The document discusses using Markov Chain Monte Carlo sampling to randomly sample large numbers of viable metabolic networks from a vast space of possible metabolic networks in order to study how environmental versatility affects modularity in metabolic networks. It presents a framework for modeling metabolic genotypes as binary strings representing which reactions are present or absent and using flux balance analysis to determine viability. The goal is to ask whether versatility in sustaining life across different chemical environments promotes modularity in large-scale metabolic networks.
The concept of equivalence points (EP) runs like a golden thread through acid-base theory and applications. There are different types of equivalence points. This article provides a classification of EPs and semi-EPs. This is done for the general case of N-protic acids (based on simple mathematical equations).
This document discusses several concepts related to buffers, including:
1) Calculating the pH of an acetate buffer with given concentrations. The pH is calculated to be 4.67.
2) Choosing an appropriate buffer to achieve a target pH of 4.30 from given options based on their pKa values. Acetate is chosen.
3) Calculating the acid concentration needed for a 0.100 M base concentration to achieve the target pH. The acid concentration is calculated to be 0.28 M.
IB Chemistry on Homologous series and functional groups of organic moleculesLawrence kok
The document discusses organic functional groups and their IUPAC nomenclature rules. It defines classes of organic compounds such as alkanes, alkenes, alkynes, alcohols, ethers, ketones, aldehydes, carboxylic acids, esters, amides, amines, nitriles and haloalkanes based on their functional groups. It provides examples and molecular formulas for different functional groups and discusses IUPAC nomenclature rules for systematically naming organic compounds including identifying the parent chain, functional group, substituents and their positions.
Derivation of mathematical closed-from equations for multiprotic acids, which allow the calculation of titration curves and buffer intensities (with Excel). Examples are given for acetic acid, carbonic acid, phosphoric acid, and citric acid.
The document summarizes cellular respiration and the processes of glycolysis, the citric acid cycle, and the electron transport chain. Glycolysis breaks down glucose into pyruvate, producing 2 ATP and 2 NADH. The citric acid cycle further oxidizes pyruvate through a series of reactions, producing 8 NADH, 2 FADH2, and 2 ATP. The electron transport chain uses the electron carriers NADH and FADH2 to produce large amounts of ATP through oxidative phosphorylation.
This study investigated the polymerization of lactic acid as a model for prebiotic peptide formation via ester-amide exchange. Lactic acid was polymerized in a closed system at 85°C over various time points. HPLC and 1H-NMR were used to analyze the polymers and determine degree of polymerization (DP) and total lactic acid units. DP was found to increase with time while total units decreased, showing polymer regeneration. Methods showed consistent results within 10-15% error. Further studies will compare kinetics to a computer simulation to determine rate constants and model polymerization from various monomers.
The concept of equivalence points (EP) runs like a golden thread through acid-base theory and applications. There are different types of equivalence points. This article provides a classification of EPs and semi-EPs. This is done for the general case of N-protic acids (based on simple mathematical equations).
This document discusses several concepts related to buffers, including:
1) Calculating the pH of an acetate buffer with given concentrations. The pH is calculated to be 4.67.
2) Choosing an appropriate buffer to achieve a target pH of 4.30 from given options based on their pKa values. Acetate is chosen.
3) Calculating the acid concentration needed for a 0.100 M base concentration to achieve the target pH. The acid concentration is calculated to be 0.28 M.
IB Chemistry on Homologous series and functional groups of organic moleculesLawrence kok
The document discusses organic functional groups and their IUPAC nomenclature rules. It defines classes of organic compounds such as alkanes, alkenes, alkynes, alcohols, ethers, ketones, aldehydes, carboxylic acids, esters, amides, amines, nitriles and haloalkanes based on their functional groups. It provides examples and molecular formulas for different functional groups and discusses IUPAC nomenclature rules for systematically naming organic compounds including identifying the parent chain, functional group, substituents and their positions.
Derivation of mathematical closed-from equations for multiprotic acids, which allow the calculation of titration curves and buffer intensities (with Excel). Examples are given for acetic acid, carbonic acid, phosphoric acid, and citric acid.
The document summarizes cellular respiration and the processes of glycolysis, the citric acid cycle, and the electron transport chain. Glycolysis breaks down glucose into pyruvate, producing 2 ATP and 2 NADH. The citric acid cycle further oxidizes pyruvate through a series of reactions, producing 8 NADH, 2 FADH2, and 2 ATP. The electron transport chain uses the electron carriers NADH and FADH2 to produce large amounts of ATP through oxidative phosphorylation.
This study investigated the polymerization of lactic acid as a model for prebiotic peptide formation via ester-amide exchange. Lactic acid was polymerized in a closed system at 85°C over various time points. HPLC and 1H-NMR were used to analyze the polymers and determine degree of polymerization (DP) and total lactic acid units. DP was found to increase with time while total units decreased, showing polymer regeneration. Methods showed consistent results within 10-15% error. Further studies will compare kinetics to a computer simulation to determine rate constants and model polymerization from various monomers.
Mathematical description of an acid-base system using the tableaux method (including proton balance and mole balance). Equivalence points for carbonic acid are calculated.
Andrey rubin target photosynthetic products and chlorophyll fluorescenceigorod
1. The document discusses primary photosynthetic processes and how light, carbon dioxide, and other molecules are used to produce oxygen and carbohydrates through pathways in chloroplasts.
2. It also examines factors like light intensity, nutrient deficiencies, and heavy metals that can affect photosynthetic efficiency and productivity as measured by chlorophyll fluorescence.
3. Kinetic models are presented to describe the movement of electrons, protons, and other particles involved in photosynthesis and how their interaction generates electrochemical potentials and drives ATP synthesis.
1. The document describes an electrochemical study of catechol and some 3-substituted catechols (including 3-methylcatechol, 3-methoxycatechol, and 2,3-dihydroxybenzoic acid) in the presence of 4-hydroxycoumarin.
2. The results indicate that the catechols undergo a 1,4 Michael addition reaction with 4-hydroxycoumarin upon electrochemical oxidation, producing coumestan derivative products.
3. Electrochemical synthesis of the coumestan derivatives was successfully performed in an undivided cell in good yield and purity using controlled-potential electrolysis. The products were characterized using various analytical techniques.
The document describes the development and application of high throughput organic synthesis (HTOS) and resin capture (RECAP) technology. It discusses using this approach to rapidly generate libraries of compounds and map the structure-activity relationship for antagonists of the mGluR5 receptor. HTOS and RECAP allowed for the synthesis of over 190 compounds related to a dihydropyridone scaffold in only 72% purity. This identified substituents that increased the potency of mGluR5 antagonism, such as an ortho-substituted aryl or heterocyclic moiety. A second round of libraries with this substitution pattern identified additional novel heterocycles with improved activity.
