Ayyuce Demirbas

“Auschwitz not only kills innocents; it kills innocence as well.”
― Antonio Iturbe, The Librarian of Auschwitz

“Everything is made of invisible particles.
The elementary particles of matter—“the seeds of the things”—are eternal.
The elementary particles are infinite in number but limited in shape and size.
All particles are in motion in an infinite void.
The universe has no creator or designer.
Everything comes into being as a result of a swerve.
The swerve is the source of free will.
Nature ceaselessly experiments.
The universe was not created for or about humans.
Humans are not unique.
Human society began not in a Golden Age of tranquility and plenty, but in a primitive battle for survival.
The soul dies.
There is no afterlife.
Death is nothing to us.
All organized religions are superstitious delusions.
Religions are invariably cruel.
There are no angels, demons, or ghosts.
The highest goal of human life is the enhancement of pleasure and the reduction of pain.
The greatest obstacle to pleasure is not pain; it is delusion.
Understanding the nature of things generates deep wonder.”
― Stephen Greenblatt, The Swerve: How the World Became Modern

“There are two main approaches to predicting protein structures. The first is called homology modeling. Protein sequences and structures are the product of billions of years of evolution. If two proteins are near relatives (the technical term is “homologs”) that only recently diverged from each other, they probably have similar structures. To predict a protein’s structure by homology modeling, you first look for a homolog whose structure is already known, then try to adjust it based on differences between the sequences of the two proteins. Homology modeling works reasonably well for determining the overall shape of a protein, but it often gets details wrong. And of course, it requires that you already know the structure of a homologous protein. The other main approach is physical modeling. Using knowledge of the laws of physics, you try to explore many different conformations the protein might take on and predict which one will be most stable. This method requires enormous amounts of computing time. Until about a decade ago, it simply was impossible. Even today it is only practical for small, fast-folding proteins. Furthermore, it requires physical approximations to speed up the calculation, and those reduce the accuracy of the result. Physical modeling will often predict the right structure, but not always.”
― Bharath Ramsundar, Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

“We know that at least some terrestrial microbes can survive on Mars. What is necessary is a program of artificial selection and genetic engineering of dark plants—perhaps lichens—that could survive the much more severe Martian environment. If such plants could be bred, we might imagine them being seeded on the vast expanse of the Martian polar ice caps, taking root, spreading, blackening the ice caps, absorbing sunlight, heating the ice, and releasing the ancient Martian atmosphere from its long captivity.”
― Carl Sagan, Cosmos