Systems biology is the study of complex interactions in biological systems using a holistic approach. It aims to understand emergent properties and entire processes in a system. Key aspects of systems biology include modeling interaction networks and dynamic behavior over time through computational and experimental methods. Understanding failure modes in biological signaling circuits could provide insights into neurodegenerative disorders caused by sustained high calcium levels triggering apoptosis.
Randomizing genome-scale metabolic networksAreejit Samal
The document proposes a new Markov Chain Monte Carlo (MCMC) based method to generate randomized metabolic networks that impose biochemical and functional constraints. The method successively constrains the networks by (1) fixing the number of reactions, (2) fixing the number of metabolites, (3) excluding blocked reactions, and (4) requiring growth on a specified environment. Imposing these constraints causes the randomized networks to more closely match properties of real metabolic networks like E. coli. The approach generates an ensemble of diverse yet meaningful randomized networks to help identify design principles in metabolic networks.
Talk and poster presented at the American Society for Biochemistry and Molecular Biology/Experimental Biology Conference on April 4, 2016. Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. Parameters for a series of related networks, ranging in size from 15 to 35 genes, were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and five strains individually deleted for the transcription factors, Cin5, Gln3, Hap4, Hmo1, Zap1, of budding yeast, Saccharomyces cerevisiae BY4741. Model predictions fit the experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from http://kdahlquist.github.io/GRNmap/. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight open source code and application are available from http://dondi.github.io/GRNsight/index.html.
The document discusses using k-way interaction loglinear models to analyze gene interaction from microarray data. It begins with background on challenges with existing clustering and association rule methods. It then proposes using loglinear models to identify interactions that cannot be explained by pairwise associations alone. The method transforms expression data, finds frequent gene sets, and fits k-way interaction models to identify statistically significant higher-order interactions. Experimental results on yeast data identified known and potentially new biological interactions.
1) The document describes a new method for generating randomized metabolic networks using Markov chain Monte Carlo sampling and flux balance analysis. This allows imposing biological constraints to sample networks with the same functionality as real networks.
2) Analysis of structural properties of these randomized networks showed they have similar scale-free, small-world and bow-tie architecture as real metabolic networks. This suggests global network structure arises from simple biochemical and functional constraints.
3) The method provides a computational framework to study evolutionary questions in systems biology by uniformly sampling the vast space of genetically diverse yet functionally equivalent metabolic networks.
The document introduces bio computing and discusses how cells can be modeled as computing devices. It outlines key topics including using P systems to represent cellular computation and examples of biocomputing. Specific concepts covered include modeling genetic transcriptional networks and common network motifs that are evolutionarily preferred. Membrane structures and transport mechanisms in P systems are also summarized.
The document discusses biological computation and gene regulation in cells. It describes how (1) cells perform biochemical information processing to transform cues into biological functions, (2) embryonic stem cells can adopt different states through gene regulation, and (3) techniques like Boolean networks and satisfiability modulo theories can be used to model and analyze gene interaction networks inferred from experimental data. The techniques allow predicting cell behaviors and identifying gene interaction programs governing processes like stem cell differentiation.
Systems biology & Approaches of genomics and proteomicssonam786
This presentation provides the basic understanding of varous genomics and proteomics techniques.Systems biology studies life as a system .It includes the study of living system using various omic technologies .
Randomizing genome-scale metabolic networksAreejit Samal
The document proposes a new Markov Chain Monte Carlo (MCMC) based method to generate randomized metabolic networks that impose biochemical and functional constraints. The method successively constrains the networks by (1) fixing the number of reactions, (2) fixing the number of metabolites, (3) excluding blocked reactions, and (4) requiring growth on a specified environment. Imposing these constraints causes the randomized networks to more closely match properties of real metabolic networks like E. coli. The approach generates an ensemble of diverse yet meaningful randomized networks to help identify design principles in metabolic networks.
Talk and poster presented at the American Society for Biochemistry and Molecular Biology/Experimental Biology Conference on April 4, 2016. Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. Parameters for a series of related networks, ranging in size from 15 to 35 genes, were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and five strains individually deleted for the transcription factors, Cin5, Gln3, Hap4, Hmo1, Zap1, of budding yeast, Saccharomyces cerevisiae BY4741. Model predictions fit the experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from http://kdahlquist.github.io/GRNmap/. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight open source code and application are available from http://dondi.github.io/GRNsight/index.html.
