm.
EVOLVING CONNECTION WEIGHTS FOR PATTERN STORAGE AND RECALL IN HOPFIELD MODEL ...ijsc
In this paper, implementation of a genetic algorithm has been described to store and later, recall of some
prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm
(mutation, cross-over, elitism etc) are used to evolve the population of optimal weight matrices for the
purpose of storing the patterns and then recalling of the patterns with induced noise was made, again using
a genetic algorithm. The optimal weight matrices obtained during the training are used as seed for starting
the GA in recalling, instead starting with random weight matrix. A detailed study of the comparison of
results thus obtained with the earlier results has been done. It has been observed that for Hopfield neural
networks, recall of patterns is more successful if evolution of weight matrices is applied for training
purpose also.
Project report: Investigating the effect of cellular objectives on genome-sca...Jarle Pahr
Report from a half-semester master-level project carried out at the department of biotechnology, Norwegian University of Science and Technology. Describes a MATLAB-based framework for comparing experimental metabolic flux data with model predictions and evaluating objective functions.
Genome-Scale Metabolic Models and Systems Medicine of Metabolic SyndromeNatal van Riel
workshop on 'The interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2 Diabetes', December 12 and 13, 2013, Eindhoven University of Technology
large data set is not available for some disease such as Brain Tumor. This and part2 presentation shows how to find "Actionable solution from a difficult cancer dataset
The Algorithms of Life - Scientific Computing for Systems Biologyinside-BigData.com
In this deck from ISC 2019, Ivo Sbalzarini from TU Dresden presents: The Algorithms of Life - Scientific Computing for Systems Biology. In his talk, Sbalzarini mainly discussed the rapidly growing importance and influence in the life sciences for scientific high-performance computing.
"Scientific high-performance computing is of rapidly growing importance and influence in the life sciences. Thanks to the increasing knowledge about the molecular foundations of life, recent advances in biomedical data science, and the availability of predictive biophysical theories that can be numerically simulated, mechanistic understanding of the emergence of life comes within reach. Computing is playing a pivotal and catalytic role in this scientific revolution, both as a tool of investigation and hypothesis testing, but also as a school of thought and systems model. This is because a developing tissue, embryo, or organ can itself be seen as a massively parallel distributed computing system that collectively self-organizes to bring about behavior we call life. In any multicellular organism, every cell constantly takes decisions about growth, division, and migration based on local information, with cells communicating with each other via chemical, mechanical, and electrical signals across length scales from nanometers to meters. Each cell can therefore be understood as a mechano-chemical processing element in a complexly interconnected million- or billion-core computing system. Mechanistically understanding and reprogramming this system is a grand challenge. While the “hardware” (proteins, lipids, etc.) and the “source code” (genetic code) are increasingly known, we known virtually nothing about the algorithms that this code implements on this hardware. Our vision is to contribute to this challenge by developing computational methods and software systems for high-performance data analysis, inference, and numerical simulation of computer models of biological tissues, incorporating the known biochemistry and biophysics in 3D-space and time, in order to understand biological processes on an algorithmic basis. This ranges from real-time approaches to biomedical image analysis, to novel simulation languages for parallel high-performance computing, to virtual reality and machine learning for 3D microscopy and numerical simulations of coupled biochemical-biomechanical models. The cooperative, interdisciplinary effort to develop and advance our understanding of life using computational approaches not only places high-performance computing center stage, but also provides stimulating impulses for the future development of this field."
