This document discusses chemical kinetics and enzyme kinetics. It defines enzymes as biological catalysts that speed up chemical reactions without being used up in the process. The document describes how enzymes lower the activation energy of reactions and explains the mechanisms of enzyme action, including the lock and key and induced fit hypotheses. It also discusses Michaelis-Menten kinetics and how various factors like temperature, pH, cofactors, and inhibitors can affect the rate of enzyme activity. Allosteric enzymes are defined as having additional binding sites for effector molecules.
This ppt includes overall idea of what is enzymes, how it works, mechanism of enzymes, kinetics and how to inhibit enzyme activities. The reference is the ideal book for biochemistry - Lehninger . Understanding is easy for everyone.
Enzymes are proteins that act as biological catalysts by accelerating chemical reactions. The molecules upon which enzymes may act are called substrates, and the enzyme converts the substrates into different molecules known as products.
This ppt includes overall idea of what is enzymes, how it works, mechanism of enzymes, kinetics and how to inhibit enzyme activities. The reference is the ideal book for biochemistry - Lehninger . Understanding is easy for everyone.
Enzymes are proteins that act as biological catalysts by accelerating chemical reactions. The molecules upon which enzymes may act are called substrates, and the enzyme converts the substrates into different molecules known as products.
• Enzymes are catalysts or chemical reagents that speed up chemical reactions without being consumed.
• Most enzymes are proteins that function to reduce energy of activation in chemical reactions.
• Because most enzymes are proteins, their activity is affected by factors that disrupt protein structure, as well as by factors that affect catalysts in general.
Enzymes mechanism of action, their specificity types, active center structure and action, inhibitor types, fisher and Koshlend theory are presented. Enzymes classification, a new class of enzymes discovered recently, detailed explanation of each class reaction types is presented as well
• Enzymes are catalysts or chemical reagents that speed up chemical reactions without being consumed.
• Most enzymes are proteins that function to reduce energy of activation in chemical reactions.
• Because most enzymes are proteins, their activity is affected by factors that disrupt protein structure, as well as by factors that affect catalysts in general.
Enzymes mechanism of action, their specificity types, active center structure and action, inhibitor types, fisher and Koshlend theory are presented. Enzymes classification, a new class of enzymes discovered recently, detailed explanation of each class reaction types is presented as well
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
This pdf is about the Schizophrenia.
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Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
CHEMICAL KINETICS AND ENZYMES B23350B,B223081B,B224645B.pptx
1. CHEMICAL KINETICS
AND ENZYMES
o Venus M Mafusire B223081B
o Michelle Mazwieguta B224068B
o Shingai P Mushapaidze B223350B
o Tatenda Mikili B224645B
2. Chemical kinetics deals with rates of reactions and how
changes in concentration of components affect the rate of
chemical reactions
Enzyme kinetics is the study of the chemical reactions
that are catalysed by enzymes.
Enzymes are biological catalysts
A catalyst is a substance that speeds up a chemical
reactions and remains unchanged at the end 0f the
reaction.
3. Types of Catalysts
Catalysts provide a means of reducing the amount of energy
required to activate a reaction by providing an alternative
route/mechanism.
Homogenous catalyst refers to reactions in which the
catalyst is in the same state as the the reactants,principally
in solution.
Heterogenous catalyst refers to a reaction where the catalyst
and the substrates are in distinct states,typically solid-gas
respectively.
4. How enzymes lower the Activation Energy
Catalysts lower the activation
energy of the transition state
by making it a rare and un-
stable intermediate.
Transition state is the
intermediate state of the
reaction when the molecule is
neither a substrate or product
5. Mechanism of Enzyme Action
Enzymes have two types of mechanisms being Lock and Key
hypothesis and induced fit hypothesis
Lock and key hypothesis was proposed by Fischer and states that
“If the right key fits in the right lock,the lock can be opened,
otherwise not ,” by this he made it clear that enzymes are specific.
6.
7. Induced fit hypothesis
The induced-fit model was proposed by Koshland ,the model
suggests that an enzyme when binding with its
substrate,optimizes the interface through physical
interactions to form the final complex structure (enzyme-
substrate complex).
Both the enzyme and substrate change shape slightly ,creating an
ideal fit for catalysis.
8.