The document discusses using k-way interaction loglinear models to analyze gene interaction from microarray data. It begins with background on challenges with existing clustering and association rule methods. It then proposes using loglinear models to identify interactions that cannot be explained by pairwise associations alone. The method transforms expression data, finds frequent gene sets, and fits k-way interaction models to identify statistically significant higher-order interactions. Experimental results on yeast data identified known and potentially new biological interactions.
1) The document describes a new method for generating randomized metabolic networks using Markov chain Monte Carlo sampling and flux balance analysis. This allows imposing biological constraints to sample networks with the same functionality as real networks.
2) Analysis of structural properties of these randomized networks showed they have similar scale-free, small-world and bow-tie architecture as real metabolic networks. This suggests global network structure arises from simple biochemical and functional constraints.
3) The method provides a computational framework to study evolutionary questions in systems biology by uniformly sampling the vast space of genetically diverse yet functionally equivalent metabolic networks.
The document introduces bio computing and discusses how cells can be modeled as computing devices. It outlines key topics including using P systems to represent cellular computation and examples of biocomputing. Specific concepts covered include modeling genetic transcriptional networks and common network motifs that are evolutionarily preferred. Membrane structures and transport mechanisms in P systems are also summarized.
The document discusses biological computation and gene regulation in cells. It describes how (1) cells perform biochemical information processing to transform cues into biological functions, (2) embryonic stem cells can adopt different states through gene regulation, and (3) techniques like Boolean networks and satisfiability modulo theories can be used to model and analyze gene interaction networks inferred from experimental data. The techniques allow predicting cell behaviors and identifying gene interaction programs governing processes like stem cell differentiation.
Systems biology & Approaches of genomics and proteomicssonam786
This presentation provides the basic understanding of varous genomics and proteomics techniques.Systems biology studies life as a system .It includes the study of living system using various omic technologies .
Dynamic complex formation during the yeast cell cycleLars Juhl Jensen
The document summarizes the generation of a qualitative model of the yeast cell cycle through integrating various datasets on gene expression, protein-protein interactions, and other molecular data. The model provides a global overview of temporal complex formation during the cell cycle. Key aspects of the model include extracting a cell cycle interaction network, identifying periodically expressed proteins, assessing interaction quality, filtering by subcellular localization, and examining the roles of static and dynamic proteins. The temporal interaction network serves as a discovery tool and reveals novel protein complexes and functions.
GFP For Exploring Protein-Protein Interactions - Nelson Giovanny Rincon Silva Nelson Giovanny Rincon S
This document describes using green fluorescent protein (GFP) to study protein-protein interactions. GFP from jellyfish has properties that make it useful for this, such as fluorescence and stability. The document discusses fusing GFP to proteins of interest to monitor their interactions. Specifically, it examines fusing GFP to the S-peptide and S-protein fragments of the ribonuclease protein. It describes constructing and purifying these GFP fusion proteins, then using gel retardation and fluorescence polarization assays to measure the interactions and determine binding constants. The assays allow quantifying the fraction of proteins bound versus unbound to study protein binding.
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
NMDA Receptor Physiological Activators and Inhibitors A Three-fold Molecular ...Laurensius Mainsiouw
1) NMDA receptors demonstrate slow kinetics, are highly permeable to calcium ions, and require binding of glutamate and glycine for activation. Their function is important for processes like long-term potentiation that underlie memory and learning.
2) The receptor consists of four subunits that can vary, leading to differences in properties like agonist binding affinity. Single channel recordings have provided insight into the kinetic schemes of receptor interactions.
3) Binding of agonists is believed to cause conformational changes in the receptor subunits, bringing the transmembrane domains closer together and opening the ion channel. Mutational studies support models where agonist binding causes lobes of the ligand binding domains to move, transmitting the signal to open
Abstract:Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents PS2O, which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybrid Evolutionary-Conventional Algorithm (HECA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GSO), combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said algorithms is used to overcome the Problem of premature convergence. PS2O, HECA , GSO is applied to Reactive Power Optimization problem and is evaluated on standard IEEE 57, practical 191 test Bus Systems. The results shows that all the three algorithms perform well in solving the reactive power problem and prevent premature convergence to high degree but still keep a rapid convergence. Of all the three PS2O has the edge in reducing the real power loss when compared to HECA & GSO.
