Unsolved or Poorly Solved Computational Problems

Caltech News Server hsauro at cxx.calxxx.edu
Mon Mar 3 22:36:37 EST 2003


There are lots of unsolved problems in computational biology.

Here are a few:

How can we combine stochastic and continuous modeling, even better, can we
develop an algorithm that will switch parts of the dynamical model
automatically from stochastic to ODEs as necessary?

In almost all biochemical network simulations there will be fast and slow
reactions, can we develop a DAE solver that will automatically partition the
reactions for us at runtime into fast and slow dynamics?

What sort of theory do we need to understand very large biochemical
networks? What are the computational issues in trying to simulate very large
networks?

How can we apply classical control engineering to biological control
systems, there is already a control theory of biochemical networks but how
does this relate to engineering control theory?

and the list goes on.

Straight forward simulation of a biochemical is essentially a 'solved'
problem and there is lots of software available to this sort of work, so
what we don't need is yet another differential equation or stochastic system
solver!

>What are the important (not just interesting) problems?

My experience has been that interesting problems almost always invariable
turn out to be extremely important, I would concentrate on interesting
problems, that way you're more likely to make a significant and lasting
contribution. Examples of this abound in biology, most of our ability to
manipulate and sequence genomes arose as a result of work done in the 50s
studying viruses which infected bacteria, very interesting, but arguably, a
complete waste of time as a piece of 'useful research'. But ultimately it
led to one of the greatest revolutions in biology.

If you're concerned with human health and welfare, I don't think this sort
of work will make a significant contribution for many years, and even then
it might only affect a small number of people. If however, you managed to
persuade people to stop smoking then you'd save over 400,000 lives a year in
the USA alone!

Perhaps the one disease class which might benefit from a computational
approach is cancer, the statistics for 1985 were 340,000 deaths (excluding
lung cancer). The sort of contribution that comp bio could make is
understanding the cancer state from a control point of view. All cancers are
the result of a failure in the signaling control networks.

Herbert Sauro

"Richard Scott" <rtscott at forgetspamPacbell.net> wrote in message
news:Vyy6a.1551$3S5.173279277 at newssvr15.news.prodigy.com...
> Greetings,
>
> At work, I have been working on a high performance C++ library not based
on
> STL.  The applications area was not bioinformatics but I have recently
> gotten interested in algorithmic development for chemical and biological
> simulation, and sequence identification.  I have an engineering physics
> background but very little knowledge of either biology or chemistry.
>
> What are the major unsolved or poorly solved computational problems in
> computational biology that could potentially lead to real improvements in
> people's health and welfare?  I know this is a naive question but my
> knowledge is rudimentary, and I don't know where to find a description of
> the problems or the potential solutions.
>
> I have been surfing the Web to find answers but some issues are unclear.
> For example, is the sequencing problem solved to everyone's satisfaction?
> What kinds of models and algorithmic approaches are being used for protein
> folding?  What are the important (not just interesting) problems?
>
> Richard Scott
>





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