Watch the video: https://wp.me/p3RLHQ-kBB
Learn more: https://www.isc-hpc.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The 20th Century was transformed by the ability to program on silicon, an innovation that made possible technologies that fundamentally revolutionised how the world works. As we face global challenges in health, food production, and in powering an increasingly energy-greedy planet, it is becoming clear that the 21st Century could be equally transformed by programming an entirely different material: biological matter. The power to program biology could transform medicine, agriculture, and energy, but relies, fundamentally, on an understanding of biochemistry as molecular machinery in the service of biological information-processing. Unlike engineered systems, however, living cells self-generate, self-organise, and self-repair, they undertake massively parallel operations with slow and noisy components in a noisy environment, they sense and actuate at molecular scales, and most intriguingly, they blur the line between software and hardware. Understanding this biological computation presents a huge challenge to the scientific community. Yet the ultimate destination and prize at the culmination of this scientific journey is the promise of revolutionary and transformative technology: the rational design and implementation of biological function, or more succinctly, the ability to program life.
Large scale machine learning challenges for systems biologyMaté Ongenaert
Large scale machine learning challenges for systems biology
by dr. Yvan Saeys - Machine Learning and Data Mining group, Bioinformatics and Systems Biology Division, VIB-UGent Department of Plant Systems Biology
Due to technological advances, the amount of biological data, and the pace at which it is generated has increased dramatically during the past decade. To extract new knowledge from these ever increasing data sets, automated techniques such as data mining and machine learning techniques have become standard practice.
In this talk, I will give an overview of large scale machine learning challenges in bioinformatics and systems biology, highlighting the importance of using scalable and robust techniques such as ensemble learning methods implemented on large computing grids.
I will present some of our state-of-the-art tools to solve problems such as biomarker discovery, large scale network inference, and biomedical text mining at PubMed scale.
Making effective use of graphics processing units (GPUs) in computationsOregon State University
Graphics processing units (GPUs) are specialized computer processors used in computers and video game systems to accelerate the creation and display of images. Due to their inherent parallel structure, they also have great potential to speed up computations in many scientific and engineering applications. GPUs are attractive for their ability to perform a large number of computations in parallel at an attractive price. Many of the world¹s largest supercomputers use GPUs to achieve their high performance, and personal computers and laptops use them for graphics displays and image processing. This seminar will explore the use of GPUs in general, describe examples of the use of GPUs in computations, and introduce some best practices for GPU computing.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Cornell Pbsb 20090126 Nets
1. Understanding Protein Function on a Genome-scale through the Analysis of Molecular Networks Mark B Gerstein Yale Slides at Lectures.GersteinLab.org (See Last Slide for References & More Info.) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
2.
3. Traditional single molecule way to integrate evidence & describe function Descriptive Name: Elongation Factor 2 Summary sentence describing function: This protein promotes the GTP-dependent translocation of the nascent protein chain from the A-site to the P-site of the ribosome. EF2_YEAST Lots of references to papers
4.
5.
6. Hierarchies & DAGs of controlled-vocab terms but still have issues... [Seringhaus & Gerstein, Am. Sci. '08] GO (Ashburner et al.) MIPS (Mewes et al.)
7.
8. Networks (Old & New) [Seringhaus & Gerstein, Am. Sci. '08] Classical KEGG pathway Same Genes in High-throughput Network
9. Networks occupy a midway point in terms of level of understanding 1D: Complete Genetic Partslist ~2D: Bio-molecular Network Wiring Diagram 3D: Detailed structural understanding of cellular machinery [Jeong et al. Nature, 41:411] [Fleischmann et al., Science, 269 :496]
10. Networks as a universal language Disease Spread [Krebs] Protein Interactions [Barabasi] Social Network Food Web Neural Network [Cajal] Electronic Circuit Internet [Burch & Cheswick]
11. Networks as a Central Theme in Systems Biology Reductionist Approach Integrative Approach [Adapted from H Yu] Systems Biology
12. Interactome networks Network pathology & pharmacology [Adapted from H Yu] Breast Cancer Parkinson’s Disease Alzheimer’s Disease Multiple Sclerosis
13. Using the position in networks to describe function [NY Times, 2-Oct-05, 9-Dec-08]
14. Types of Networks Interaction networks [Horak, et al, Genes & Development, 16:3017-3033] [DeRisi, Iyer, and Brown, Science, 278:680-686] [Jeong et al, Nature, 41:411] Regulatory networks Metabolic networks Nodes: proteins or genes Edges: interactions
15. Combining networks forms an ideal way of integrating diverse information Metabolic pathway Transcriptional regulatory network Physical protein-protein Interaction Co-expression Relationship Part of the TCA cycle Genetic interaction (synthetic lethal) Signaling pathways
16.