Keywords:PS2O, Hybrid Evolutionary-Conventional Algorithm, Genetical Swarm Optimization, Reactive Power Optimization.
This document discusses network analysis of metabolism in four kingdoms of life using elementary flux modes. It provides examples of how this analysis can be used to determine optimal pathways, predict engineering effects, and assess enzyme deficiencies. The analysis allows detection of previously unknown pathways and futile cycles. Applying this to human metabolism, studies have found evidence that fatty acids can be converted to glucose through entangled routes, and have identified futile cycles that may play a role in aging.
This document discusses systems biology and provides examples of regulatory networks and dynamics modeling in systems biology. It summarizes that systems biology aims to understand biological processes using a systems-level approach by integrating 'omics data, quantitative analysis, and computational modeling to study biological systems at various scales, from pathways to whole organisms. It also notes the rapid expansion of the field since 2000 and discusses current and future directions, including data integration, modeling dynamics, placing networks in spatial and temporal contexts, and applications to medicine.
1. Statistical analysis of big data sets from microarrays and RNA-seq is used to identify differentially expressed genes. Heat maps and volcano plots are commonly used to visualize the data.
2. Gene ontology, gene set enrichment analysis, and transcription factor analysis are used to analyze lists of genes and identify biological processes, pathways, and regulatory relationships.
3. Networks can be constructed by integrating gene lists with protein-protein and gene regulatory interaction databases to build signaling, regulatory, and interaction networks for further analysis.
Talk device approach to biology march 29 1 2015Bob Eisenberg
Device Approach to Biology (and Engineering)
The goal of biological research is often more to control than to understand. Devices in biology (like ion channels) control individual functions just as they do in our technology. Study of control requires a multiscale approach because a handful of atoms, moving in 10-15 sec, control biological functions extending meters and taking seconds. Structural biology and molecular dynamics are essential (and beautiful!) parts of this hierarchy, but so are the functions themselves, and the electric field equations that link structure and function on all scales from atoms to nerve cells. Analyzing biological systems as devices is usually successful, and almost always productive.
BioNetVisA 2018 ECCB workshop
From biological network reconstruction to data visualization and analysis in molecular biology and medicine.
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Analyzing Employee’s Heart rate using Nonlinear Cellular Automata modelIOSR Journals
Non-linear Cellular Automata model is a simulation tool which can be used to diagnosis the intensity of the disease. This paper aims to study the Heart rate behavior between normal respiratory patients and healthy controls/unhealthy controls. We also discuss about Heart Rate Variability (HRV) of employee’s through non-linear Cellular Automata model. Cellular Automata model gives us striking results for further studies
Insilico methods for design of novel inhibitors of Human leukocyte elastaseJayashankar Lakshmanan
Oral contributed paper “Insilico methods for design of novel inhibitors of Human leukocyte elastase” in the International conference on Systemics, Cybernetics and Informatics-2006
This document describes using a Bayesian network approach to build a gene regulatory network in response to cold stress in plants. Microarray data from Arabidopsis plants exposed to cold temperatures over time was analyzed using local pooled error tests to identify differentially expressed genes. The GeneNet algorithm was then applied to the differentially expressed genes to infer partial directed networks and identify master regulatory genes that may play important roles in cold stress response signaling pathways. The network provides a framework for further experimental validation of key genes.
This document presents a mathematical model for an electrochemically synthesized thin film of a conducting polymer used to immobilize an enzyme. The model describes the steady-state analysis of a mediated amperometric system involving substrate conversion to product via enzyme kinetics and reoxidation of reduced mediator at an electrode. Rate equations are developed and steady-state assumptions are applied to derive an expression for observed flux in terms of kinetic parameters and substrate concentration. The model can be used to predict system behavior and experimentally test characteristics.