9. Michaelis-Menten Kinetics
Michaelis-Menten model of enzyme kinetics explains how the rate of
an enzyme catalysed reaction depends on the concentration of the
enzyme and its substrate .
Lets consider a rection in which a substrate(S) binds reversibly to an
enzyme (E) to form an enzyme-substrate complex (ES),which then
reacts irreversibly to form a product (P) and release the enzyme again.
S + E ES P + E
Two important terms in Michaelis-Menten kinetics are :
• Vmax
• Km(Michaelis constant)
10. Vmax – the maximum rate of reaction, when all enzymes active
sites are saturated with substrate.
Km – the substrate concentration at which the reaction rate is
50% of the Vmax .Km is a measure of the affinity an enzyme has
for its substrate , as the lower the value Km , the more efficient the
enzyme is carrying out its function at a lower substrate
concentration.
The Michaelis-Menten equation for the reaction above is :
This equation describes how the initial rate of reaction (V) is
affected by the initial substrate concentration ([S]). It assumes
that the reaction is in the steady state, where the ES
concentration remains constant
11.
12.
13. Factors that affect the rate of enzyme
activity
Temperature
Effect of pH
Effect of activators(Co-factors)
Concentration of enzymes
Concentration of substrate
Enzyme inhibitors
14. Temperature
Enzymes operate fastest at an Optimum Temperature which is
about 40 degrees celcius.
At very low temperatures enzymes become inactive and the rateof
reaction is low or nearly zero.
As temperature increases the molecules gain kinetic energy so
the rate of collisions increases only upto the optimum
temperature.Temperatures above the Optimum temperature
results in the denaturing of enzymes (the vibration of molecules at
high temperatures causes the bonds that maintain the enzymes
3D structure to be brocken and the protein unfolds therefore the
active site is lost).
15.
16. Effect of pH
Enzymes function at an optimum pH which is about 7 to 8
however some may have an optimum just below for example
salivary amylase has an optimum pH of 6.8.
When pH is above the optimum it affects the charge of the amino
acids at the active site, so the properties of the active site change
and the substrate can no longer bind (enzme is denatured).
17.
18. Effect of activators(Co-factors)
Co-factors are non-protein molecules required to activate
enzymes .
Cofactors can be either inorganic (e.g., metal ions and iron-sulfur
clusters) or organic compounds (e.g., flavin and heme).
Organic cofactors can be either prosthetic groups, which are
bound to an enzyme, or coenzymes, which are released from the
enzyme's active site during the reaction e.g NADH, NADPH and
adenosine triphosphate.
Coenzymes are small organic molecules that can be loosely or
tightly bound to an enzyme.
Tightly bound coenzymes can be called allosteric groups.
Coenzymes transport chemical groups from one enzyme to
another.
19. Some of these chemicals such as riboflavin, thiamine and folic
acid are vitamins (compounds that cannot be synthesized by the
body and must be acquired from the diet).
The chemical groups carried include the hydride ion (H-) carried
by NAD or NADP+, the phosphate group carried by adenosine
triphosphate, the acetyl group carried by coenzyme A, formyl,
methenyl or methyl groups carried by folic acid and the methyl
group carried by S-adenosylmethionine.
20. Concentration of enzymes
With increase in enzyme
concentration there is
increase in the rate of
reaction as there are
more active sites.
21. Concentration of substrate
As the substrate concentration increases there is increase in the
rate of reaction as collision between the substrate and enzyme
active sites increase,however when there is a high concentration
of substrate the enzyme active sites become concentrated and
the rate of reaction remains constant
22.
23. Enzyme inhibitors
Enzyme inhibitors are compounds which modify the catalytic
properties of the enzyme and therefore slow down the reaction
rate
There are two types of inhibition being competitive and non-
competitive inhibition.
Competitive inhibitors have a shape which is complimentary to
that of the substrate and hence fits into the enzymes active site
and usually competitive inhibition is reversible.
Non-competitive inhibitors bind to another part of the enzyme
molecule, changing the shape of the whole enzyme, including the
active site, so that it can no longer bind substrate molecules.
25. Allosteric Enzymes
Enzymes that have an additional binding site for effector
molecules other than the active site.
26. An example of an allosteric inhibitor is ATP in cellular respiration ,
this metabolic process operates in a feedback loop .The high
ratio of ATP to ADP will inhibit Phosphofructokinase(PFK) and
glycolysis