17. Predicting Networks How do we construct large molecular networks? From extrapolating correlations between functional genomics data with fairly small sets of known interactions, making best use of the known training data.
38. Network Dynamics #1: Cellular States How do networks change across different cellular states? How can this be used to assign function to a protein?
39.
40. Global topological measures for directed networks In-degree Regulatory and metabolic networks are directed Out-degree 5 3 TFs Targets
41. Scale-free networks Hubs dictate the structure of the network log(Degree) log(Frequency) Power-law distribution [Barabasi]
42. Hubs tend to be Essential Essential Non- Essential Integrate gene essentiality data with protein interaction network. Perhaps hubs represent vulnerable points? [ Lauffenburger, Barabasi] "hubbiness" [Yu et al., 2003, TIG]
43. Relationships extends to "Marginal Essentiality" Essential Not important Marginal essentiality measures relative importance of each gene (e.g. in growth-rate and condition-specific essentiality experiments) and scales continuously with "hubbiness" important Very important "hubbiness" [Yu et al., 2003, TIG]
44.
45. Gene expression data for five cellular conditions in yeast Multi-stage Binary [Brown, Botstein, Davis….] Cellular condition Cell cycle Sporulation Diauxic shift DNA damage Stress response
46.
47. Network usage under different conditions static Luscombe et al. Nature 431: 308
55. Analysis of condition-specific subnetworks in terms of global topological statistics Luscombe et al. Nature 431: 308 Sporulation Cell cycle Diauxic shift DNA damage Stress response Outdegree Indegree Pathlength Clustering coefficient Binary Quick, large-scale turnover of genes Multi-stage Controlled, ticking over of genes at different stages
56. Analysis of condition-specific subnetworks in terms of occurrence of local motifs Luscombe et al. Nature 431: 308 Sporulation Cell cycle Diauxic shift DNA damage Stress response Single-input module Multi-input module Feed-forward loop Binary Quick, large-scale turnover of genes Multi-stage Controlled, ticking over of genes at different stages
57. Summary multi-stage conditions binary conditions Cell cycle Sporulation Diauxic shift DNA damage Stress less pronounced Hubs more pronounced longer Path Lengths shorter more TF inter-regulation less complex loops (FFLs) Motifs simpler (SIMs)
58.
59.
60. Luscombe et al. Nature 431: 308 transitient hubs permanent hubs Swi4, Mbp1 Ime1, Ume6 Msn2, Msn4 cell cycle sporulation diauxic shift DNA damage stress response all conditions
61. Luscombe et al. Nature 431: 308 transitient hubs permanent hubs Unknown functions cell cycle sporulation diauxic shift DNA damage stress response all conditions
62. Network Dynamics #2: Environments How do molecular networks change across environments? What pathways are used more ? Used as a biosensor ?