Fast-SL: An efficient algorithm to identify synthetic lethals in metabolic ne...Karthik Raman
Slide deck on Fast-SL, an efficient algorithm to identify synthetic lethals. Presented at the annual NNMCB meeting at Pune, India on 27 Dec 2015. Original paper reference: http://bioinformatics.oxfordjournals.org/content/31/20/3299
This document summarizes a framework for automatically extracting human protein-protein interaction data from biomedical literature. It describes benchmarking interaction datasets based on shared functional annotations and known physical interactions. It also outlines a method using a conditional random field tagger to identify protein names in text and two approaches for extracting interactions: co-citation analysis and learning interaction extractors from annotated sentences. Evaluation shows the extracted interactions have accuracy comparable to manually curated databases.
The document summarizes a presentation on biosensors. It discusses the different parts and classifications of biosensors, including the three generations of glucose biosensors and the basics of transducers. It also examines cell-based biosensors, enzyme immunosensors, and DNA biosensors, outlining their mechanisms and advantages. The presentation provides an overview of key topics relating to biosensor technology.
This document discusses using an artificial neural network based direct inverse method to control a bioreactor. In the first part, it provides background on bioreactors, challenges with controlling them using conventional controllers, and how neural networks can help address those challenges. The rest of the document focuses on simulating control of a bioreactor using a direct inverse neural network controller and comparing its performance to a conventional PI controller. The neural network controller was able to achieve faster and more accurate responses to setpoint changes and disturbances compared to the PI controller, which experienced offsets and longer settling times due to the bioreactor's nonlinear dynamics and input multiplicities.
Dynamic complex formation during the yeast cell cycleLars Juhl Jensen
The document summarizes the generation of a qualitative model of the yeast cell cycle through integrating various datasets on gene expression, protein-protein interactions, and other molecular data. The model provides a global overview of temporal complex formation during the cell cycle. Key aspects of the model include extracting a cell cycle interaction network, identifying periodically expressed proteins, assessing interaction quality, filtering by subcellular localization, and examining the roles of static and dynamic proteins. The temporal interaction network serves as a discovery tool and reveals novel protein complexes and functions.
GFP For Exploring Protein-Protein Interactions - Nelson Giovanny Rincon Silva Nelson Giovanny Rincon S
This document describes using green fluorescent protein (GFP) to study protein-protein interactions. GFP from jellyfish has properties that make it useful for this, such as fluorescence and stability. The document discusses fusing GFP to proteins of interest to monitor their interactions. Specifically, it examines fusing GFP to the S-peptide and S-protein fragments of the ribonuclease protein. It describes constructing and purifying these GFP fusion proteins, then using gel retardation and fluorescence polarization assays to measure the interactions and determine binding constants. The assays allow quantifying the fraction of proteins bound versus unbound to study protein binding.
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
NMDA Receptor Physiological Activators and Inhibitors A Three-fold Molecular ...Laurensius Mainsiouw
1) NMDA receptors demonstrate slow kinetics, are highly permeable to calcium ions, and require binding of glutamate and glycine for activation. Their function is important for processes like long-term potentiation that underlie memory and learning.
2) The receptor consists of four subunits that can vary, leading to differences in properties like agonist binding affinity. Single channel recordings have provided insight into the kinetic schemes of receptor interactions.
3) Binding of agonists is believed to cause conformational changes in the receptor subunits, bringing the transmembrane domains closer together and opening the ion channel. Mutational studies support models where agonist binding causes lobes of the ligand binding domains to move, transmitting the signal to open
Abstract:Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents PS2O, which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybrid Evolutionary-Conventional Algorithm (HECA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GSO), combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said algorithms is used to overcome the Problem of premature convergence. PS2O, HECA , GSO is applied to Reactive Power Optimization problem and is evaluated on standard IEEE 57, practical 191 test Bus Systems. The results shows that all the three algorithms perform well in solving the reactive power problem and prevent premature convergence to high degree but still keep a rapid convergence. Of all the three PS2O has the edge in reducing the real power loss when compared to HECA & GSO.
Keywords:PS2O, Hybrid Evolutionary-Conventional Algorithm, Genetical Swarm Optimization, Reactive Power Optimization.