Single- and Double- Coordination Mechanism in Ethylene Tri- and Tetramerizati...Craig Young
This document discusses a study of the mechanism of ethylene trimerization and tetramerization using a chromium-diphosphinoamine catalyst system. The study finds:
1) In addition to 1-hexene and 1-octene, the reaction produces cyclopentanes, n-alkanes, and higher alpha-olefins as coproducts.
2) The amounts of coproducts other than 1-hexene increase more than proportionally with increasing ethylene pressure, whereas 1-hexene increases approximately proportionally.
3) A theoretical study suggests a mechanism involving both mono-coordinated and bis-coordinated chromium intermediates. The bis-coordinated
Building a bridge between human-readable and machine-readable representations...NextMove Software
This document discusses approaches for representing biopolymers such as peptides in both human-readable and machine-readable formats. It describes names that are preferred by machines, such as SMILES and IUPAC condensed representations, as well as names preferred by humans using the standard 3-letter amino acid codes and other conventions. The document then discusses developing a naming system that is suitable for both humans and machines by text-mining peptide representations from literature to identify commonly used abbreviations, codes, and modifications. The goal is to develop a systematic naming approach that captures this real-world peptide terminology while also enabling computational interpretation and processing.
Cellular respiration is a catabolic process that occurs in the mitochondria of plants and animals. It uses oxygen to break down glucose and other food molecules to extract energy in the form of ATP. The process involves four main stages: glycolysis, the Krebs cycle, and the electron transport chain and oxidative phosphorylation. This releases energy to produce up to 38 ATP per glucose molecule.
The citric acid cycle (CAC) is the final common pathway for the oxidation of nutrients. It occurs in the mitochondria of cells. Acetyl-CoA from various sources enters the CAC and is oxidized to CO2, producing reduced cofactors that drive ATP synthesis. The 8-step cycle produces ATP, GTP, and reduced cofactors NADH and FADH2. Key enzymes and cofactors regulate the cycle in response to energy demands and product inhibition. Anaplerotic reactions maintain CAC intermediate levels.
Cellular respiration involves the breakdown of glucose in the presence of oxygen to release energy. Glucose is broken down into pyruvate which enters the mitochondria, undergoing further breakdown through glycolysis, the Krebs cycle, and electron transport chain. This releases carbon dioxide, water, and generates up to 38 ATP molecules per glucose molecule for energy in the cell.
Cellular respiration is a catabolic process that uses oxygen to break down glucose and other organic molecules to extract energy in the form of ATP. It occurs in four main stages: 1) glycolysis in the cytosol, 2) transport of pyruvate into the mitochondria, 3) the Krebs cycle in the mitochondrial matrix, and 4) the electron transport chain and oxidative phosphorylation on the inner mitochondrial membrane. The overall process produces 38 ATP molecules from complete oxidation of one glucose molecule.
- Location: cytosol and mitochondrion
- Initial reactant: glucose
- Final products: pyruvate (glycolysis), CO2, ATP, NADH, FADH2 (cellular respiration)
- Glycolysis yields 2 ATP and 2 NADH. The citric acid cycle and oxidative phosphorylation together yield approximately 38 ATP.
The document summarizes cellular respiration and the roles of glycolysis, the Krebs cycle, and the electron transport chain in producing ATP from glucose. It explains that:
1) Glycolysis partially oxidizes glucose to produce pyruvate and generate 2 ATP and 2 NADH.
2) Pyruvate then enters the mitochondria and is further oxidized through a series of reactions to produce acetyl-CoA, CO2 and more NADH.
3) The Krebs cycle fully oxidizes acetyl-CoA in 8 steps to produce more electron carriers like NADH and FADH2, along with only 2 ATP.
4) These electron carriers are then used
The document summarizes the process of cellular respiration, which occurs in three main stages. In glycolysis, glucose is broken down to produce pyruvate along with a small amount of ATP. Pyruvate then enters the mitochondria where it is further oxidized through a series of reactions to produce acetyl-CoA. This is fed into the Krebs cycle where it undergoes additional reactions to yield more electron carriers like NADH and FADH2. While the Krebs cycle directly produces only 2 ATP, it generates the electron carriers that will ultimately be used by the electron transport chain to produce the majority of ATP through oxidative phosphorylation.
1. Cellular respiration consists of three main stages: glycolysis, the citric acid cycle, and oxidative phosphorylation.
2. Glycolysis breaks down glucose in the cytoplasm, producing pyruvate and a small amount of ATP. Pyruvate then enters the mitochondria.
3. In the citric acid cycle, pyruvate is combined with coenzyme A and oxidized, producing carbon dioxide, NADH, FADH2, and a small amount of ATP.
4. During oxidative phosphorylation, electrons from NADH and FADH2 are passed through an electron transport chain, pumping protons across the inner mitochondrial membrane and building a proton gradient. ATP synthase uses this
Cellular respiration involves three main stages:
1) Glycolysis breaks down glucose into pyruvate, producing a small amount of ATP.
2) The citric acid cycle further breaks down pyruvate and produces more ATP, NADH, and FADH2.
3) Oxidative phosphorylation uses the electrons from NADH and FADH2 to power the electron transport chain, producing large amounts of ATP through chemiosmosis. This multi-step process efficiently harvests energy from organic molecules to produce usable chemical energy in the form of ATP.
The document summarizes key concepts about cellular respiration:
1. Cellular respiration involves three main stages - glycolysis, the Krebs cycle, and the electron transport chain - to harvest chemical energy from glucose and produce ATP through redox reactions and oxidative phosphorylation.
2. Glycolysis converts glucose to pyruvate, producing a small amount of ATP. The Krebs cycle further oxidizes pyruvate and generates more ATP, NADH, and FADH2.
3. The electron transport chain uses the NADH and FADH2 to power oxidative phosphorylation as electrons are passed to oxygen. This final stage produces the majority of ATP through chemiosmosis.
Cell respiration involves the controlled release of energy through the breakdown of organic molecules like glucose. There are two main types: aerobic respiration, which requires oxygen and produces carbon dioxide and water; and anaerobic respiration, which does not require oxygen. Aerobic respiration occurs in three main stages - glycolysis in the cytoplasm, the link reaction in the mitochondria, and the Krebs cycle and electron transport chain in the mitochondrial matrix. This process generates ATP through redox reactions and chemiosmosis.
Cellular respiration involves a series of chemical reactions that convert glucose into energy in the form of ATP. Glucose undergoes glycolysis to form pyruvic acid, which then enters the Krebs cycle in the mitochondria. Electrons are passed through the electron transport chain, releasing energy to produce ATP. Oxygen is the final electron acceptor in this process, with carbon dioxide and water as byproducts. This allows living cells to harness the energy stored in glucose and other nutrients to power their functions.
