BioSec 2012: Mohammad

From Soma-notes
Jump to navigation Jump to search

Energy and Enzymes

I summarized this chapter earlier:

Catalyst called enzymes speed up the rates of reaction without the need for an increase in temperature.

Phosphatases: A group of enzymes catalyze the removal of phosphates groups from a range of molecules.

Energy: The capacity to do work e.g build a protein from a group of amino acids or pump sucrose across a cell membrane.

First law of thermodynamics: Energy can be transformed from one place to another, but it cannot be created or destroyed.

Second law of thermodynamics: The total disorder (entropy) of a system and its surroundings always increases.

It takes energy to maintain low entropy or the “orderness” of things.

Cells are always breaking down and new ones are always being created by the synthesis of a huge array of proteins, carbohydrates and lipid molecules.

Free energy: The portion of a system’s energy that is available to do work.

Spontaneous reactions:

1- Reactions tend to be spontaneous if the products have less potential energy than the reactants.

2- Reactions tend to be spontaneous when the products are less ordered than the reactants.

3- Change in free energy indicated whether a reaction is spontaneous. The free energy change as the system goes from initial to final states is the sum of the changes in the energy content and entropy.

Point of chemical equilibrium: a state in which reaction does not stop but rather a state in which the rate of the forward reaction equals the rate of the backward reactions.

The change in free energy of life is always negative as organisms constantly take in energy-rich molecules and use them to do work. Organisms reach equilibrium only when they die.

Exergonic reaction: A reaction that releases free energy. Products contain less free energy than the reactants

Endergonic reaction: The products contain more free energy than the reactants.The reactants involved in the reaction need to gain free energy from the surroundings to form products of the reaction. Catabolic pathway: Energy is released by the breakdown of complex molecules to simpler compounds e.g. cellular respiration

Anabolic pathway: Energy is consumed to build complex molecules from more simple ones. e.g. photosynthesis.

Cells supplies energy to drive endergonic reactions using ATP (adenosine triphosphate)

ATP contains free enegry from high energy phosphate bonds.

Removal of one or two of the three phosphate groups is a spontaneous reaction that relieves the repulsion of the negatively charged phosphate groups and releases large amounts of free energy.

The breakdown of ATP is a hydrolysis reaction and results in the formation of ADP (adenosine diphosphate) and a molecule of inorganic phosphate.

Hydrolysis of ATP produces free energy (heat).

How do living cells link the hydrolysis of ATP to an endergonic reaction such that the energy is not wasted as heat?: Energy coupling: An enzyme brings ATP and the reactant molecule into close association.

Energy in carbohydrates, fats and proteins consumed in food is used in the energy requiring endergonic process that combines ADP and Pi.

The continued breakdown and resynthesis of ATP is called the ATP cycle.

A spontaneous reaction does not mean that it proceeds rapidly.

Activation Energy: initial energy required to start a reaction and tranform molecules into transition state where bonds are stable and are ready to be broken.

Enzymes act as a catalyst by lowering the activation energy required by molecules in reactants to start a reaction.

Enzymes speed-up the rate of spontaneous exergonic reactions.

Enzymes do not supply free energy to the reaction.

Three main mechanisms in which an Enzyme lowers activation Energy:

1- Bringing the reacting molecules together

2- Exposing the reactant molecule to altered charge environments that promote catalysis

3- Changing the shape of a substrate molecule

Temperature and pH levels have a significant effect on enzyme activity and thus the reaction rates. The optimum pH level for an enzyme depends on the pH level of its environment. If Temperature becomes too high the enzyme of denatured.

As the enzyme concentration increases the rate of catalysis increases.

Competitive Enzyme inhibition: enzyme inhibitors bind to the enzyme’s active site

Non-competitive inhibitors: enzyme inhibitors bind to a location on the enzyme other than the active site.

Enzyme inhibition is important to the cell since it controls the presence of multiple active enzymes at the same time. (allosteric regulation).

Feedback inhibition provides a mechanism to prevent the wasting of cell’s energy. If the concentration of the inhibitor increases the enzyme action is inhibited, if the concentration of the inhibitor decreases, the enzyme is activated.