This document discusses network analysis of metabolism in four kingdoms of life using elementary flux modes. It provides examples of how this analysis can be used to determine optimal pathways, predict engineering effects, and assess enzyme deficiencies. The analysis allows detection of previously unknown pathways and futile cycles. Applying this to human metabolism, studies have found evidence that fatty acids can be converted to glucose through entangled routes, and have identified futile cycles that may play a role in aging.
This document discusses systems biology and provides examples of regulatory networks and dynamics modeling in systems biology. It summarizes that systems biology aims to understand biological processes using a systems-level approach by integrating 'omics data, quantitative analysis, and computational modeling to study biological systems at various scales, from pathways to whole organisms. It also notes the rapid expansion of the field since 2000 and discusses current and future directions, including data integration, modeling dynamics, placing networks in spatial and temporal contexts, and applications to medicine.
1. Statistical analysis of big data sets from microarrays and RNA-seq is used to identify differentially expressed genes. Heat maps and volcano plots are commonly used to visualize the data.
2. Gene ontology, gene set enrichment analysis, and transcription factor analysis are used to analyze lists of genes and identify biological processes, pathways, and regulatory relationships.
3. Networks can be constructed by integrating gene lists with protein-protein and gene regulatory interaction databases to build signaling, regulatory, and interaction networks for further analysis.
Talk device approach to biology march 29 1 2015Bob Eisenberg
Device Approach to Biology (and Engineering)
The goal of biological research is often more to control than to understand. Devices in biology (like ion channels) control individual functions just as they do in our technology. Study of control requires a multiscale approach because a handful of atoms, moving in 10-15 sec, control biological functions extending meters and taking seconds. Structural biology and molecular dynamics are essential (and beautiful!) parts of this hierarchy, but so are the functions themselves, and the electric field equations that link structure and function on all scales from atoms to nerve cells. Analyzing biological systems as devices is usually successful, and almost always productive.
BioNetVisA 2018 ECCB workshop
From biological network reconstruction to data visualization and analysis in molecular biology and medicine.
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Analyzing Employee’s Heart rate using Nonlinear Cellular Automata modelIOSR Journals
Non-linear Cellular Automata model is a simulation tool which can be used to diagnosis the intensity of the disease. This paper aims to study the Heart rate behavior between normal respiratory patients and healthy controls/unhealthy controls. We also discuss about Heart Rate Variability (HRV) of employee’s through non-linear Cellular Automata model. Cellular Automata model gives us striking results for further studies
Insilico methods for design of novel inhibitors of Human leukocyte elastaseJayashankar Lakshmanan
Oral contributed paper “Insilico methods for design of novel inhibitors of Human leukocyte elastase” in the International conference on Systemics, Cybernetics and Informatics-2006
This document describes using a Bayesian network approach to build a gene regulatory network in response to cold stress in plants. Microarray data from Arabidopsis plants exposed to cold temperatures over time was analyzed using local pooled error tests to identify differentially expressed genes. The GeneNet algorithm was then applied to the differentially expressed genes to infer partial directed networks and identify master regulatory genes that may play important roles in cold stress response signaling pathways. The network provides a framework for further experimental validation of key genes.
This document presents a mathematical model for an electrochemically synthesized thin film of a conducting polymer used to immobilize an enzyme. The model describes the steady-state analysis of a mediated amperometric system involving substrate conversion to product via enzyme kinetics and reoxidation of reduced mediator at an electrode. Rate equations are developed and steady-state assumptions are applied to derive an expression for observed flux in terms of kinetic parameters and substrate concentration. The model can be used to predict system behavior and experimentally test characteristics.
Fast-SL: An efficient algorithm to identify synthetic lethals in metabolic ne...Karthik Raman
Slide deck on Fast-SL, an efficient algorithm to identify synthetic lethals. Presented at the annual NNMCB meeting at Pune, India on 27 Dec 2015. Original paper reference: http://bioinformatics.oxfordjournals.org/content/31/20/3299
This document summarizes a framework for automatically extracting human protein-protein interaction data from biomedical literature. It describes benchmarking interaction datasets based on shared functional annotations and known physical interactions. It also outlines a method using a conditional random field tagger to identify protein names in text and two approaches for extracting interactions: co-citation analysis and learning interaction extractors from annotated sentences. Evaluation shows the extracted interactions have accuracy comparable to manually curated databases.
