Proteomics and its applications in phytopathologyAbhijeet Kashyap
Dear friends, I Abhijeet kashyap presenting the basics of proteomics to you all . Proteomics is the large-scale study of proteins, particularly their structures and functions.Proteomics helps in understanding the structure and function of different proteins as well as protein-protein interactions of an organism.
Oncology: Spatial Localization of Ras proteinsNachiket Vartak
This is a presentation of work done at the MPI Dortmund from 2008-2013 on the mechanism through with localization of the Ras protein in generated in cells. It presents the inhibiton Palmostatin-B, which inhibits this mechanism, leading to reveral of oncogenic signaling and cancerous phenotypes.
Proteomics and its applications in phytopathologyAbhijeet Kashyap
Dear friends, I Abhijeet kashyap presenting the basics of proteomics to you all . Proteomics is the large-scale study of proteins, particularly their structures and functions.Proteomics helps in understanding the structure and function of different proteins as well as protein-protein interactions of an organism.
Oncology: Spatial Localization of Ras proteinsNachiket Vartak
This is a presentation of work done at the MPI Dortmund from 2008-2013 on the mechanism through with localization of the Ras protein in generated in cells. It presents the inhibiton Palmostatin-B, which inhibits this mechanism, leading to reveral of oncogenic signaling and cancerous phenotypes.
insilico protein structure prediction and and structure analysis and its type which are commonly used in dry lab. docking is a procedure in which two protein,or protein to ligand binding intrection analysis by software tools.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
#INTRODUCTION OF PPIs
#EXAMPLE OF PPIs
#CLASSIFICATION OF PPIs
#IDENTIFICATION METHOD OF PPIs
#YEAST TWO HYBRID SYSTEM
#DATABASE OF PPIs
#APPLICATIONS OF PPIs
#FACTOR AFFECTING PPIs
Proteins facilitates most biological processes in a cell, including gene expression, cell growth, proliferation, nutrient uptake, morphology, motility, intercellular communication and apoptosis.
Protein–protein interactions (PPIs) refer to physical contacts established between two or more proteins as a result of biochemical events.
These interactions are very important in our lives as any disorder in them can lead to fatal diseases such as Alzheimer’s and Creutzfeld- Jacob Disease.
The most well known example of Protein-Protein Interaction is between Actin and Myosin while regulating Muscular contraction in our body.
The protein –protein interaction have commonly been termed the ‘INTERACTOME’ by scientists.
Homo-Oligomers: Complexes having one type of protein subunits.
E.g. : PPIs in Muscle Contraction
Hetero-Oligomers: Complexes having multiple types protein subunits.
E.g. : PPI between Cytochrome Oxidase and TRPC3 (Transient receptor potential cation channels
Brief Introduction of Protein-Protein Interactions (PPIs)Creative Proteomics
For more information, please visit https://www.creative-proteomics.com/services/protein-protein-interaction-networks.htm. Protein-protein interactions play important roles in various biological processes. PPIs can be classified based on different factors, including composition, affinity, and lifetime.
In this video from PASC18, Alexander Nitz from the Max Planck Institute for Gravitational Physics in Germany presents: The Search for Gravitational Waves.
"The LIGO and Virgo detectors have completed a prolific observation run. We are now observing gravitational waves from both the mergers of binary black holes and neutron stars. We’ll discuss how these discoveries were made and look into what the near future of searching for gravitational waves from compact binary mergers will look like."
Watch the video: https://wp.me/p3RLHQ-iTv
Learn more: github.com/gwastro/pycbc
and
https://pasc18.pasc-conference.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
insilico protein structure prediction and and structure analysis and its type which are commonly used in dry lab. docking is a procedure in which two protein,or protein to ligand binding intrection analysis by software tools.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
#INTRODUCTION OF PPIs
#EXAMPLE OF PPIs
#CLASSIFICATION OF PPIs
#IDENTIFICATION METHOD OF PPIs
#YEAST TWO HYBRID SYSTEM
#DATABASE OF PPIs
#APPLICATIONS OF PPIs
#FACTOR AFFECTING PPIs
Proteins facilitates most biological processes in a cell, including gene expression, cell growth, proliferation, nutrient uptake, morphology, motility, intercellular communication and apoptosis.