The citric acid cycle (TCA cycle) is a central metabolic pathway that catalyzes the oxidation of acetyl-CoA derived from carbohydrates, fats, and proteins, producing carbon dioxide and reducing equivalents in the form of NADH and FADH2 that are used in oxidative phosphorylation. It was established in the 1930s that TCA cycle intermediates stimulated oxygen consumption and CO2 release. Key discoveries in the 1940s-1950s showed that acetyl-CoA is the intermediate connecting pyruvate to the TCA cycle, and that the TCA cycle enzymes are organized in the mitochondrial matrix.
Cellular respiration involves three main stages - glycolysis, the Krebs cycle, and the electron transport chain. Glycolysis breaks down glucose into pyruvate and produces a small amount of ATP. In the presence of oxygen, pyruvate enters the mitochondria and is further oxidized in the Krebs cycle, producing more ATP, NADH, and FADH2. These reduced coenzymes donate their electrons to the electron transport chain, where oxygen is the final electron acceptor. This powers ATP synthase to produce the majority of the cell's ATP through chemiosmosis. In total, the complete oxidation of one glucose molecule yields approximately 38 ATP.
The document summarizes key concepts about energy transformations that occur in the human body through respiration and oxidative phosphorylation. Chemical energy from nutrients is converted to ATP through substrate-level phosphorylation and oxidative phosphorylation. Oxidative phosphorylation uses the electron transport chain in the mitochondria to generate a proton gradient across the inner mitochondrial membrane, which drives ATP synthesis from ADP and inorganic phosphate.
The document summarizes cellular respiration, which consists of three main stages: glycolysis, the citric acid cycle, and oxidative phosphorylation. Glycolysis breaks down glucose into pyruvate and generates a small amount of ATP. The citric acid cycle further oxidizes pyruvate and generates more ATP, NADH, and FADH2. During oxidative phosphorylation, electrons from NADH and FADH2 are passed through an electron transport chain where their energy is used to pump protons across a membrane and generate a proton gradient. ATP synthase uses this proton gradient to generate most of the cell's ATP through chemiosmosis.
Mathematical description of an acid-base system using the tableaux method (including proton balance and mole balance). Equivalence points for carbonic acid are calculated.
Andrey rubin target photosynthetic products and chlorophyll fluorescenceigorod
1. The document discusses primary photosynthetic processes and how light, carbon dioxide, and other molecules are used to produce oxygen and carbohydrates through pathways in chloroplasts.
2. It also examines factors like light intensity, nutrient deficiencies, and heavy metals that can affect photosynthetic efficiency and productivity as measured by chlorophyll fluorescence.
3. Kinetic models are presented to describe the movement of electrons, protons, and other particles involved in photosynthesis and how their interaction generates electrochemical potentials and drives ATP synthesis.
1. The document describes an electrochemical study of catechol and some 3-substituted catechols (including 3-methylcatechol, 3-methoxycatechol, and 2,3-dihydroxybenzoic acid) in the presence of 4-hydroxycoumarin.
2. The results indicate that the catechols undergo a 1,4 Michael addition reaction with 4-hydroxycoumarin upon electrochemical oxidation, producing coumestan derivative products.
3. Electrochemical synthesis of the coumestan derivatives was successfully performed in an undivided cell in good yield and purity using controlled-potential electrolysis. The products were characterized using various analytical techniques.
The document describes the development and application of high throughput organic synthesis (HTOS) and resin capture (RECAP) technology. It discusses using this approach to rapidly generate libraries of compounds and map the structure-activity relationship for antagonists of the mGluR5 receptor. HTOS and RECAP allowed for the synthesis of over 190 compounds related to a dihydropyridone scaffold in only 72% purity. This identified substituents that increased the potency of mGluR5 antagonism, such as an ortho-substituted aryl or heterocyclic moiety. A second round of libraries with this substitution pattern identified additional novel heterocycles with improved activity.
Single- and Double- Coordination Mechanism in Ethylene Tri- and Tetramerizati...Craig Young
This document discusses a study of the mechanism of ethylene trimerization and tetramerization using a chromium-diphosphinoamine catalyst system. The study finds:
1) In addition to 1-hexene and 1-octene, the reaction produces cyclopentanes, n-alkanes, and higher alpha-olefins as coproducts.
2) The amounts of coproducts other than 1-hexene increase more than proportionally with increasing ethylene pressure, whereas 1-hexene increases approximately proportionally.
3) A theoretical study suggests a mechanism involving both mono-coordinated and bis-coordinated chromium intermediates. The bis-coordinated
Building a bridge between human-readable and machine-readable representations...NextMove Software
This document discusses approaches for representing biopolymers such as peptides in both human-readable and machine-readable formats. It describes names that are preferred by machines, such as SMILES and IUPAC condensed representations, as well as names preferred by humans using the standard 3-letter amino acid codes and other conventions. The document then discusses developing a naming system that is suitable for both humans and machines by text-mining peptide representations from literature to identify commonly used abbreviations, codes, and modifications. The goal is to develop a systematic naming approach that captures this real-world peptide terminology while also enabling computational interpretation and processing.
Cellular respiration is a catabolic process that occurs in the mitochondria of plants and animals. It uses oxygen to break down glucose and other food molecules to extract energy in the form of ATP. The process involves four main stages: glycolysis, the Krebs cycle, and the electron transport chain and oxidative phosphorylation. This releases energy to produce up to 38 ATP per glucose molecule.
The citric acid cycle (CAC) is the final common pathway for the oxidation of nutrients. It occurs in the mitochondria of cells. Acetyl-CoA from various sources enters the CAC and is oxidized to CO2, producing reduced cofactors that drive ATP synthesis. The 8-step cycle produces ATP, GTP, and reduced cofactors NADH and FADH2. Key enzymes and cofactors regulate the cycle in response to energy demands and product inhibition. Anaplerotic reactions maintain CAC intermediate levels.
Cellular respiration involves the breakdown of glucose in the presence of oxygen to release energy. Glucose is broken down into pyruvate which enters the mitochondria, undergoing further breakdown through glycolysis, the Krebs cycle, and electron transport chain. This releases carbon dioxide, water, and generates up to 38 ATP molecules per glucose molecule for energy in the cell.
Cellular respiration is a catabolic process that uses oxygen to break down glucose and other organic molecules to extract energy in the form of ATP. It occurs in four main stages: 1) glycolysis in the cytosol, 2) transport of pyruvate into the mitochondria, 3) the Krebs cycle in the mitochondrial matrix, and 4) the electron transport chain and oxidative phosphorylation on the inner mitochondrial membrane. The overall process produces 38 ATP molecules from complete oxidation of one glucose molecule.