64. Comparative Metagenomics Water Soil Do the proportions of pathways represented in these two samples differ? Dinsdale et. al., Nature 2008
65. Trait-based Biogeography Green et. al., Science 2008 Average Yearly Rainfall (mm) Leaf span Leaf area Do the proportions of pathways represented in these two samples CHANGE as a function of their environments? Long Island Sound, CT Charles River, MA
66. Global Ocean Survey Statistics (GOS) 6.25 GB of data 7.7M Reads 1 million CPU hours to process Rusch, et al., PLOS Biology 2007
67. Expressing data as matrices indexed by site, env. var., and pathway usage Pathway Sequences (Community Function) Environmental Features [Rusch et. al., (2007) PLOS Biology; Gianoulis et al., PNAS (in press, 2009]
69. Canonical Correlation Analysis: Simultaneous weighting [ Gianoulis et al., PNAS (in press, 2009) ] + b’ + c’ GPI = a’ + b + c UPI = a
70. Canonical Correlation Analysis: Simultaneous weighting [ Gianoulis et al., PNAS (in press, 2009) ] + b’ + c’ GPI = a’ + b + c UPI = a Metabolic Pathways Environmental Features Temp Chlorophyll etc Photosynthesis Lipid Metabolism etc
71. Environmental-Metabolic Space The goal of this technique is to interpret cross-variance matrices We do this by defining a change of basis. [ Gianoulis et al., PNAS (in press, 2009) ] a,b
72. Circuit Map Environmentally invariant Environmentally variant Strength of Pathway co-variation with environment [ Gianoulis et al., PNAS (in press, 2009) ]
73. Conclusion #1: energy conversion strategy, temp and depth [ Gianoulis et al., PNAS (in press, 2009) ]
74. Conclusion #2: Outer Membrane components vary the environment [ Gianoulis et al., PNAS (in press, 2009) ]
75. Conclusion #3: Covariation of AA biosynthesis and Import Why is their fluctuation in amino acid metabolism? Is there a feature(s) that underlies those that are environmentally-variant as opposed to those which are not? [ Gianoulis et al., PNAS (in press, 2009) ]
76. Conclusion #4: Cofactor (Metal) Optimization Methionine degradation Polyamine biosynthesis Spermidine/Putrescine transporters Methionine synthesis Cobalamin biosynthesis Cobalt transporters Methionine Salvage IS DEPENDENT-ON Methionine IS NEEDED FOR S-adenosyl Methionine Biosynthesis (synthesize SAM one of the most important methyl donors) RELIES ON Arg/His/Ornithine transporters Methionine salvage, synthesis, and uptake, transport [ Gianoulis et al., PNAS (in press, 2009) ]
78. Networks & Variation Which parts of the network vary most in sequence? Which are under selection, either positive or negative?
79.
80. ADAPTIVE EVOLUTION CAN BE SEEN ON TWO DIFFERENT LEVELS Intra-species variation Fixed mutations (differences to other species) Single- basepair Structural variation Copy Number Variants Single-Nucleotide Polymorphisms Segmental Duplications Fixed Differences Source: PMK Positive Selection Positive Selection
81. POSITIVE SELECTION LARGELY TAKES PLACE AT THE NETWORK PERIPHERY Source: Nielsen et al. PLoS Biol. (2005), HPRD, and Kim et al. PNAS (2007) High likelihood of positive selection Lower likelihood of positive selection Not under positive selection No data about positive selection Positive selection in the human interactome
82.
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84. BURIED SITES ARE CONSERVED AND MUCH LESS LIKELY TO HARBOR NON-SYNONYMOUS MUTATIONS p<<0.01 Buried sites dN/dS Ratio Exposed sites p<<0.01 Site with Synonymous Mutations only Sites with Non-synonymous Mutations Average Relative Surface Exposure Source: Kim et al. PNAS (2007) Why do we observer this? Perhaps central hub proteins are involved in more interactions & have more surface buried.
85. Another explanation: THE NETWORK PERIPHERY CORRESPONDS TO THE CELLULAR PERIPHERY Chromosome Nucleus Cytoplasm Membrane Extracellular Region Betweenness Centrality (x 10 4 ) Degree Centrality Source: Gandhi et al. ( Nature Genetics 2006), Kim et al. PNAS (2007)
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94. (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu TopNet.GersteinLab.org Acknowledgements MG MS
97. Acknowledgements TopNet.GersteinLab.org (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu H Yu P Kim K Yip J Lu S Douglas M Seringhaus Y Xia T Gianoulis J Korbel A Sboner P Patel P Cayting P Bork, J Raes Job opportunities currently for postdocs & students N Luscombe A Paccanaro S Teichmann M Babu MS MG
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