Are we really evolving software via trial and error as opposed to properly and specifically designing them?

Is it possible to properly design a piece of software to do exactly what we want it to do and nothing else considering that it will be part of a bigger system?

With all the complex biological systems how can computer scientists acquire all that knowledge?

Do we need to acquire all that knowledge before making a decision whether this is useful in terms of computer system's security or not?

How long or how much effort needs to be spent in such a process?

From the few chapters that we've read, a thought that goes through my mind: Can we completely understand how a biological system works in full? If we ever do, can we fully understand how it interacts with its environment (drugs, foriegn organisms,etc.), can we ever understand the full short or long term implications of a minor change in a biological system?. Even though we consider a software system or a collection of systems as complex structures, relatively speaking compared to a biological system a software systems seems quite small, deterministic and controllable.

How much can computer scientists take from biology?

Can we take sub-systems from biology or do we need to go full out replicating existing biological systems?

There seems to be alot of diversity in biology, yet diverse organims can affect each other for example a human getting sick from a bird through a certain strain of bird flu but most of the times diversity provides protection. Can diversity of computer systems provide security?

Can't wait to see how the immune system works and how its evolution happens "online" or during the organisms life.

Biology tends to build things in a bottom up fashion, a fully connected graphs that get pruned with time. Humans tends to engineer systems differently.

"Attacking" a cell affects many other parts or systems within the organism.

A human attacking a computer system tends to make sure his attack does not affect other parts of the system that may trigger alarms or suspicions. That is possible because components of a computer system unlike a biological system could be completely isolated from each other.

Several communication paths are used to relay messages between cells. Membrane receptors resemble a software interface to the system where embedded receptors resemble exposed APIs.

The amazing complexity of the biological pathways amazes me for example:

Originator --> Specific receptor site-binding molecules --> Receptors --> phosphorylation of protein --> Enzyme kicked into action --> Second messengers activated --> FEEDBACK LOOP TO CONTROL PROCESS

Can we enhance a system's security by obscurity or added complexity?

If we were to design a computer system based on biology/evolution, a self replicating, code generating system; what would we gain? how would that system look like?

Can we create an artificial immune system that is as good as a biological one? Can we consider all computer systems in the world to be part of one parent biological organism with one big distributed artificial immune system (a distributed OS)?


Self preservation is the key driving force behind separating the cell division process into multiple phases as opposed to one big jump from start to end. To be able to secure each step of the way the cycle phases evolved into more complicated processes that are more regulated and well controlled.

This is also the result of organisms or cells distrusting their environment.

If it wasn't for the regulated cell process, every cell will be wanting to divide and divide endlessly if possible, consuming energy and causing lots of other problems.

Obviously cell regulation and control can be greatly related to computer systems security.

Jumping DNA....

Archaea and Eukaryota display evidence of how the process of cell division evolved, they protect there DNA in rigorous, salty, acidic environments (e.g intestine bacteria

Cell organels also divide.

Some cells don’t go through the entire cycle, they halt at G0, for example Nerve cells.

Computer Systems are not regarded as one body like biological systems are. Computer systems need to worry about the system as whole (eg. cloud computing).

The most important structure in cell division is the micro-tubules, they are mainly responsible for attaching to the right chromosomes after lining them up properly and physically pulling them off their centromeres.


How complete is Mendel’s picture:

- He got very clear results.

- How did he get nice clear outcomes from simple rules? : He selected clear samples to work with certain characteristics that he knew would work.

Mendelian vs Non-Mendelian genetics. The binary classification system of things is very biased.

Mendel developed the idea of genetics and the fact that patterns come in effect as a result of something happening.

Genes are code.There is no such thing as Junk DNA.

The Genetic conceptual model has lots of problems.Simplified views of the world are needed to see things clearly.and to serve as stepping stones to science

Can we see Mendelian type stuff in code reuse? There are multiple copies of the same code and then there is a choice of which to run? Backup copies of everything.

Hybrid vigour: the inter-breeding is unhealthy.

Selective breeding is an algorithm to reduce differences.Its trying to turn haploid back into diploid organisms.