- Location: cytosol and mitochondrion
- Initial reactant: glucose
- Final products: pyruvate (glycolysis), CO2, ATP, NADH, FADH2 (cellular respiration)
- Glycolysis yields 2 ATP and 2 NADH. The citric acid cycle and oxidative phosphorylation together yield approximately 38 ATP.
The document summarizes cellular respiration and the roles of glycolysis, the Krebs cycle, and the electron transport chain in producing ATP from glucose. It explains that:
1) Glycolysis partially oxidizes glucose to produce pyruvate and generate 2 ATP and 2 NADH.
2) Pyruvate then enters the mitochondria and is further oxidized through a series of reactions to produce acetyl-CoA, CO2 and more NADH.
3) The Krebs cycle fully oxidizes acetyl-CoA in 8 steps to produce more electron carriers like NADH and FADH2, along with only 2 ATP.
4) These electron carriers are then used
The document summarizes the process of cellular respiration, which occurs in three main stages. In glycolysis, glucose is broken down to produce pyruvate along with a small amount of ATP. Pyruvate then enters the mitochondria where it is further oxidized through a series of reactions to produce acetyl-CoA. This is fed into the Krebs cycle where it undergoes additional reactions to yield more electron carriers like NADH and FADH2. While the Krebs cycle directly produces only 2 ATP, it generates the electron carriers that will ultimately be used by the electron transport chain to produce the majority of ATP through oxidative phosphorylation.
1. Cellular respiration consists of three main stages: glycolysis, the citric acid cycle, and oxidative phosphorylation.
2. Glycolysis breaks down glucose in the cytoplasm, producing pyruvate and a small amount of ATP. Pyruvate then enters the mitochondria.
3. In the citric acid cycle, pyruvate is combined with coenzyme A and oxidized, producing carbon dioxide, NADH, FADH2, and a small amount of ATP.
4. During oxidative phosphorylation, electrons from NADH and FADH2 are passed through an electron transport chain, pumping protons across the inner mitochondrial membrane and building a proton gradient. ATP synthase uses this
Cellular respiration involves three main stages:
1) Glycolysis breaks down glucose into pyruvate, producing a small amount of ATP.
2) The citric acid cycle further breaks down pyruvate and produces more ATP, NADH, and FADH2.
3) Oxidative phosphorylation uses the electrons from NADH and FADH2 to power the electron transport chain, producing large amounts of ATP through chemiosmosis. This multi-step process efficiently harvests energy from organic molecules to produce usable chemical energy in the form of ATP.
The document summarizes key concepts about cellular respiration:
1. Cellular respiration involves three main stages - glycolysis, the Krebs cycle, and the electron transport chain - to harvest chemical energy from glucose and produce ATP through redox reactions and oxidative phosphorylation.
2. Glycolysis converts glucose to pyruvate, producing a small amount of ATP. The Krebs cycle further oxidizes pyruvate and generates more ATP, NADH, and FADH2.
3. The electron transport chain uses the NADH and FADH2 to power oxidative phosphorylation as electrons are passed to oxygen. This final stage produces the majority of ATP through chemiosmosis.
Cell respiration involves the controlled release of energy through the breakdown of organic molecules like glucose. There are two main types: aerobic respiration, which requires oxygen and produces carbon dioxide and water; and anaerobic respiration, which does not require oxygen. Aerobic respiration occurs in three main stages - glycolysis in the cytoplasm, the link reaction in the mitochondria, and the Krebs cycle and electron transport chain in the mitochondrial matrix. This process generates ATP through redox reactions and chemiosmosis.
Cellular respiration involves a series of chemical reactions that convert glucose into energy in the form of ATP. Glucose undergoes glycolysis to form pyruvic acid, which then enters the Krebs cycle in the mitochondria. Electrons are passed through the electron transport chain, releasing energy to produce ATP. Oxygen is the final electron acceptor in this process, with carbon dioxide and water as byproducts. This allows living cells to harness the energy stored in glucose and other nutrients to power their functions.
The citric acid cycle (TCA cycle) is a central metabolic pathway that catalyzes the oxidation of acetyl-CoA derived from carbohydrates, fats, and proteins, producing carbon dioxide and reducing equivalents in the form of NADH and FADH2 that are used in oxidative phosphorylation. It was established in the 1930s that TCA cycle intermediates stimulated oxygen consumption and CO2 release. Key discoveries in the 1940s-1950s showed that acetyl-CoA is the intermediate connecting pyruvate to the TCA cycle, and that the TCA cycle enzymes are organized in the mitochondrial matrix.
Cellular respiration involves three main stages - glycolysis, the Krebs cycle, and the electron transport chain. Glycolysis breaks down glucose into pyruvate and produces a small amount of ATP. In the presence of oxygen, pyruvate enters the mitochondria and is further oxidized in the Krebs cycle, producing more ATP, NADH, and FADH2. These reduced coenzymes donate their electrons to the electron transport chain, where oxygen is the final electron acceptor. This powers ATP synthase to produce the majority of the cell's ATP through chemiosmosis. In total, the complete oxidation of one glucose molecule yields approximately 38 ATP.
The document summarizes key concepts about energy transformations that occur in the human body through respiration and oxidative phosphorylation. Chemical energy from nutrients is converted to ATP through substrate-level phosphorylation and oxidative phosphorylation. Oxidative phosphorylation uses the electron transport chain in the mitochondria to generate a proton gradient across the inner mitochondrial membrane, which drives ATP synthesis from ADP and inorganic phosphate.
The document summarizes cellular respiration, which consists of three main stages: glycolysis, the citric acid cycle, and oxidative phosphorylation. Glycolysis breaks down glucose into pyruvate and generates a small amount of ATP. The citric acid cycle further oxidizes pyruvate and generates more ATP, NADH, and FADH2. During oxidative phosphorylation, electrons from NADH and FADH2 are passed through an electron transport chain where their energy is used to pump protons across a membrane and generate a proton gradient. ATP synthase uses this proton gradient to generate most of the cell's ATP through chemiosmosis.
The citric acid cycle (CAC), also known as the Krebs cycle or tricarboxylic acid (TCA) cycle, is a series of chemical reactions used by all aerobic organisms to generate energy through the oxidation of acetyl-CoA derived from carbohydrates, fats, and proteins. The cycle consists of eight sequential reactions that generate high-energy electron carriers and carbon dioxide. Each turn of the cycle produces 3 NADH molecules, 1 FADH2, and 1 GTP that fuel oxidative phosphorylation and generate ATP. The cycle takes place exclusively in the mitochondrial matrix and is crucial for cellular respiration.
