The worse thing that can
happen to someone is liking someone and fail to accomplish what your hopes take
you to. A broken heart is quite difficult, specially if it has been happening
for quite a while. What can be a quite pain is the fact that you are so down
that you talk to someone, and certainly that person is not going to loss the
opportunity to tell you what to do. They advise how you should have handled the
problem, e.g. you should not tell her you feelings, you should not go so fast,
you should have waited, you shouldn't have waited. What piece me off the most
is that the comments are vague, personal, and limited; as vague as prediction
from people that read cards. Some would take them as granted, once when you
fail, you fail. At least in my reality, a girl's no is no, there is no coming
back. So what lasts to us is either cry until no tear is left or take an active
position, making science of it! Einstein once said that theory is when you know
all, but nothing works; experiment is when all works, but no one understand a
thing. Herein we are in the theory domain.
Systems biology is a set
of ideas and methodologies mainly developed in the last decades, however the
core is as old as the Greek miracle. The ideia is quite simple: imagine you
have a system you want to understand scientifically, make laws, design
experiments, make predictions, and so on, all the goods a scientific wisdom can
provide you. However, it soon comes to you that by studying parts by parts, you
always miss something. What is light stopped like? asked Einstein; the answer
is nothing, light just exist at the speed of light. What is the blur of a stone
tightened to a piece of rope like? Nothing, it must be in circle, with a
certain speed to make a blur. Of course, all the examples are from physics, and
they are simple systems, single entities. What about, the beating of your heart
using just one protein? nothing, it must have several proteins interacting to
make the periodic rhythm that keeps you alive. Thus, systems biology is a
paradigm for studying systems that cannot be understood when they are singled
out. Examples are many such as cancer, caused by several or a small number of
genes, network motifs that creates important subsystems such as filters and so
on.
Are you still following me?
where is the girl's stuff? good. We said that someone has broken you heart, you
are depressed, and you make the mistake to talk to someone. That person will
try to ease your pain by telling what you should have done. So far so good. The
first mistake is that from a systems biology perspective, a "yes" or
"no", a broken heart from a nice meeting is an interaction thing;
therefore, your wizard's advise might not work for you. Picture each person as
a node in a graph, if you are not familiar with these concept, you can find
them in the internet, they are not complicate. Picture the yes or no as a
connection, called edge. See that several things cannot be controlled, such as
the place, the people around you and so on; of course, I suppose, you are not
going to try something in the coffee break of your mathematics meeting, I would
not. Call all these details, stochastic. They are perturbations that you have
no control, but may influence your performance. So the ones that you can
control is you and the other person, not her response, but who to talk to, not
completely, but somehow. This means that if you keep all the same and replace
one node, such as you or the person you decided to give a shot, you might have
an yes or not, opposite to the previous result; of course, life is not so
simple, playing with the feelings of the others can be wrong, catastrophic,
death sentence. So, any law that someone gives you is for the edge, not the
node.
Of course it would be
easier if we could make experiments. Further, we need an almost equal amount of
yes and nos, but most people is going to tend for one of them. For instance, in
my case, I have a small success rate, then I cannot say what is stochastic and
what is merit of mine, cannot say what is edge and what is node. If you fail,
does not necessarily mean that doing the opposite you are going to get the
opposite result; even if so, the opposite is not black and right.
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