Protein–protein interactions (PPIs) refer to physical contacts established between two or more proteins as a result of biochemical events.
These interactions are very important in our lives as any disorder in them can lead to fatal diseases such as Alzheimer’s and Creutzfeld- Jacob Disease.
The most well known example of Protein-Protein Interaction is between Actin and Myosin while regulating Muscular contraction in our body.
The protein –protein interaction have commonly been termed the ‘INTERACTOME’ by scientists.
Homo-Oligomers: Complexes having one type of protein subunits.
E.g. : PPIs in Muscle Contraction
Hetero-Oligomers: Complexes having multiple types protein subunits.
E.g. : PPI between Cytochrome Oxidase and TRPC3 (Transient receptor potential cation channels
Brief Introduction of Protein-Protein Interactions (PPIs)Creative Proteomics
For more information, please visit https://www.creative-proteomics.com/services/protein-protein-interaction-networks.htm. Protein-protein interactions play important roles in various biological processes. PPIs can be classified based on different factors, including composition, affinity, and lifetime.
In this video from PASC18, Alexander Nitz from the Max Planck Institute for Gravitational Physics in Germany presents: The Search for Gravitational Waves.
"The LIGO and Virgo detectors have completed a prolific observation run. We are now observing gravitational waves from both the mergers of binary black holes and neutron stars. We’ll discuss how these discoveries were made and look into what the near future of searching for gravitational waves from compact binary mergers will look like."
Watch the video: https://wp.me/p3RLHQ-iTv
Learn more: github.com/gwastro/pycbc
and
https://pasc18.pasc-conference.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENE...ecij
This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem.
Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a
limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA)
to overcome this limitation, by starting with random solutions within the search space and narrowing
down the search space by considering the minimum and maximum errors of the population members.
Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to
converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro
thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
Quartz crystal microbalance based electronic nose system implemented on Field...TELKOMNIKA JOURNAL
Nowadays, an electronic nose becomes an important tool for detecting gas. The electronic nose consists of gas sensor array combined with neural networks to recognize patterns of the sensor array. Currently, the implementation of the neural network on the electronic nose systems still use personal computer so that less flexible or not portable. This paper discusses the electronic nose system implemented in a Field Programmable Gate Array (FPGA). The sensor array consists of eight Quartz Crystal Microbalance (QCM) coated with chemical materials. The eight channel-frequency counter is used to measure the frequency change of the sensor due to the presence of gas adsorbed to the surfaces. The bipolar sigmoid activation function used in the neuron model is approximated by a second order equation. The experimental result showed that the electronic nose system could recognize all the types of gas with 92% success rate.
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
Low power test pattern generation for bist applicationseSAT Journals
Abstract This paper proposes a novel test pattern generator (TPG) for built-in self-test. Our method generates multiple single input change (MSIC) vectors in a pattern, i.e., each vector applied to a scan chain is an SIC vector. A reconfigurable Johnson counter and a scalable SIC counter are developed to generate a class of minimum transition sequences. The proposed TPG is flexible to both the test-per-clock and the test-per-scan schemes. A theory is also developed to represent and analyze the sequences and to extract a class of MSIC sequences. Analysis results show that the produced MSIC sequences have the favorable features of uniform distribution and low input transition density. Simulation results with ISCAS benchmarks demonstrate that MSIC can save test power and impose no more than 7.5% overhead for a scan design. It also achieves the target fault coverage without increasing the test length. Keywords—Built-in self-test (BIST), low power, single-input change (SIC), test pattern generator (TPG)
Los días 20 y 21 de octubre de 2016, la Fundacion Ramón Areces organizó un simposio internacional para analizar las 'Enfermedades raras de la piel: de la clínica al gen y viceversa'. El doctor Fernando Larcher Laguzzi, del CIEMAT-Universidad Carlos III de Madrid-IIS Fundación Jiménez Díaz, ejerció de coordinador.
