Beauty tip: Apply the moisturizer along Langer’s lines on your face.

This sequence of thoughts was triggered by an observation by Prof. Peter Lee during a biomechanics class.

Many students of biology or anatomy would know that our skin’s dermis (inner layer) is a connective tissue whose largest constituent is collagen. Collagen is a type of extra-cellular protein that looks like three ropes wound around each other at microscopic level. Two interesting facts about skin and collagen are in order: 1) Skin is the largest connective tissue and the largest organ of our body. 2) Collagen is the second most (after meat) animal produce consumed by human beings (if you exclude milk). All of the gelatin used in in food, pharmaceutical, photography, and cosmetic industry comes from animal connective tissues.

During the class, we came to know that the strings of collagen permanently orient themselves along specific directions. This orientation of collagen produces cleavage lines on the surface of skin, and that is what gives rise to all lines seen on our skin. These lines are the cause of wrinkles at elder age. In fact, only few parts of our body do not have clearly defined cleavages and our skin is *stretched* all the time along these lines.

These lines (sort of field lines of a very complex electric or magnetic field) were first mapped out by an Austrian anatomist Carl Langer (1819-1887). He systematically made fine cuts on the cadaver skin and observed in which direction the cuts elongate. The direction of elongation reveals the direction in which the skin is stretched and hence the orientation of underlying collagen. These lines of tension on skin are named after him as Langer’s lines.

Here is what these lines look like..

If you experience a cut along the Langer’s lines, the scar will not be very visible and the wound will heal fast. But if the cut is perpendicular to the Langer’s lines, the wound will tend to open up and the scar will be more visible. Surgeon’s (especially those involved in plastic surgery and beauty services) must respect these lines when making incisions on patient’s skin.

And perhaps these lines are the reason (as Prof. Lee remarked) wives ask their husbands to rub moisturizer on their face not randomly but along certain directions

Any comments from ladies? Do you think the lines in the image above are preferred directions for rubbing creams?

Is it fine to use automated tools to develop software for life-support machine?

The software design technology is assumed to have reached extremely mature state.
Very complex software design tasks such as developing a flight-controller for autonomous scramjet roaring at mach-10 seem to be achievable automatically(Have a look at this story on MATLAB website.)

In such scenario, we assume that software tools available to us are perfect enough to be employed in critical applications such as design of software for life-support machine.
No doubt there are unsolved problems (especially posed by artificial intelligence and information extraction), but we all assume that absolutely essential algorithms (like sorting, searching, reading or writing a file etc. ) used in any program have been perfected.

If you think so, you should look at this post written by Joshua Bloch (one of the developers of JDK.)

Joshua points out how a simple code for finding out a mid-index of an array turns out to be buggy after 20 years of it being written first.

This post clearly points to the need for any software developer to be humble about the quality of his algorithm. The bug-free algorithm works all the time and in all situations. We must admit that we are unlikely to forsee all the situations to which our algorithms may be subjected, so we can never quite get a bug-free algorithm – no matter how simple a thing it does. So we must be very vigilant about bugs – especially in basic algorithms because their cumulative effect is pervasive and huge.

Again, is it fine to use automated tools to develop software for life-support machine?

My micro-eureka moment of today.

Well, make it nano (or pico (or femto)) if you want. I have not discovered a new principle as Archimedes did, but have come to realize a mathematical beauty that exists in the nature’s design.

I have realized that genetic code is a many-to-one function that transforms a sequence of nucleotide into sequence of amino acids. Because this function is many-to-one, it can not be inverted. In terms of genetics it means that – if you know the sequence of nucleic acids in a particular gene, you can always say what protein would it produce; but it is not possible to know the gene that has produced a particular protein (which is a sequence of amino acids).

Sounds foreign? Let me explain.

The right question to ask is: How does a gene control protein manufacture?

