All posts by gp

Future Planetary Exploration

News and discussion of planetary probe proposals and funding. Lots of good international resources. Recent focus is the JET (Journey to Enceladus and Titan) initiative. The author offers some insightful opinion on mission selection and the true cost of outer planet exploration. Opinion polls are also hosted around the planetary science community’s current Decadal Survey.



goal:  to investigate Quantum Monte Carlo
            as a method for Quantum Chemistry

Quantum Chemistry (QC) attempts to predict the reactivity behaviour of atoms and molecules using quantum mechanics. It is also fundamental to molecular physics and molecular biology. QC offers a powerful theoretical tool, especially to the life sciences.

The Schrödinger Equation describes the quantum state of a physical system evolving in time. Solutions of such equations can be used to calculate energy levels, structure, and bonding properties of atoms and molecules. This usually involves tactical approximations and a good deal of calculus.

To take advantage of distributed computing power, the QMC@home project is applying the Quantum Monte Carlo approach. In Monte Carlo analysis, the problem at hand is mapped onto a kind of dartboard. The ‘darts’ are then randomly generated (hence the name Monte Carlo). Many darts (perhaps millions) are thrown onto the map to estimate probabilities. The results are analyzed to provide solutions.

A simple example of Monte Carlo analysis is the calculation of π.
If a circle (radius=r) is placed exactly inside a square (side=2r), the ratio of the area of the circle to that of the square is π/4. Random ‘darts’ thrown at this ‘map’ can be tallied to estimate the same ratio. This is called a Monte Carlo (MC) simulation.
Therefore, π = 4 x (MC ratio). More darts on the MC map will generally lead to a more accurate estimate of π.

QMC is new, and its applicability to real chemistry requires further study. This study requires massive amounts of parallel computing power. This is where the public can help. To join the effort, you need only install a ‘client’ on your PC. This client is a small piece of software that usually runs in the background. Participation in this effort by the general public is free, easy to do, and strongly encouraged.


goal:  to understand protein function and
            related molecular biology

There is a tremendous amount of processing power distributed throughout the world in home computers that have a modern Graphic Processing Unit (GPU) card. These cards typically have dozens to hundreds of separate processors, which can be used to work on highly parallel tasks. Although intended for games and graphic applications, they are also now being successfully utilized as an aid to scientific research, computational biology in this case. is a project that conducts molecular simulations on such a distributed ‘grid’. Anyone with a modern nVIDIA GPU can participate (see the currently supported cards list on the GPUgrid website).

HIV Protease is a viral enzyme that helps in the assembly of HIV virions. Understanding its structure and function is computationally time consuming but crucial in the development of inhibitors to target this protease.

Brain function studies are being aided by the simulation of the molecular dynamics of the D2 Dopamine receptor and associated mobility of sodium ions.

Protein-protein binding affinities are being calculated using the Sarcoma Homology 2 protein domain. This is important in understanding diseases where cell growth and development are impaired.

Various other molecular simulations are underway or planned.

To join the effort, you need only install a ‘client’ on your PC. This client is a small piece of software that usually runs in the background. Participation in this effort by the general public is free, easy to do, and strongly encouraged. Their website has pictures, animations, documentation, etc.


goal: to understand protein folding, misfolding, and related diseases

Proteins are the machinery of life. They are responsible for the structure and processes we see in living organisms. They are continually produced within cells, different cell types making different sets of proteins. The genetic code (DNA) stores gene patterns as sequences of base pairs. Genes are expressed (proteins created) when RNA carries these patterns to ribosomes which then read the sequence of bases three at a time. These triplets (codons) are translated into amino acids to assemble a protein. Each long one-dimensional molecule folds into a 3-D shape under local molecular and atomic chemistry rules. This 3-D shape actually determines much of the functionality of a protein. Rarely, this folding process goes wrong (alternate 3-D configurations occur), causing disorders in the organism such as Alzheimer’s, Parkinson’s disease, and many cancer-related syndromes.

Understanding this protein folding process benefits from massive computing power. Folding@home was established to draw upon the considerable resources of distributed computers worldwide. The ‘client’ is a small piece of software that usually runs in the background on your computer. Participation in this effort by the general public is free, easy to do, and strongly encouraged.

Cognition and Computation

Colin Blakemore FRS
Cognition and Computation
The Royal Society
December 2010

found at

An overview of the current state of neurology, looking at the wealth of new information being gathered via genomics and brain imaging. Anthropology, zoology, and functional genetics are added to the mix. Notes the important yet perplexing inclusion of subjectivity, as suggested by Francis Crick near the end of his life.


Parsimony means efficient simplicity. In science and philosophy, it often guides the quest for the shortest, cleanest path or explanation. This word comes from the Latin verb parcere – to spare. It is usually a safe bet that most series of events proceed under the rule of ‘common sense’ where waste of energy and/or time is minimized. Understanding and theorizing about such processes can benefit from the heuristic of parsimony.

One example of parsimony-guided analysis is found in an area of evolutionary biology called phylogenetics. Here, closeness of relationships is determined by counting the number of evolutionary changes between taxa (groups of organisms). For example, DNA base pair differences can be enumerated to hypothesize a most likely ancestral tree with speciation, hybridization, and extinction defining the branches. Even for just a few taxa, many different phylogenetic trees are possible. Applying parsimony, the most likely is the one that requires the fewest genetic changes. This scheme offers the hope that an entire systematic taxonomy (tree of life) can be compiled.

Parsimony is a valid heuristic in comparing phylogenetic trees because each tree results from the same theory (evolution) in general, and ancestry in particular. However, parsimony must not be applied when comparing competing theories. This can give misleading, and sometimes completely wrong, results. The only basis for comparing theories is each one’s ability to explain current observations and make correct predictions, not their elegance or parsimony. Parsimony cannot be used as a logical principle. For example, it is a fallacy to use parsimony to argue against what is in fact a fundamental requirement for life: complexity.

Ethics of Synthetic Life

Mildred K. Cho and David A. Relman

Synthetic “Life,” Ethics, National
Security, and Public Discourse

SCIENCE Vol 329 2 July 2010

found at

Review of implications of synthetic life. Interesting take on how biology is no longer the realm of only biologists in this “rapidly changing landscape”.