High gene copies is found in plants, animals can’t seem to handle more copies too well.

n-version programming, multiple libraries on windows machines are examples.

We often build systems to manifest bugs very quickly. We have the notion of removing bugs and you get a working program, we don’t have the notion of making the program works even if it has bugs!!!

Automating trial and error to evolve code.

Defence programming; not very well practiced.

Software component dependencies are not very well understood. Once we get into larger space, interactions are no longer understandable.

Linux kernel is a perfect example of code evolution. It’s gotten more clean and better architected. It’s been worked on and continuously evolved by programmers who did not ignore what they did not understand.

Code is an extension of the human brain.It’s a communication between thought processes.

Open source code is long term viable, because you provide it a pool of people that maintain its livelihood and its evolution.

If there is no selective pressure to evolve code there is no need for optimization.

Living code.

Understanding what legacy code is. How much resources have been put in it. It’s a statement about the human behind it. Is it a live community or a dead community. You do not throw away your legacy code you evolve it.

Sometimes you need to need to build a community around new code because it’s very hard to get the community to understand old code. (Firefox example dieing off for many years)

Diploid stuff is not how we build code. Diploid stuff is a reguation engine that filters out bad code.


  • What enzymes to produce under what circumstances.
  • The lacOperons determine how things are turned on/off
  • Its a probailistic switch because concentration is not a constant thing
  • It's a dynamic system, it can not be represented in bits.
  • To express this in a computer system, it would be based on a fixed threshold.
  • Inside a cell, they are synthetic switches,
  • This is all computation. A big feedback loop of smaller feedback loops.
  • Methodological bias, you find what you're looking for.
  • Lactose is the backup source for glucose
  • Lacoperons inputs are lactose and glucose
  • Process is based on concetrations
  • Repressor protein binds to glucose if its available
  • Sigmoid function for concetration
  • Living system dont work with respect to absraction, they live with what works
  • Sigmoid function is is like transistors, one amd zeros are a range and we abstract that out
  • The lacoperon is continous unless there is a strong selective bias
  • Cell cycle are sigmoidal as well.
  • Cell mantains state using chemical reactions. Markers and proteins.
  • Cell forgetting is an active process. Homoestasis, a cleaning process.
  • Radiation damage is an unforgettable state
  • Cells habituate.
  • Processors habituate with code (stupid habituation)
  • Cells will change functionality based on habituation.
  • RNA to protein operate at much finer time scales.
  • What is the benefit of programming this way?
  • The gap is state dependance in computation. Path dependance in the state of the system.
  • The pop up dialig box poping up multilple times and doesnt learn
  • How things happen matters in biological systems, in computation we dont care, we dont remember, we just care about end state.


  • Osmoregulatin and Excretion: the mechanisms cell use to exchange water and solutes with the surrounding fluid through osmosis
  • Osmosis: Passive Diffusion: water moves through a selectively permeable membrane from high water concentration to low water concetration.
    • Osmotice concentration or Osmolality
    • Regulating Osmosis through different means: Animals must keep their celluar and extracellular fluids isoosmotic other cells can swell or shrink

Some animals are osmoconformers: their osmotic pressure matches that of the environment.

    • Others who regulate are called Osmoregulator
  • Excretion:
    • To mantain ionic and pH balance and osmotic concentration. Water is a solvent for the waste products (from metabolism of nitrogenous compounds, called metabolic water). Excreted as ammonia, urea or uric acid
    • Mechanisms of excretion in invertibrates. Special tubules, skeleton
    • Mammals: excretory tubules called nephrons are located in the Kidney
    • Description of the structure of mammalian Kidney. (Kidenys, Ureters, Bladder, Urethra)

Nephrons perform excretion in sucessive steps. The function of peritubular capilaries. More than a Million nephrons.