PID Controller Design for a Real Time Ball and Beam System – A Double Integra...idescitation
In this paper, the authors have discussed and shown
how to tune the PID controller in closed loop with time-delay
for the double integrator systems for a particular stability
margins. In math model it is assumed that time delay (ô) of
the plant is known. As a case study the authors have consid-
ered the mathematical model of the real-time beam and ball
system and analyzed the simulation and real time response.
“MS-Extractor: An Innovative Approach to Extract Microsatellites on „Y‟ Chrom...IJERD Editor
Simple Sequence Repeats (SSR), also known as Microsatellites, have been extensively used as
molecular markers due to their abundance and high degree of polymorphism. The nucleotide sequences of
polymorphic forms of the same gene should be 99.9% identical. So, Microsatellites extraction from the Gene is
crucial. However, Microsatellites repeat count is compared, if they differ largely, he has some disorder. The Y
chromosome likely contains 50 to 60 genes that provide instructions for making proteins. Because only males
have the Y chromosome, the genes on this chromosome tend to be involved in male sex determination and
development. Several Microsatellite Extractors exist and they fail to extract microsatellites on large data sets of
giga bytes and tera bytes in size. The proposed tool “MS-Extractor: An Innovative Approach to extract
Microsatellites on „Y‟ Chromosome” can extract both Perfect as well as Imperfect Microsatellites from large
data sets of human genome „Y‟. The proposed system uses string matching with sliding window approach to
locate Microsatellites and extracts them.
Welcome to the June 25-26, 2018 Workshop on – 2 Day Workshop on Transcriptomic Data Analysis….
Below you should see an embedded video stream. You can open the stream to fill the full screen to observe or join the workshop as a participant with the link that was emailed to you. If you did not get the link, use the chat box on the bottom right to request the link with your registered email ID.
HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENE...ecij
This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem. Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA) to overcome this limitation, by starting with random solutions within the search space and narrowing down the search space by considering the minimum and maximum errors of the population members. Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Similar to NetBioSIG2014-Talk by Ashwini Patil (20)
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
Visualization and Analysis of Dynamic Networks Alexander Pico
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugin’s functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
1. TimeXNet: Identifying active
gene sub-networks using time-
course gene expression profiles
Ashwini Patil
Institute of Medical Science
University of Tokyo
NetBio SIG, ISMB 2014
2. Goal
• Comprehensive computational analysis of the innate
immune response
Mouse Interaction network
103218 protein-protein, protein-DNA,
post-translational modifications
Time-course gene expression
RNA-seq expression levels in dendritic
cells on LPS stimulus at 8 time points
4. Method - TimeXNet
Partition differentially expressed
genes into 3 time-based groups
Identify most probable paths in the
network connecting the three groups
Patil et al., PLOS Comp. Biol., 2013
5. Minimum cost flow optimization
• ResponseNet
• Identifies paths between two groups of genes (genetic hits and differentially
expressed genes in yeast)
- Yeger-Lotem et l., Nat. Genetics, 2009