Cellular respiration involves the breakdown of glucose and other organic molecules to extract energy in the form of ATP. It occurs in three main stages: glycolysis, the Krebs cycle in the mitochondria, and oxidative phosphorylation along the electron transport chain. During aerobic respiration, glucose breakdown yields about 38 ATP molecules total. Without oxygen, anaerobic respiration produces less ATP but allows glycolysis to continue by converting pyruvate into other waste products like lactic acid.
Cellular respiration involves the breakdown of glucose and other organic molecules to extract energy in the form of ATP. It occurs in three main stages: glycolysis, the Krebs cycle in the mitochondria, and oxidative phosphorylation along the electron transport chain. This releases a total of 38 ATP per glucose molecule in the presence of oxygen through redox reactions involving NADH and FADH2 as electron carriers. Without oxygen, anaerobic respiration produces less ATP through pathways like lactic acid fermentation.
The document summarizes key concepts about cellular metabolism. It discusses:
1) Metabolism is the set of chemical reactions within organisms and involves coupling of exergonic and endergonic reactions.
2) Catabolic pathways like glycolysis and the citric acid cycle break down molecules to generate ATP. Glycolysis occurs in the cytoplasm and citric acid cycle in the mitochondria.
3) Anabolic pathways like protein synthesis require ATP to build large biomolecules. Protein synthesis involves transcription of DNA to mRNA and translation to a polypeptide chain.
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.
This document discusses several computational systems biology techniques for studying metabolic regulation, including flux balance analysis (FBA). It provides an introduction to FBA, explaining that it is used to compute reaction fluxes and growth rates based on steady-state assumptions. The key inputs needed for FBA are the list of reactions in the metabolic network along with stoichiometric coefficients, the biomass composition, and which metabolites can be uptaken. The output of FBA is the predicted steady-state fluxes of all reactions and maximum growth rate.
Areejit Samal Preferential Attachment in Catalytic ModelAreejit Samal
1) The document describes an evolving network model and a modified version that incorporates preferential attachment. In the original model by Jain and Krishna, crashes often occurred due to core-shifts or complete crashes as the network evolved.
2) The modified model with preferential attachment leads to faster formation of the first autocatalytic set and transition to an organized phase. It also makes crashes extremely rare.
3) Networks in the organized phase of the preferential attachment model have denser cores with more fundamental loops, resulting in higher robustness against crashes compared to the original model.
The document describes a study of the genetic regulatory network in E.coli that controls metabolism. It details the network contained in the iMC1010v1 database, which includes 479 genes encoding metabolic enzymes, 104 genes encoding transcription factors, and dependencies on 96 external metabolites. A Boolean model is used to simulate the network, where each gene is assigned a logical rule determining its on/off state based on the inputs from other genes and environmental factors.
Areejit Samal Randomizing metabolic networksAreejit Samal
The document discusses the large-scale structure of metabolic networks. It describes how metabolic networks exhibit small-world and scale-free properties with hierarchical organization. The networks also display a bow-tie architecture. The document notes that certain network motifs perform important information processing tasks and have been selected by evolution. It discusses the need for carefully constructed null models when identifying design principles, and issues with commonly used edge-exchange algorithms when applied to bipartite metabolic networks.
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.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
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Modularity in metabolic networks
1. Areejit Samal
LPTMS/CNRS, Univ Paris-Sud 11, Orsay, France
and
Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
Environmental versatility promotes modularity in
large-scale metabolic networks
International Conference on Mathematical and Theoretical Biology, Jan 23-27, 2012, Pune, India
Jan 24, 2012
In collaboration
with:
Andreas Wagner
University of Zurich
Olivier C. Martin
Université Paris-Sud
2. Modular Organization of Biological Systems
Spatial modularity:
Organisms are partitioned into organs
and tissues where cells of similar types
are in close proximity.
Topological modularity:
Modules represent subsets of nodes in a
network which are strongly connected to
each other but weakly connected to the
remaining network.
If nodes within a module tend to be involved in the same biological function, then both
spatial and topological modularity point to a general architectural principle:
The parts of an organism that perform specific tasks or functions are grouped
into modules that can function semi-autonomously.
For a review see: LH Hartwell et al, Nature (1999)
3. Motivation
We ask if modularity in large-scale metabolic networks can arise as a by-
product of phenotypic constraints associated with the need to sustain life in
one or more chemical environments.
An organism’s metabolism is highly versatile if it can sustain life in many
different chemical environments.
Using recently developed techniques to randomly sample large numbers of
viable metabolic networks from a vast space of metabolic networks, we use
flux balance analysis to study insilico metabolic networks that differ in their
versatility.
We then ask whether versatility affects the modularity of metabolic networks.
4. Outline
Modeling Framework
MCMC sampling of random viable genotypes
Environmental versatility index and measures of modularity
Results
Conclusions and Outlook
5. Framework: Global Reaction Set
+
Biomass composition
The E. coli biomass
composition of iJR904
was used to determine
viability of genotypes.
Nutrient metabolites
The set of external
metabolites in iJR904
were allowed for
uptake/excretion in the
global reaction set.
KEGG Database + E. coli iJR904
5870 reactions
We can implement Flux
Balance Analysis (FBA)
within this database.