The document summarizes a presentation on biosensors. It discusses the different parts and classifications of biosensors, including the three generations of glucose biosensors and the basics of transducers. It also examines cell-based biosensors, enzyme immunosensors, and DNA biosensors, outlining their mechanisms and advantages. The presentation provides an overview of key topics relating to biosensor technology.
This document discusses using an artificial neural network based direct inverse method to control a bioreactor. In the first part, it provides background on bioreactors, challenges with controlling them using conventional controllers, and how neural networks can help address those challenges. The rest of the document focuses on simulating control of a bioreactor using a direct inverse neural network controller and comparing its performance to a conventional PI controller. The neural network controller was able to achieve faster and more accurate responses to setpoint changes and disturbances compared to the PI controller, which experienced offsets and longer settling times due to the bioreactor's nonlinear dynamics and input multiplicities.
Similar to Systems Biology Lecture - SCFBIO Sep 18, 09.pdf (20)
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
2. JGomes, SBS IITD 2
Definition of Systems Biology
Wikipedia definition
Systems biology is a biology‐based inter‐
disciplinary study field that focuses on the
systematic study of complex interactions in
biological systems, thus using a new perspective
(holism instead of reduction) to study them.
‡ discover new emergent properties
‡ understand better the entirety of processes that
happen in a biological system.
3. JGomes, SBS IITD 3
Other Definitions
‡ Systems biology is a comprehensive quantitative analysis of the
manner in which all the components of a biological system interact
functionally over time (Alan Aderem, Cell, Vol 121, 611‐613, 2005.
Institute of Systems Biology, Seattle).
‡ Systems biology is the study of the behavior of complex biological
organization and processes in terms of the molecular constituents
(Marc W. Kirschner, Cell, Vol 121, 503‐504, 2005. Department of
Systems Biology, Harvard Medical School).
‡ Systems biology can be described as “Integrative Biology” with the
ultimate goal of being able to predict de novo biological outcomes given
the list of components involved (Edison T. Liu, Cell, Vol 121, 505‐506,
2005. Genome Institute of Singapore).
‡ “Systems biology” aims at a quantitative understanding of biological
systems to an extent that one is able to predict systemic features (Peer
Bork and Luis Serrano, Cell, Vol 121, 507‐509, 2005. EMBL Germany).
4. JGomes, SBS IITD 4
Why is it difficult to define Systems
Biology ?
Because there always appears to be a delicate
balance between opposing aspects
‡ Scale: genome‐wide vs small scale networks
‡ Discipline: biological vs physical
‡ Method: computational vs experimental
‡ Analysis: deterministic vs probablistic
5. JGomes, SBS IITD 5
History of Systems Biology
Hans V Westerhoff & Bernhard O Palsson, Nature Biotechnology
Vol 22 No 10 Oct 2004
Two Roots
‡ Molecular biology,
with its emphasis on
individual
macromolecules.
‡ formal analysis of
new functional states
that arise when
multiple molecules
interact
simultaneously.
6. JGomes, SBS IITD 6
Multi-disciplinary Field
‡ Engineering Principles
‡ Nonlinear systems analysis
‡ Network theory
‡ Abstract mathematics – representation theory,
group theory and graph theory
‡ Nonlinear Thermodynamics
‡ Physics
‡ Chemistry
‡ Biology
7. JGomes, SBS IITD 7
Where do we start ?
‡ A living cell can be viewed as a dynamical system in
which a large number of different substances react
continuously and non‐linearly with one another
‡ It is insufficient to study each part in isolation
‡ Time domain data of concentrations of biologically
important chemicals in living are now available or
possible to measure
It is possible to start with observed time-domain
concentrations of substances and automatically create
both the topology of the network of chemical reactions and
the rates of each reaction?
