Simulations Plus

I recently had the occasion to visit the Monet exhibition at the Grand Palais in Paris. Several things about this exhibit blew me away.

A new graph library that provides the right graphs at the right time and with the right information has been developed by Cognigen Corporation, a leading provider of pharmacometric analysis and support services. Comprehensive graphical exploratory data analysis is essential to building pharmacometric models of drug behavior. Previously, deciding which graphs were required to describe the data and then creating a new program for each graph consumed excessive time from both scientists and programmers.

I have long been addicted to The New York Times Book Review. It is not that I am a writer groupie as much as I find it fascinating to read about how a book came about.

Few among us would think highly of a leader who directed us to innovate on demand. After all, innovation is something that comes from a mysterious creative force that strikes like lightning to the fortunate inventor, bringing with it fame and fortune. Think Bill Hewlett and Dave Packard; Steve Jobs and Steve Wozniak; Bill Gates; Mark Zuckerberg; or Tim Berners-Lee (huh?) [1].

Although some meteorological modelers may disagree, it seems to me that models predicting development and movement of hurricanes are getting more and more accurate.

Scientists in the field of pharmaceutical research and development face a most daunting challenge. Our understanding of the diseases we work to treat grows more complex and perplexing with each new published study. Take, for example, the gusher of information coming out on Alzheimer’s disease. How can research findings from genetics, neurology, nutrition, protein chemistry, pharmacology, and epidemiology (just to name a few) be tracked, sorted, and used?

Seems like every time we learn something new, whether it is in biology or cosmology, we learn something else that just makes us sit back and say Wow. So it is with a recent story in The Economist about new information regarding one of the universal “constants” alpha.

G.H. Hardy* said, “A mathematician, like a painter or a poet, is a maker of patterns. And just as in poetry and painting, the mathematician’s patterns must be beautiful. Beauty is the first test,” he said. “There is no permanent place in the world for ugly mathematics.”

Jackson Pollock’s Autumn Rhythm (seen here) is an example of a complex painting that can be grasped with a few moments of contemplation, according to Terry Teachout (links to a dead page) in the Wall Street Journal. Pollock worked during the mid 20th century, when nature was assumed to be random. However, as Robert Taylor (links to content that is no longer available) explained in a 2002 Scientific American article:

Update: For another perspective on tech employment in the United States and China, see Andy Grove’s recent article in Bloomberg Businessweek.

Take a look at BigDog and his amazing robot pals in this link to the New York Times. Or, if you love cute and cuddly, you should check out Paro.

One of the true pleasures of my job is the opportunity to travel and meet people from all over the world. Different cultures presume different business and social behaviors, of course.* But I have found that in spite of the differences, there are at least two similarities among world cultures: a passion to cheer for your national team to win the World Cup and a nearly universal reviling of the vuvuzelas.

In a recent Wall Street Journal column, Terry Teachout had a wonderful essay questioning the complexity of modern art.* He quotes from James Joyce’s Finnegans Wake, which contains sentences like this:

If you want to understand process formalization, read Jane Austen.

As you study the blue ball machine, it is easy to see how you can be mesmerized by a process that doesn’t actually do anything. See if you can find the part where the ball replaces the worker’s head. Then find the little sign that every now and then flashes NO. It never says yes

I first came across the word “disambiguation” at a weekend workshop called Ontology in Science. (There is so much that’s just wrong about what I just admitted, but never mind.) I like this word a lot because it makes people ask, “for goodness sake, what are you talking about?” But disambiguation is a serious word, especially in science. It means “to remove ambiguity.” Once you learn that there is a word for getting rid of ambiguity, you begin to realize how much ambiguity there is in the world, especially when people communicate. And it seems to me that the smarter the people and the more complex the topic, the more disambiguation is necessary.

I have been reading The Machine That Changed the World: The Story of Lean Production (1), by James Womack and others from MIT’s International Motor Vehicle Program (Content no longer available) research team. This book caused a sensation 20 years ago with its description of the Toyota Production System. The blurb on the book’s back cover says, “The hallmarks of lean production are teamwork, communication, and efficient use of resources. The results are remarkable cars with one-third the defects, built in half the factory space, using half the man-hours.”

Why is it so hard to incorporate modeling and simulation into drug development? Why do these powerful tools so often fail to provide satisfactory outcomes?

In December 2009, The Wall Street Journal sponsored its second Future of Finance Initiative (links to a dead page) to provide a forum for 80 of the world’s top financiers to brainstorm suggestions for reforming the financial system in the wake of the 2008 implosion of the global economy.

I have become addicted to David Foster Wallace’s nonfiction essays, several of which were published in A Supposedly Fun Thing I’ll Never Do Again.*