6. Genome-scale metabolic model reconstruction pipeline
Genome sequence with annotation
Biochemical Knowledge
Physiology
Genome-scale reconstructed metabolic network
AbbreviationOfficialName Equation (note [c] and [e] at the beginning refer to the compartment the reaction takes place in, cytosolic and extracellular respectively)Subsystem ProteinClassDescription
ALATA_L L-alanine transaminase [c]akg + ala-L <==> glu-L + pyr Alanine and aspartate metabolismEC-2.6.1.2
ALAR alanine racemase [c]ala-L <==> ala-D Alanine and aspartate metabolismEC-5.1.1.1
ASNN L-asparaginase [c]asn-L + h2o --> asp-L + nh4 Alanine and aspartate metabolismEC-3.5.1.1
ASNS2 asparagine synthetase [c]asp-L + atp + nh4 --> amp + asn-L + h + ppi Alanine and aspartate metabolismEC-6.3.1.1
ASNS1 asparagine synthase (glutamine-hydrolysing) [c]asp-L + atp + gln-L + h2o --> amp + asn-L + glu-L + h + ppiAlanine and aspartate metabolismEC-6.3.5.4
ASPT L-aspartase [c]asp-L --> fum + nh4 Alanine and aspartate metabolismEC-4.3.1.1
ASPTA aspartate transaminase [c]akg + asp-L <==> glu-L + oaa Alanine and aspartate metabolismEC-2.6.1.1
VPAMT Valine-pyruvate aminotransferase [c]3mob + ala-L --> pyr + val-L Alanine and aspartate metabolismEC-2.6.1.66
DAAD D-Amino acid dehydrogenase [c]ala-D + fad + h2o --> fadh2 + nh4 + pyr Alanine and aspartate metabolismEC-1.4.99.1
ALARi alanine racemase (irreversible) [c]ala-L --> ala-D Alanine and aspartate metabolismEC-5.1.1.1
FFSD beta-fructofuranosidase [c]h2o + suc6p --> fru + g6p Alternate Carbon Metabolism EC-3.2.1.26
A5PISO arabinose-5-phosphate isomerase [c]ru5p-D <==> ara5p Alternate Carbon Metabolism EC-5.3.1.13
MME methylmalonyl-CoA epimerase [c]mmcoa-R <==> mmcoa-S Alternate Carbon Metabolism EC-5.1.99.1
MICITD 2-methylisocitrate dehydratase [c]2mcacn + h2o --> micit Alternate Carbon Metabolism EC-4.2.1.99
ALCD19 alcohol dehydrogenase (glycerol) [c]glyald + h + nadh <==> glyc + nad Alternate Carbon Metabolism EC-1.1.1.1
LCADi lactaldehyde dehydrogenase [c]h2o + lald-L + nad --> (2) h + lac-L + nadh Alternate Carbon Metabolism EC-1.2.1.22
TGBPA Tagatose-bisphosphate aldolase [c]tagdp-D <==> dhap + g3p Alternate Carbon Metabolism EC-4.1.2.40
LCAD lactaldehyde dehydrogenase [c]h2o + lald-L + nad <==> (2) h + lac-L + nadh Alternate Carbon Metabolism EC-1.2.1.22
ALDD2x aldehyde dehydrogenase (acetaldehyde, NAD) [c]acald + h2o + nad --> ac + (2) h + nadh Alternate Carbon Metabolism EC-1.2.1.3
ARAI L-arabinose isomerase [c]arab-L <==> rbl-L Alternate Carbon Metabolism EC-5.3.1.4
RBK_L1 L-ribulokinase (L-ribulose) [c]atp + rbl-L --> adp + h + ru5p-L Alternate Carbon Metabolism EC-2.7.1.16
RBP4E L-ribulose-phosphate 4-epimerase [c]ru5p-L <==> xu5p-D Alternate Carbon Metabolism EC-5.1.3.4
ACACCT acetyl-CoA:acetoacetyl-CoA transferase [c]acac + accoa --> aacoa + ac Alternate Carbon Metabolism
BUTCT Acetyl-CoA:butyrate-CoA transferase [c]accoa + but --> ac + btcoa Alternate Carbon Metabolism EC-2.8.3.8
AB6PGH Arbutin 6-phosphate glucohydrolase [c]arbt6p + h2o --> g6p + hqn Alternate Carbon Metabolism EC-3.2.1.86
PMANM phosphomannomutase [c]man1p <==> man6p Alternate Carbon Metabolism EC-5.4.2.8
PPM2 phosphopentomutase 2 (deoxyribose) [c]2dr1p <==> 2dr5p Alternate Carbon Metabolism EC-5.4.2.7
List of metabolic reactions that can occur in an organism along with
gene-protein -reaction (GPR association)
Genome-scale reconstructed metabolic model
ACALDt acetaldehyde reversible transport acald[e] <==> acald[c] Transport, Extracellular
GUAt Guanine transport gua[e] <==> gua[c] Transport, Extracellular
HYXNt Hypoxanthine transport hxan[e] <==> hxan[c] Transport, Extracellular
XANt xanthine reversible transport xan[e] <==> xan[c] Transport, Extracellular
NACUP Nicotinic acid uptake nac[e] --> nac[c] Transport, Extracellular
ASNabc L-asparagine transport via ABC system asn-L[e] + atp[c] + h2o[c] --> adp[c] + asn-L[c] + h[c] + pi[c]Transport, Extracellular
ASNt2r L-asparagine reversible transport via proton symportasn-L[e] + h[e] <==> asn-L[c] + h[c] Transport, Extracellular
DAPabc M-diaminopimelic acid ABC transport 26dap-M[e] + atp[c] + h2o[c] --> 26dap-M[c] + adp[c] + h[c] + pi[c]Transport, Extracellular
CYSabc L-cysteine transport via ABC system atp[c] + cys-L[e] + h2o[c] --> adp[c] + cys-L[c] + h[c] + pi[c]Transport, Extracellular
ACt2r acetate reversible transport via proton symport ac[e] + h[e] <==> ac[c] + h[c] Transport, Extracellular
ETOHt2r ethanol reversible transport via proton symport etoh[e] + h[e] <==> etoh[c] + h[c] Transport, Extracellular
PYRt2r pyruvate reversible transport via proton symport h[e] + pyr[e] <==> h[c] + pyr[c] Transport, Extracellular
O2t o2 transport (diffusion) o2[e] <==> o2[c] Transport, Extracellular
CO2t CO2 transporter via diffusion co2[e] <==> co2[c] Transport, Extracellular
H2Ot H2O transport via diffusion h2o[e] <==> h2o[c] Transport, Extracellular
DHAt Dihydroxyacetone transport via facilitated diffusiondha[e] <==> dha[c] Transport, Extracellular
NH3t ammonia reversible transport nh4[e] <==> nh4[c] Transport, Extracellular
ARBt2r L-arabinose transport via proton symport arab-L[e] + h[e] <==> arab-L[c] + h[c] Transport, Extracellular
Exchange and transport reactions defining possible nutrients
Cellular Objective: Biomass Reaction
(0.05) 5mthf + (5.0E-5) accoa + (0.488) ala-L + (0.0010) amp + (0.281) arg-L + (0.229) asn-L + (0.229) asp-L + (45.7318) atp + (1.29E-4) clpn_EC + (6.0E-6) coa + (0.126) ctp + (0.087) cys-L + (0.0247)
datp + (0.0254) dctp + (0.0254) dgtp + (0.0247) dttp + (1.0E-5) fad + (0.25) gln-L + (0.25) glu-L + (0.582) gly + (0.154) glycogen + (0.203) gtp + (45.5608) h2o + (0.09) his-L + (0.276) ile-L + (0.428) leu-L +
(0.0084) lps_EC + (0.326) lys-L + (0.146) met-L + (0.00215) nad + (5.0E-5) nadh + (1.3E-4) nadp + (4.0E-4) nadph + (0.001935) pe_EC + (0.0276) peptido_EC + (4.64E-4) pg_EC + (0.176) phe-L + (0.21)
pro-L + (5.2E-5) ps_EC + (0.035) ptrc + (0.205) ser-L + (0.0070) spmd + (3.0E-6) succoa + (0.241) thr-L + (0.054) trp-L + (0.131) tyr-L + (0.0030) udpg + (0.136) utp + (0.402) val-L --> (45.5608) adp +
(45.56035) h + (45.5628) pi + (0.7302) ppi
Flux Balance Analysis (FBA) can be used to analyze the capabilities of these models.