    • Urine is hyperosmotic and that is a water conserving adapation. The interaction between the kidney and the nephrons, conserve nutrients and water, balance salts and concentrate waste for excretion form the body.
    • Detailed description of the stages of excretion in the kidney at different stages and the gradient of molecule concentration in the kidney as well as the differences in permeability of successive regions of the nephron.
  • Thermoregulation:
    • Animal cells function only within the range of 0 to 45 degress C. A little below )0 the lipid bilayer of a biological membrane changes from a fluid to a frozen gel, ice crystals will destroy the cell functions. At 45 degrees C the the kinetic energy of proteins and nucleic acids unfold from their functional form. Both lead to cell death.
    • Based on negative feedback pathways in which thermoreceptors detect changes from a temprature setpoint.

Physiological and behavioural responses are triggered by signals from these sensors.

    • The responses include adjustment of heat generating oxidative reactions within the body coupled with the adjustements in the rate of heat gain or loss at the body surface.
    • The performace of the organims varies greatly within the temprature range. ( sprinting for example of a performance meausre).

Performance includes: moving quickly, digesting food efficiently and carrying out neccesary activities and processes rapidly.

  • Mechanisms: Exchange heat with the environment. conduction, convection, radiation, evaporation.
    • Ectotherms and Endotherms (gain heat from env vs. from their internal physiological sources)
    • Ectothermy and Endothermy are different strategies of coping with the variation in environmentation temprature.
      • Endothermy:
        • 36c to 39c in mammals. Most complicated thermoregulatory process.
  • Hypothalamas. Thermoreceptops such as the ones of the skin (warm/cold receptors) sends signals to the Hypothalamus. Hypothalamus produces signals iteself if the blood temprature varies by 0.01 degc.
    • Is primary thermoregulator
    • Sends signals through the autonomic nervous system, responses are vasocontriction, contraction of smooth muscles that erect hair shafts in mammals.
    • Further responses are triggered in case this doesn’t work: rhythmeic tremors of musle, secretion of epinephrine and thyroid hormone that increases the heat production by stimulation oxidation of fats other fuels (NONSHIVERING THERMOGENSIS). This uncouples electron transport from ATP production in mitochondria, the heat is transfered to the body by blood.
    • If it gets too hot: responses are relax smooth muscles, sweat glands..

mo.core.dump.33: Information Flow

  • much of the information flow is mediated by the nervous system
  • Four major components: reception, transmission, Integration and response
  • Afferent neurons --> interneurons --> Efferent neurons
  • Dentrites/Axons
  • Glial cells support neurons (nutrition)
  • Neuron to neuron communication site: Synapses
  • synapse sits between axiom terminal of a neuron and dentrite of another
  • Direct electrical signal or neurotransmitter
  • animal cells have a membrane potentional, outside the cell is positive and inside the cell is negative
    • Na+/K+ concenration through selectively permeable membrane(active transport pumps remember!)
  • Steady negative membrane potential (resting potential)
  • Potential changes from negative to positive during an action potential
  • Voltage-gated ion channels controls the Na+ and K+ movement
    • Activation gate
    • Inactivation gate
  • Action propagation through out
  • All-or-nothing principal of action potential
  • Saltatory conduction allows action potential to hop rapidly along axons
  • Singal transmission by neurotransmitors
    • Exocytosis
    • Ligan-gated ion channels
    • Neuropeptidess
  • Integartion of incoming signals
    • Integration by summation at chemical synapse
      • EPSP
      • IPSP
  • Central Nervous System
    • Brain Division/ Structure
  • Peripheral Nervous System
    • Lots of details! - Amazing stuff.

mo.core.dump.44: Defenses against disease

  • Three lines of defense against invasion
    • Physical Barriers
      • Epithelium
      • Ciliated cells sweep mucus into throat
      • Many epithelial tissue secrete enzymes lethal to bacteria
    • innate immune system
      • includes inflammation: creates internal conditions that inhibit or kill many pahtogens
      • relies of germline-encoded receptors: recognize a set of highly conserved molecular

patterns that are present on the surface of the pathogens but not found on host cells

      • Immediate non-specific response
    • adaptive immune system
      • acquired immunity
      • specific to individual pathogens
      • triggered by specific molecules on the pathogen
      • body retains memory of the first exposure allowing it to respond much faster the next time.
  • The immune system is the product of a long-term coevolutionary interactions between the pathogens and their hosts
  • How Organisms recognize pathogens (Recognition of non-self)
    • Pathogen associated molecular patterns (PAMPs) are identified using host molecules called pattern recognition receptors (PRRs)
    • One PAMPs are recognized signalling cascades are initiated that activate various components of the innate immune responses
    • May activate the same or different signalling pathways.