6. TimeXNet methodology
• Edge cost: inversely proportional to edge reliability
• Edge capacity: directly proportional to
• Fold change in expression of adjacent gene(s)
• Absolute tag counts of adjacent gene(s)
• Objective function
Minimize cost of flow through the network from T1 to
T3 genes
• Constraint
Flow must pass through intermediate nodes (T2 genes)
Most probable paths connecting T1->T2->T3 genes
2681 scored interactions among 1225 proteins
12. Comparison with other methods
Method
Experimentally confirmed
regulators (3 datasets)
KEGG Pathways
with predicted
paths (max length)
Execution
time (4 CPUs,
2.4Ghz, 12Gb
RAM)
Prior knowledge
required
Time-
course
data
TimeXNet 49.6%1 69.8%2 54.9%3 13 (7 edges) 3 min None Yes
ResponseNet* 39.2%1 53.5%2 39.2%3 0 (3 edges) 1 min None No
SDREM 12.0%1 32.6%2 11.8%3 2 (4 edges) ~10 days Initial genes Yes
1 Regulatory genes from Amit et al., Science, 2009
2 Regulatory genes from Chevrier et al., Cell, 2011
3 Target genes from Chevrier et al., Cell, 2011
*Local implementation using GLPK
13. Yeast osmotic stress response
• Time-course gene expression (min) in yeast on hyperosmotic stress
- Romero-Santacreu et al., RNA 2009
• Previously used to evaluate SDREM and ResponseNet
- Gitter et al., Genome Research 2013
• Genes with 1.5 fold change in expression
• Initial response genes: 2-4 min
• Intermediate regulators: 6-8 min
• Final effectors: 10-15 min
14. Predicted osmotic stress response network
• 2-4 min
• 6-8 min
• 10-15 min
• Predicted
Method
Gold
Standard* TFs* Hog1 Runtime
TimeXNet 19 5 Yes 5 sec
SDREM* 10 4 Yes -
ResponseNet* 3 2 No -*Taken from Gitter et al., Genome Research 2013
15. Circadian regulation of metabolism in mouse liver cells
- Unpublished
• Paths connecting genes showing rhythmic patterns of expression in 24 hours
• Network predicted by TimeXNet contains Sphk2, Pld1, Pld2, Glud1
17. • Input
• 3 sets of genes with
scores
• Weighted interaction
network
• Parameters gamma1 and
2
• Location of glpsol
executable from the GLPK
• Directory where results
will be storedCytoscape
Running TimeXNet
• Standalone application
• Command line version
• Iterative command line version to
identify optimal parameters
Patil & Nakai, under review
18. Conclusion
• TimeXNet: A method to predict active gene sub-networks using time-
course gene expression profiles
• Advantages
• Accurate and fast
• Independent of biological system: Innate immune response, circadian regulation of
metabolism in mouse, yeast osmotic stress response
• Amenable to incorporation of other time-course data types: phosphorylation levels,
protein levels, epigenetic information
• Issues to be addressed
• Allowing path prediction between more than 3 groups of genes while maintaining
speed and accuracy
• Incorporating other forms of time-course information
• Enhancements: Automatic install of GLPK, allowing users to enter non-numeric gene
IDs
Patil et al., PLOS Comp. Biol., 2013
19. Acknowledgements
• Innate immune response
• Prof. Kenta Nakai - University of Tokyo
• Dr. Yutaro Kumagai – Osaka University
• Dr. Kuo-ching Liang – University of Tokyo
• Prof. Yutaka Suzuki – University of Tokyo
• Dr. Tomonao Inobe – Toyama University
• Yeast osmotic stress response
• Dr. Anthony Gitter – Microsoft Research
• Circadian regulation of metabolism
• Dr. Craig Jolley – RIKEN Center for
Developmental Biology, Kobe
• Funding
• Japan Society for the Promotion of
Science (JSPS) FIRST Program
• JSPS Grant-in-Aid for Young Scientists
• Takeda Science Foundation (with Dr.
Tomonao Inobe)
• Computational resources
• Supercomputer at the Human Genome
Center, Institute of Medical Science,
University of Tokyo
20.