9. JGomes, SBS IITD 9
AND Compare with an Electronic
Circuit Diagram
10. JGomes, SBS IITD 10
Analogies are not perfect
‡ There are no pipes lines inside the cell
‡ Reactants and products are not restricted with ‘reactors’
‡ Characteristic times of cellular events vary over a wide
range (10‐9 s to 103 s)
‡ Petroleum plants cannot self replicate
‡ Signal molecules are not restricted to electrical channels
‡ Genetics circuits are not restricted by “circuit boards”
‡ Cells generate their own energy
‡ Cells are “ALIVE”
11. JGomes, SBS IITD 11
The levels of cellular information
‡ Define the interactions
for each gene, RNA and
protein
‡ Define each of the
reactions for this
metabolic network
‡ Examine its behavior
Gene
RNA
Proteins
substrates products
Gene
RNA
Proteins
substrates products
Gene Level
RNA level
ith
Enzyme
ith
substrate
i-1th
product
i+1th
substrate
ith
product
Dynamic Boolean Digraph Network Model
12. JGomes, SBS IITD 12
Create a wiring diagram
‡ There are multiple levels of
interaction
‡ At each level, nearly all
processes are nonlinear
‡ Kinetic parameters are
extremely difficult find
because the questions being
asked are very different
Signal
molecules
Gene
RNA
Proteins
substrates products
Models are computationally
intensive. However, there is a
need for theoretical analysis
because it will not be feasible to
carry out all the experiments
required to address a problem
13. JGomes, SBS IITD 13
Method of approach
‡ Study the properties of individual signaling elements,
then elementary circuits
‡ Then try to understand the more complex networks,
perhaps in the same way that electrical engineers work
their way up from the properties of resistors, capacitors
and diodes, to those of simple circuits and, finally,
complex devices.
‡ Then follow the chemical engineers in building up a cell
factory using electrical (=signals, gene regulation),
mechanical (=biophysics, membrane) and chemical
(=cellular processes)
14. JGomes, SBS IITD 14
Normally ON Normally OFF
Normally OFF Normally ON
Mechanism
Negative and Positive Control
Defined by the response of the operon when no
regulator protein is present
‡ Genes under negative control are expressed
unless they are switched off by a repressor
protein
„ Fail‐safe mechanism: cell is not deprived of these
enzymes even if the regulator protein is absent
‡ Genes under positive control, are expressed only
when an active protein regulator is present
„ Not clear how this mechanism evolved; clearly,
either extrinsic or intrinsic events are necessary for
positive control to trigger
Nature of regulation
1, 0, [0, 1]
15. JGomes, SBS IITD 15
Negative Control of Lactose Operon
y a
z
o
p
i
p
i mRNA
repressor
y a
z
o
p
i
p
i mRNA lac mRNA
Β-galactosidase permease transacetylase
An example of negative control
An example of negative control
16. JGomes, SBS IITD 16
Depiction of negative and positive
control
Gene-A Protein-A
Repressor-A
Gene-A Protein-A
Activator-A
Negative control
Positive control
17. JGomes, SBS IITD 17
Double negative feedback loop
Bistable signal transduction circuits
Bistable signal transduction circuits
‡ A double‐negative feedback loop. In this circuit, protein A (blue) inhibits or
represses B (red), and protein B inhibits or represses A. Thus there could be a
stable steady state with A on and B off, or one with B on and A off, but there
cannot be a stable steady with both A and B on or both A and B off. Such a
circuit could toggle between an A‐on state and a B‐on state in response to
trigger stimuli that impinge upon the feedback circuit.
‡ A positive feedback loop. In this circuit, A activates B and B activates A. As a
result, there could be a stable steady state with both A and B off, or one with
both A and B on, but not one with A on and B off or vice versa. Both types of
circuits could exhibit persistent, self‐perpetuating responses long after the
triggering stimulus is removed.
18. JGomes, SBS IITD 18
Feedback Regulation Motif in Genetic
Circuits
Regulation
Encarta Dictionary: an official rule, law, or order stating what may or may not
be done or how something must be done
Control Theory: control of a variable at desired set point or trajectory by
manipulating input variables
Biology: the phenomenon by which certain biomolecules either directly or
indirectly alter the rate of synthesis of other biomolecules
Related terms: regulator genes, regulator proteins, regulator RNA, regulatory
networks
Event
Measure
Desired
outcome
Mechanism
+_
Negative feedback can bring about oscillations
19. JGomes, SBS IITD 19
Examples of Genetic Circuits
a) Design of the system {Gardner et al.
genetic toggle switch in Escherichia coli.