7. Framework: Metabolic Genotype
Global Reaction Set List of catalyzed reactions
R1: 3pg + nad → 3php + h + nadh
R2: 6pgc + nadp → co2 + nadph + ru5p-D
R3: 6pgc → 2ddg6p + h2o
.
.
.
RN: ---------------------------------
1
0
1
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1
1
0
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1
1
1
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G1 G2 G3
Genotypes G1 and G3 differ by a single reaction swap. This reaction swap can be decomposed into a
single reaction addition, which produces from G1 a new genotype G2, followed by a single reaction
deletion, which produces G3 from G2.
A metabolic genotype G is any subset of reactions taken from the global reaction
set of N reactions. Such a genotype G can be represented as a binary string of
length N with each reaction i either present (1) or absent (0).
9. Definition: Genotype network
We denote by Ω(n) the vast space of metabolic genotypes with a given number n of
reactions. Even for modest n, Ω(n) is a huge space containing N!/[n! (N - n)!]
genotypes.
We refer to the set or space of viable genotypes within Ω(n) as V(n).
1
0
1
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1
0
0
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1
1
1
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1
1
0
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…………………………………………………………..…
Genotypes with n reactions:
Bit strings with exactly n entries 1.
Space of possible genotypes Ω(n)
Viable genotypes
V(n)
Unviable genotypes
The set of viable genotypes with fixed number of reactions n forms a genotype network or
neutral network
10. Viable genotypes occupy tiny fraction of the
genotype space
Number of reactions in genotype
2000 2200 2400 2600 2800
Fractionofgenotypesthatareviable
100
10-5
10-10
10-15
10-20
Glucose
Acetate
Succinate
Analytic prefactor
We tried to estimate the fraction of viable space by generating random genotypes with a
given number of reactions n and determining their phenotype.
Constraint of having super-
essential reactions
The convex curve indicates that the effect on
viability of successive reaction removals becomes
more and more severe.
The numerical estimation of the fraction
becomes extremely difficult for genotypes
with n<2100 reactions while typical
microbial metabolic networks contain 400-
1200 reactions.
1
0
1
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1
0
0
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1
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1
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1
1
0
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……………………
Generate a sample of random bit strings with
exactly n entries 1.
Count the number of viable bit strings in the
sample.
11. Markov Chain Monte Carlo (MCMC) sampling of viable
genotypes
• MCMC allows one to uniformly
sample the tiny fraction of viable
space by generating a random walk
among viable genotypes, going from
one viable to another by performing a
reaction swap.
• If a swap leads to a non-viable
genotype, the walk stays at the
previous genotype for that step and
then the process is repeated.
G1 G3
G4
Viable genotype Unviable genotype
Random walkReaction swap
Genotype networks in metabolic reaction spaces
Areejit Samal, João F Matias Rodrigues, Jürgen Jost, Olivier C Martin* and Andreas Wagner*
BMC Systems Biology 2010, 4:30
12. MCMC sampling of viable genotypes
1
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E. coli
Accept
Reject
Step 1
Swap Swap
Reject
106 Swaps
Step 2
103 Swaps
store store
Erase
memory of
initial
network
Saving
Frequency
Accept/Reject Criterion of reaction swap:
New network is able to grow on the specified environment
Global Reaction Set
R1: 3pg + nad → 3php + h + nadh
R2: 6pgc + nadp → co2 + nadph + ru5p-D
R3: 6pgc → 2ddg6p + h2o
.
.
.
RN: ---------------------------------
The advantage of using reaction swaps in our approach is that it leaves the number of reactions constant
over time.
Genotype networks in metabolic reaction spaces
Areejit Samal, João F Matias Rodrigues, Jürgen Jost, Olivier C Martin* and Andreas Wagner*
BMC Systems Biology 2010, 4:30
13. Environmental Versatility Index Venv
MCMC sampling algorithm can be used to sample
the space of genotypes having a given phenotype.
The desired phenotype is viability on a given set of
minimal environments.
If the set of minimal environments, consists of Venv
environments, we designate the genotype’s
Environmental Versatility Index to Venv.
Venv =1 refers to genotypes viable in one specific
environment; Venv =2 refers to genotypes viable in
two given environments; and so on.
We have considered 89 minimal environments
whose sole carbon sources are their only
distinguishing feature.
Versatility
Venv
Number of
Sampled
genotypes
1 1000
2 1000
5 1000
10 1000
. .
. .
89 1000
14. Fully Coupled Set (FCS)
A pair of reactions v1 and v2 are said to be fully coupled to each other if a nonzero flux
for v1 implies a proportionate (nonzero) flux for v2 in any steady state and vice versa.
A fully coupled set (FCS) in a metabolic network is a maximal set of reactions that are
mutually fully coupled to each other. Such sets have been referred to as
reaction/enzyme subsets or correlated reaction sets in the literature.
FCSs define functional modules in metabolic network which have been shown to be
biochemically sensible.
Determining all FCSs in a large metabolic network can be done efficiently using linear
optimization.
15. Properties of Fully Coupled Sets (FCSs)
In steady state, each FCS can be replaced by a single effective reaction. FCSs
can be used to coarse-grain metabolic networks.
It has been shown that if one reaction in a FCS is essential, the complete FCS is
essential (Samal et al., BMC Bioinformatics (2006)).
Since, FCSs guarantee flux proportionality among its constituent reactions, FCSs
are more relevant for unveiling functional coupling than graph-based measures
(Samal et al., BMC Bioinformatics (2006), Notebaart et al., PLoS Comp Bio
(2008)).
FCS can be branched and contains cycles.
Example of a FCS in the E. coli metabolic
network that is branched and contains a
cycle: Green (red) edges represent
irreversible (reversible) reactions. The
reactions in this FCS are involved in cell
envelope biosynthesis.
16. Functional measures of modularity in metabolic networks
We defined two indices of metabolic
network modularity based on Fully
Coupled Sets (FCSs) of reactions in a
metabolic network.
M : the number of reactions in a
metabolic network that belong to
different FCSs (modules).
s : the number of FCSs (modules) in
a metabolic network.
Versatility
Venv
Number of
Sampled
genotypes
<M> <s>
1 1000 . .
2 1000
5 1000
10 1000
. .
. .
89 1000 . .
17. Modularity index M increases with versatility
Venv
1 5 10 20 30 40 50 70 90
<M>
240
250
260
270
280
290
300
310
320
Average over different nested sets
(a)
The data shown here are
based on MCMC sampled
genotypes with number
n=831 reactions (same as
in E. coli metabolic model.
The Environmental Versatility Index Venv denotes the number of distinct environments in
which a genotype is viable.
The modularity index M for a genotype gives the number of reactions contained in the FCSs
(modules) of that genotype.