Genes are particular sequence of nucleic acids (A,T,G,C) with defined pattern. Every triplet (termed codon) in this sequence encodes an amino acids. Proteins are nothing but a peculiar series of amino acids. When a gene is activated, its sequence is copied to mRNA (messenger RNA). Messenger RNA then carries this genetic information to the place where codons are read and amino acids encoded by them strung togeather. When amino acids are strung togeather, they assume particular chemical and structural properties that in turn govern their functional properties. Proteins are work-horses of cells – they do most of the work.
The code that matches genetic codon with a particular amino acid is uniform, degenerate and unambiguous. What this means is:

• Unifromity: All organisms use the (almost) the same genetic code. (Isn’t it amazing – it really shows we really have grown out of amoeba!)
• Degeneracy: More than one codon can represent any given amino acids (by the way, in humans there are 20 of them)
• Unambiguity: Every codon represents one and only one amino acid.

So if you consider genetic code to be a function (call it ‘g’) maping a set of codons (call it ‘C’) to a set of amino acids (call it ‘A’), we can see that g: C -> A maps every member of the set C to a single but non-unique member of A. Thus, function g is many to one.

Now, it is a mathematical truth that many-to-one function cannot be inverted. Hence, you can never ever get a nucleotide sequence out of protein structure.(edited thanks to Johan’s comment) Hence, eventhough you can infer what nucleic acid sequences may give rise to certain amino acid sequence, you can never be sure which one actually did.

I am Wired!

These days, I am participating in an amazing research carried out by Psychology department of NUS.
They wish to relate the responses given to stress (physical and psychological) by certain populations with their genetic makeup. I am all instrumented these days. I had started writing out the theme, but just towards the end; I realized that I may be violating non-disclosure terms by divulging their method of research.
So for time being, I have just uploaded a pic of me with wires all around.

What is systems biology?

I thought I should write short intro to systems biology, mainly because that will help me clarify my thoughts.

So here it is:

What is systems biology?

Systems biology tries to study and model biological phenomena using traditional engineering, physical, chemical and mathematical principles. It is a very new field in its infancy and some say that it came into being after the complete human genome sequence was discovered.
The aim of the systems biology is two fold:

1. To discover and study interactions between constituents of a living cell, tissue, organ, respiratory-circulatory-nervous etc. systems.
Why? Because human body is not just a collection of cells, organs and some systems. The *interactions* that these constituents of body have among each other are as important as the individual function of consituent itself. These interactions give rise to biological phenomena that cannot be imagined when studying individual parts. Traditional biology has always tried to see such parts individually (mainly because of the complexity of studying the whole thing at a time and lack of knowledge about complex dependencies between parts). But there are certain properties called *Emergent properties* that are observable only when the entire system is studied.

[Aside: What are emergent properties? Assume that u r given three mundane things: a tungston wire coiled up as filament, a bottle-like small glass casing and a metal cap. If you study them individually, you can't figure out any use of them. But taken togeather, you know you can generate light using these things. Now, u cant generate light using these components separately. *Light is generated due to interaction between these components as much as due to components individual behaviour* The ability of the system called light bulb to generate light is called emergent property as it cannot be ascribed to any single component]

To understand the emergent properties of a complex system (and its subsystems) called human body, systems biology aims to discover unknown interactions and consequent exhibitable effects. Some people say systems biology is *new biology* and will forever change the face of medical discipline.

2. To model these interactions using mathematical and engineering tools and use this model to predict things such as: effect of drug on certain disease (this was earlier done by intelligent guess works and animal/human trials), a persons health after say 5 years, the exact cause of someones illness and so on…

To achieve above to aims systems biology requires inter-disciplinary work between biologists, physicists, chemical engineers, mathematicians and computer scientists. It seems that systems biology will force all of these disciplines to push the knowledge envelope. It will demand new algorithms and computing facilities from mathematicians and computer scientists. It will demand novel data acquisition and measurement methods from physicists, chemists and biologists.

Typical tasks in the systems biology are:

1. Observing these phenomena using tools such as spectroscopy, MRI, ultrasound and so on…
2. Denoising acquired data.
3. Modelling human biological phenomena (starting from DNA replication to creation of protein to creation of other parts of living cell to their exibitable effects)
4. Predicting clinically important results.
5. Validating predicted results.

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It is really exciting to see the merger of rather different fields of engineering and medicine in this way.

I hope it was not very round-about and boring.