21. Edge Capacities
For edges between the auxiliary source, S, and the initial response genes GT1,
2 1log
/ /
imax i
Si T
imax ii i
fc e
C i G
fc N e N
(3)
For edges connected to the intermediate regulators GT2,
2 2 2log ,
/ /
imax i
ij T T
imax ii i
fc e
C i G j G
fc N e N
(4)
2 2
2
log log
/ // /
,
2
jmax jimax i
imax jmaxi ji ji j
ij T
fc efc e
fc N fc Ne N e N
C i j G
(5)
For edges between the late effectors, GT3, and the auxiliary sink T,
2 3log
/ /
imax i
iT T
imax ii i
fc e
C i G
fc N e N
(6)
2 2
2
log log
/ // /
,
2
jmax jimax i
imax jmaxi ji ji j
ij T
fc efc e
fc N fc Ne N e N
C i j G
(5
For edges between the late effectors, GT3, and the auxiliary sink T,
2 3log
/ /
imax i
iT T
imax ii i
fc e
C i G
fc N e N
(6
For edges between the auxiliary source, S, and the initial response genes GT1,
2 1log
/ /
imax i
Si T
imax ii i
fc e
C i G
fc N e N
(3)
For edges connected to the intermediate regulators GT2,
2 2 2log ,
/ /
imax i
ij T T
imax ii i
fc e
C i G j G
fc N e N
(4)
2 2
2
log log
/ // /
,
2
jmax jimax i
imax jmaxi ji ji j
ij T
fc efc e
fc N fc Ne N e N
C i j G
(5)
For edges between the late effectors, GT3, and the auxiliary sink T,
For edges connected to the intermediate regulators GT2,
• Graph G = (V, E) with E edges and V
nodes (containing S – auxiliary
source, T – auxiliary sink)
• fc = fold change
• 𝑒 = average expression level at all
time points
• N = number of genes with expression
values
• S = auxiliary source node
• T = auxiliary sink node
• GT1, GT2, GT3 = genes having
maximal fold change at times T1, T2
and T3
For all other edges, not connected to the intermediate regulators or the auxiliary source and s
21 ,ij TC i j S G T
22. Edge costs
1Si Si Tw C i G (8)
2ij ij Tw C i G (9)
3iT iT Tw C i G (10)
2,ij ij Tw f s i j S G T , as per equation (2)
The edge costs were calculated as:
Where ()f = scaling function
likelihood ratio , HitPredictijs i j ; 0.163 999ijs
999 , Innatedb, KEGGijs i j
, TRANSFACijs Transfacscore i j ; 1 6ijs
3iT iT Tw C i G
2,ij ij Tw f s i j S G T , as per equation (2)
The edge costs were calculated as:
10log ,ij ijA w i j E
2ij ij Tw C i G
3iT iT Tw C i G
2,ij ij Tw f s i j S G T , as per equation (2)
The edge costs were calculated as:
log ,A w i j E
2ij ij Tw C i G
3iT iT Tw C i G
2,ij ij Tw f s i j S G T , as per equation (2)
The edge costs were calculated as:
10log ,ij ijA w i j E
Using a large molecular interaction and regulatory network
Using time-course gene expression profiles on activation
Identify novel candidate genes and their time-dependent sub-networks
Primary host response to invading pathogens
Characterized by pattern-recognition receptors (PRRs) eg. Toll-like receptors Tlr1, Tlr2 … Tlr10
PRRs recognize specific microbial components – pathogen associated patterns (PAMPs)
PAMPs bind to PRRs and trigger downstream signaling cascades, resulting in expression of pro-inflammatory cytokines and systemic inflammation
MyD88 dependent pathway
early response
expression of proinflammatory cytokines
TRIF dependent pathway
late response
Expression of interferons (IFNs) and IFN-inducible genes
Comparatively small network of high confidence interactions connecting genes showing large changes in expression over time
2681 interactions among 1225 proteins
Each edge and node is assigned a flow – indicative of its connectivity (importance?) in the network
Genes showing no significant change in expression form a substantial part of the network
Jun, Fos, Chemokines, kinases, Stats, Sp3
Akt serine threonine kinases
Dual specificity phosphatases – responsible for dephosphorylating the Map kinases to repress the immune response
Xiap – anti-apoptotic inhibitor
Ppp2ca- Protein phosphatase 2a catalytic subunit alpha
Important role in regulation of endotoxin tolerance through the regulation of MyD88 activity (Xie et al., Cell Reports 2013)
Dephosphorylates 20S proteasome subunit
Affects ability of the proteasome to degrade substrates in concert with Protein Kinase A (Zong et la., Circulation Res 2006)
Network suggests a similar regulation of the immunoproteasome by ppp2ca
GO term enrichment: immune response, regulation of programmed cell death
KEGG pathways enriched: TLR signaling pathway, Jak-STAT signaling pathway, pathways in cancer, chemokine signaling pathway