Skip to main content

The Connections Between the Sciences


I apologize for taking so long with this entry of my blog. I have been abnormally busy lately with my academics and poetry.
Today, I am writing on how all of the sciences are related to one another, in the hopes that you will come to realize that the sciences are not as separate as popular culture and news has us believe.


This blog will be geared to those individuals – weather you're the average person or a student of science, or a full blown scientist – who have the opinion that the different fields of science are completely isolated from one another. This sentiment is not true, and I hope to show the false-hood of this concept here.
In physics, we have the concept of “The Right-Hand-Rule”. This pretty much determines whether the a force perpendicular to two vectors is “positive” or “negative”. Torque is a good example of this. The amount of torque placed on, say, a bolt by a crescent wrench is perpendicular to the position vector and the force vector; in other words, how much force you're applying to the wrench clockwise or counterclockwise, and where on the wrench you're applying it. When the torque you apply to the bolt is negative, you are loosening the bolt, while a positive torque yields a tightening the bolt.
Biochemistry has a similar concept, though the application is different. To a biochemist, the right-hand-rule yields a “handedness” to a spiral molecule. The most common use is with proteins, which the molecule is oriented in a particular fashion and what the physicist calls a positive with the torque, the biochemist calls a “right-handed” molecule, because the spiral twists upwards in a right-handed sense. That is to say, when you curl your fingers of the right hand in the direction of the spiral, the thumb is pointing up. When the spiral is in what the physicist would call negative, the biochemist calls a left-handed helix.
In proteins and chains, there are proteins and chains, there is directionality involved, which is a concept straight from vectorial mathematics. When there are two parts of a protein chain very close to each other, there are molecular forces which hold them together. How these forces hold these parts of a chain together depends on the “directionality” of the chain segments. The directionality refers to whether they both are carbon to nitrogen or nitrogen to carbon, or one is carbon to nitrogen while the other is nitrogen to carbon. This is equivalent to two people laying in the same bed where both of them have their head on the same side of the bed versus where they are laying head to foot. The prior is referred to as “parallel” and the latter is referred to as “anti-parallel”. The prefix anti- means they are parallel to one another, but going on opposite directions, like the two sides of the same street.
In biochemistry, the affect of proteins, DNA, RNA, etc. inside the body attract each other electrically. This attraction comes straight from the physics concept of electromagnetism. Here, I will give a couple equations to show a point. In physics, the energy of a system of two objects with wildly different charges is given by E=(k*q)/(r), where E is energy, k is the electrical constant, q is the charge of the smaller charge, and r is the distance between the two charges. In biochemistry, since the two charges are much closer to equal than in physics – and since we have to look at the solvent in the system – we use a modified version of this equation, E=(k*q1*q2)/(D*r),
where each q is the charge of each respective molecular group and D is the Dielectric constant of the solvent. The dielectric constant is basically the electrical conductivity of the solvent.
Notice that these two equations are quite similar. The fact that the physics equation leaves out an (r/D),
tells us that this quantity is effectively one. As we all know, when we multiply anything by 1, we get out that same anything we put in. (The wordiness of the previous sentence is exactly why we put letters in mathematics. I will write a blog on that concept sometime in the future.)


This is just the tip of the iceberg for the interconnectivity of the sciences. My prompt for you is to find more ways any two or three or any number of sciences is interconnected. This holds especially true for those of you going to college right now. The more you yourself look for and find the interconnectivity of the sciences, the better you will learn the material of your primary degree program. You will also see that the credit requirements for your degree programs are less arbitrary than you realize.

I hope you have learned something today. Until next time, don't forget to be awesome.
-K. “Alan” Eister Δαβ

Comments

Popular posts from this blog

Basic Statistics Lecture #3: Normal, Binomial, and Poisson Distributions

As I have mentioned last time , the uniform continuous distribution is not the only form of continuous distribution in statistics.  As promised, here are the three most common continuous distribution types.  As a side note, all sampling distributions are relative to the algebraic mean. Normal Distribution: I think most people are familiar with the concept of a normal distribution.  If you've ever seen a bell curve, you've seen the normal distribution.  If you've begun from the first lecture of this lecture series, you've also seen the normal distribution. This type of distribution is where the data points follow a continuous curve, is non-uniform, has a mean (algebraic average) equal to the median (the exact middle value), falls from highest probability at the mean to (for all practical purposes) zero as the x-values approach $\pm \infty$, and therefor has equal number of data points to the left and to the right of the mean, and has the domain of $(\pm \i

Confidence Interval: Basic Statistics Lecture Series Lecture #11

You'll remember last time , I covered hypothesis testing of proportions and the time before that , hypothesis testing of a sample with a mean and standard deviation.  This time, I'll cover the concept of confidence intervals. Confidence intervals are of the form μ 1-α ∈ (a, b) 1-α , where a and b are two numbers such that a<b, α is the significance level as covered in hypothesis testing, and μ is the actual population mean (not the sample mean). This is a the statement of there being a [(1-α)*100]% probability that the true population mean will be somewhere between a and b.  The obvious question is "How do we find a and b?".  Here, I will describe the process. Step 1. Find the Fundamental Statistics The first thing we need to find the fundamental statistics , the mean, standard deviation, and the sample size.  The sample mean is typically referred to as the point estimate by most statistics text books.  This is because the point estimate of the populati

Basic Statistics Lecture #5: Baye's Theorem

As promised last time , I am going to cover Baye's Theorem. If Tree diagram is the common name for Bayes Theorem.  Recall that conditional probability is given by $P(A \mid B) = \frac{P(A \wedge B)}{P(B)}$.   For tree diagrams, let's say that we have events A, B 1 , B 2 , B 3 , … (the reason we have multiple B's is because they all are within the same family of events) such that the events in the family of B are mutually exclusive and the sum of the probabilities of the events in the family of B are equal to 1. Then we have $$P(B_i \mid A)= \frac{P(B_i)*P(A \mid B_i)}{\sum_{m=1}^{n}[P(B_m)*P(A \mid B_m)]}$$  What this means is reliant on the tree diagram. If we are only looking at the sub-items of A, this is what the tree diagram would look like. If J has a probability of 100%, and P(C) and P(D) are not 0, then when we are trying to find the probability of any of the B's being true given that A is true, we have to set the probability of A to be the entir