18. Modularity index s with environmental versatility
Venv
12 5 10 20 30 40 50 70 89
<s>
70
75
80
85
90
95
100
Average over different nested sets
(b)
The data shown here are
based on MCMC sampled
genotypes with number
n=831 reactions (same as
in E. coli metabolic model.
The Environmental Versatility Index Venv denotes the number of distinct environments in
which a genotype is viable.
The modularity index s for a genotype gives the number of FCSs (modules) of that
genotype.
19. Environmental versatility enhances M and s regardless of
the value of number n of reactions in a genotype
Venv
1 5 10 20 30 40
<M>
190
200
210
220
230
240
250
260
270
Average over different orders
Venv
1 5 10 20 30 40
<M>
220
230
240
250
260
270
280
Average over different orders
(a)
(b)
Venv
1 5 10 20 30 40
<s>
45
50
55
60
65
70
Average over different orders
Venv
1 5 10 20 30 40
<s>
60
62
64
66
68
70
72
74
76
78
Average over different orders
(a)
(b)
Modularity index M Modularity index s
n = 500
n = 700
20. Higher values of M and s are by-products of increasing versatility
We next wished to check if the additional
reactions accounting for the increase in
modularity would be closely linked to the
additional nutrients that metabolic networks
must utilize as their versatility increases.
These additional reactions and the modules
they reside could occur just downstream of
the nutrients.
The notion of downstream can be made
quantitative through the Scope or Network
expansion algorithm.
21. Network Expansion or Scope algorithm
Reference: Handorf, Ebenhöh and Heinrich J Mol Evol (2005)
This algorithm is based on principle of forward evolution
The scope distance of a reaction from the nutrient metabolites in the seed set is
defined as the iteration number of the step when that particular reaction is first
selected by the algorithm.
Distance 1 Distance 2 Distance 3Distance 0
22. The increase in modularity with environmental versatility can be
attributed to reactions that are close to nutrients
Global reaction set
Scope distance from nutrient metabolites
0 5 10 15 20 25
Frequency
0
100
200
300
400
500
(a) Reactions that account for increase in M
Scope distance from nutrient metabolites
0 5 10 15 20 25
Frequency
0
5
10
15
20
25
30
(b)
Distribution of scope distances from
the nutrients for all possible
reactions.
Distribution of scope distances from
nutrients for those reactions in additional
FCSs that differentiate the sampled
genotypes at Venv=89 from those at Venv =1.
Using Kolmogorov–Smirnov test (K–S test) we could reject the hypothesis that
the two distributions are same at a p-value < 0.00001.
23. Example of FCSs that account for the increase in
modularity with versatility
These FCSs metabolize the nutrients:
fucose, rhamnose and 3-hydroxycinnamic acid.
24. Distribution of M for genotypes in our most constrained
ensemble and comparison with E. coli
M
260 280 300 320 340 360 380
Frequency
0
50
100
150
200
250
300
E. coli
(a)
The histogram of modularity M for a sample of 1000 genotypes with Venv =89
different minimal environments. The estimate of M for E. coli is also displayed.
We can reject the hypothesis that the modularity index M of E. coli is drawn from
this same distribution.
25. Distribution of s for genotypes in our most constrained
ensemble and comparison with E. coli
The histogram of s for a sample of 1000 genotypes with Venv =89 different minimal
environments. The estimate of s for E. coli is also displayed.
We see that the number of FCSs in E. coli is just slightly above the position of the
distribution's peak in our ensemble. Thus, the atypically high M of the E. coli network
does not stem from its having more modules than typical networks allowing growth
on 89 carbon sources.
s
70 80 90 100 110 120
Frequency
0
50
100
150
200
250
300
350
E. coli
(b)
26. Atypically large FCSs near the output end of the E. coli
metabolic network
Samal et al, BMC Bioinformatics (2006); Private Gallery
Pink metabolites are part of
Biomass reaction
FCSs and operons are
positively associated
in E. coli.
27. Conclusions
• We find that more versatile networks are more modular and also have more
modules than less versatile networks.
• The reactions that form part of newly arising modules in highly versatile
networks tend to be close to reactions that process nutrients.
• E. coli metabolic network is significantly more modular than random networks
in our most constrained ensemble.
• Since versatility corresponds to viability in increasing numbers of
environments, it can be considered as a trait associated with fitness itself.
• Our work supports a scenario for the origin of modularity in which increasing
functional constraints can make modularity emerge.
28. Two scenarios for the evolution of modularity
• Modularity might result from directional selection favouring change in one trait
while stabilizing selection maintains other traits unchanged.
This scenario was shown to be true in model gene regulatory networks.
• Modular fluctuations in evolutionary goals can be sufficient to produce and
maintain modularity. In this scenario, modularity will be lost once there are no
fluctuations in the evolutionary goals.
29. Empirical studies show that generalist prokaryotes are more
modular than specialists
Empirical observations by groups of Uri Alon
(Parter et al, BMC Evol Bio (2009)) and Eytan
Ruppin (Kreimer et al, PNAS (2008)) where
generalists prokaryotes living in many different
environments are more modular than specialists
are fully consistent with our conclusion.
Note that the size of
the networks increases
with environmental
variability in these
studies.
Our samples of random viable networks have not
been subject to unknown selection pressures
unlike metabolism of real organisms.
30. Functional constraints lead to emergence of modularity
• Our results support the scenario proposed can explain the
emergence of modularity in both non-fluctuating and
fluctuating environment scenario.
• The main requirement for emergence is an increase in the
number of functions that a network performs.
The intersection of genotype
spaces for two different
environments contains
modular networks.
Modularity is ultimately a
property of genotype spaces
(or the genotype-phenotype
map).
Uri Alon’s PictureOur Picture
31. Global structural properties of real metabolic networks:
Consequence of simple biochemical and functional constraints
Increasinglevelofconstraints
Biological
function is a
main driver of
structure in
metabolic
networks
• Power law degree distribution
• Small Average Path Length
• High Clustering coefficient
• Bow-tie architecture
32. Acknowledgement
CNRS & Max Planck Society Joint Program in Systems Biology (CNRS MPG
GDRE 513) for a Postdoctoral Fellowship.
Reference
Genotype networks in metabolic reaction spaces
Areejit Samal, João F Matias Rodrigues, Jürgen Jost, Olivier C Martin* and Andreas Wagner*
BMC Systems Biology 2010, 4:30
Environmental versatility promotes modularity in genome-scale metabolic networks
Areejit Samal, Andreas Wagner* and Olivier C Martin*
BMC Systems Biology 2011, 5:135
Funding
Andreas Wagner
University of Zurich
Olivier C. Martin
Université Paris-Sud
Federation of European Biochemical Societies (FEBS) for a Fellowship.