Nature 2000,403:339‐342} engineered
two double‐negative feedback
systems into E. coli. In the system
shown here, LacI represses the
expression of TetR (and GFP, used
as a reporter of the status of tetR
transcription), and TetR represses
the expression of LacI.
b) Response of the system. The
system could be made to toggle
between the TetR‐off and TetR‐on
states by the addition of external
trigger stimuli: IPTG to disinhibit
tetR, and anhydrotetracycline (aTc)
to disinhibit lacI.
20. JGomes, SBS IITD 20
Transcription Networks
‡ The cells requires different proteins for different
circumstances
‡ PTS proteins for transport of sugar from environment into
cytosol
‡ Different repair proteins if cell is damaged
‡ This information processing is carried out
largely by transcription networks
Signal 1 Signal 2 Signal 3 Signal N
x1 x2 xM
Gene A Gene B Gene k
21. JGomes, SBS IITD 21
Nodes and Edges
In the context of genetic circuits or gene networks,
a node represents an active biological species
such as a gene, enzyme or signal molecule.
Likewise, an edge depicts the connection and the
nature of the connection for between them
Self edge: an edge that starts and ends on the same
node
Directed edge: an edge that carries information and
indicates the direction of movement of that
information
Ni
Ei
N1
N2 N3
N4
E12 E13
E24 E34
E4∞
22. JGomes, SBS IITD 22
Graphs
A graph G is a finite nonempty set V together with an irreflexive
symmetric relation R on V. Since R is symmetric, for each ordered pair
(u, v) ∈ R, the pair (v, u) also belongs to R. We denote by E the
symmetric pairs in R.
Consider for example the graph G defined by
V = {v1, v2, v3, v4} together with the relation
R = {(v1, v2),(v1, v3), (v2, v1), (v2, v3),(v3, v1)(v3, v2), (v3, v4),(v4, v3)}
In this case,
E = [{(v1, v2), (v2, v1), (v3, v1) (v1, v3), (v2, v3), (v3, v2), (v3, v4), (v4, v3)}]
V is the vertex set
The number of vertices is called the order of G
Each element of E consists of two symmetric ordered pairs from R
E is called the edge set of G
Hence
|V| = order of G and |E| = size of G
24. JGomes, SBS IITD 24
Circadian Clocks
‡ Cyclic behavior exhibited by genetic circuits
‡ It is possible to describe these changes by using
ordinary differential equations
‡ Code it in MATLAB (for example)
25. JGomes, SBS IITD 25
Role of Ca2+ Signaling in
Neurodegenerative disorders
‡ A sustained high level of Ca2+ can lead to cell
apoptosis
‡ How easy/difficult is it for this to happen?
‡ What kind of failures can lead to these
conditions?
We try to understand this in the framework
developed so far
30. JGomes, SBS IITD 30
Failure Condition Analysis
Material flow failure – robust response – safe
„ Elevated blood glucose
„ Elevated blood glutamine
Signaling Failure
„ Changes in binding affinity – robust response – safe
„ Changes in CICR (Ca induced Ca release) – sensitive
– can cause a disorder
31. JGomes, SBS IITD 31
CICR Failure Analysis
0
5
10
15
20
25
30
0 600 1200 1800 2400 3000 3600
Time (s)
Calcium
signal
(uM)
0.0E+00
2.0E-05
4.0E-05
6.0E-05
8.0E-05
1.0E-04
1.2E-04
Dephosphorylated
BAD
(uM)
CaC2
BAD
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 600 1200 1800 2400 3000 3600
Time (s)
Calcium
signal
(uM)
0.0E+00
2.0E-05
4.0E-05
6.0E-05
8.0E-05
1.0E-04
1.2E-04
Dephosphorylated
sugnal
(uM)
CaC2
BAD
0
0.1
0.2
0.3
0.4
0.5
0.6
100 120 140 160 180 200
Time (s)
Calcium
signal
(uM)
A sustained Ca2+ signal triggers dephosphorylation of BAD. The level of
the signal influences the time it takes to trigger. Once BAD is triggered
apoptosis sets in.
32. JGomes, SBS IITD 32
SUMMARY
Systems Biology is an emerging area with the
potential of making a significant contribution to
human life
‡ Drug targeting
‡ Drug designing
‡ Elucidating basic principles
‡ Prediction of disease conditions