  • Nonspecific Defence: Innate immunity
    • Signalls pathways activate processes such as phagocytocis (the internalization and destruction of particulate matter) of small pathogens by hemocytes
    • Melanotic encapsulation
    • Production of antimicrobial peptides that kill pathogens not yet killed.
    • May initiate inflammation
    • May activate the soluble receptors of the complement system
    • Antimicrobial Peptides
      • some are called defensins
      • found in all epithelial surfaces
      • defensins attack the plasma membrane of the pathogens
    • Inflammation:
      • See figure 44.2 page 1089
      • Monocytes, Macrophages, cytokines, neurophiles, chemokines
      • neutrophils die as as result of the harshness of the attack (ezymes(defensins), toxic compounds). They come out in puss.
      • eosinophiles: parasitic worms can’t be engulfed, these guys cluster around it and secrete enzymes to kill it.
    • The compliment system
      • a group of soluble plasma proteins
      • they’re inactive and circulate in blood
      • participate in cascade of reactions on the pathogens surface
      • create pores in bacterial membrane destroying their osmotic pressure
      • Histamine release
  • Defences against virues
    • Viruses can not be identified using their surface patterns
    • RNA Interference
      • Interferes with the virus cells ability to transcribe specific genes using ***RNAi interferes with the thus inhibiting it
      • To combat RNAi, viruses contain genes that encode RNAi supressors to help them survive!
    • Interferon
      • Interferon-Alpha/Beta are produced by cells
      • Autocrine/Paracrine effect
      • Bind to cell-surface receptors, triggering a signal transduction pathway that changes the gene expression patterns of the cell
      • Activiation of ribonuclease enzyme that degrades celluar RNA and inactivates protein synthesis
      • This inhibits replication of the viral genome
      • puts the cell in a weakened state that it can recover from
    • Apoptosis
      • Programmed cell death
      • Pathogens activate apoptotic pathways
    • Natural killer cells
      • type of lymphocyte
      • circulate in the body and kill target host cells
      • Directly through the secretion of granules contained perforin (causes pores in the membrane)
      • Indirectly by secretion of proteases, a protein-degrading enzymes, inducing apoptosis
      • To differentiate between host cells and pathogen, most vertebrate cells contain major histocompatiblity complex (MHC) protein.

  • Adaptive immunity system
    • Time delay, but innate imm. system handles it in the mean time
    • Has memory
    • Antigens are identified by B and T cells
    • Antibody/cell mediated immunity responses
    • Memory cells!
    • Antibody mediated immunity reponse
      • B-cell recptors and T-cell receptors bind to specific pathogen patterns
      • Produce antibodies
        • Large complex proteins
        • 5 classes of antibodies
    • T-Cell activation by dentritic cells
    • B-Cell activation by helper T-Cells
      • B-cells produce and secrete antibodies
    • See figure 44.7 for the whole process

Some thoughts/Defenses

  • Gene expression: executing a process with uid 0
  • Cell boundaries/memory map
  • Stored routines/libraries?
  • What is a priviledged process in the body?
  • There is no sense of non-self. It is only a behavioural differentiation. What in biology self and non-self based on bahaviour?
  • Signal pathways from different subsystems should be tainted. (different types of chemicals might trigger the same thing)
  • Kernel subsystem should be regarded as a separate entity from user land.
  • Cell boundaries - VM Pages? Kernel vs user address space
  • peptides
  • mega-virus

Last Class

  • Biology is the process of evaluation. As a result there is a pattern.
  • Biology and computer evolution is the same in the sense that they’re both subject to the same process.
  • Past thoughts paper: authenticating with our minds
  • Synthetic biology is just as fragile as engineered systems.
  • Trying to fit biology into an engineer’s image of how things should be build is a failure
  • Constant change and turnover is vital for survival. Computers are sitting ducks