CS791L Biological-Computation and Bio-Informatics
Friday 1:30-3:30 pm
CS building Room 142
email: hava@cs.umass.edu
Office: Computer Science 344
Telephone: 7-4282
Office Hours: Mon 10:30-12:00
Course Summary
This class is a joint effort between the BIGIALS group and the Computer
Science department. All classes are arranged to reach the heterogeneous
crowd interested in physical/biological sciences and informatics. The
seminar is research oriented and will prepare the participating students
and faculty attending it for collaborative research in this
interdisciplinary area.
Bio-informatics includes the use of
computational and mathematical techniques (e.g., algorithms, machine
learning, AI, complexity theory, dynamical systems) to decipher
complicated information in biological structures, gene sequences, and
cellular signals. It also includes the construction of new technologies
that have been overlooked before and whose roots are in nature, as is
the aim of the IBM Blue gene project. This interdisciplinary field is of
critical importance for understanding information from humongous
biological sources, as well as for the design and construction of new
computational methods, software, and hardware. Main topics include
adaptive computational approaches and AI algorithms for structural and
functional biology (e.g., protein structure prediction, prediction of
proteins function and interactions prediction, fold prediction) , neural
computation, "dynamical diseases" (e.g., finding structure in cardiac
rhythms), building evolutionary robots, DNA and quantum computers, and
more.
The course will survey leading trends in bio-computation. Some topics
will be introduced by invited lecturers who are active and well known in
the field.
Assignments and Grades
Grades will be based an in-class, oral presentation, attendance,
participation in class discussion, and completing a term project. The
presentation will involve reading background material about the work of
one of the visiting scholars or on parts of the book Bio-informatics:
the machine learning approach' and presenting it in class during the
week preceding the guest lecture. A written summary of the presentation
will be distributed by the student at the day of the talk. In addition,
each student will propose a project related to their presentation, and
complete a term paper by May 6, 2002.
Students are advised to read the relevant papers of outside speakers 1
week prior to the presentations, and be ready for a discussion on
it. This is the best way of learning from their great experience.
We will also read some chapters from
Bioinformatics - The Machine
Learning Approach, by Pierre Baldi and Soren Brunak.
Tentative Schedule
Possible additional lecturers:
Talk Abstracts
- 2/8
Dr. Tzachi Pilpel
Combinatorial analyses of genetics regulatory networks
Several computational methods based on microarray data are currently
used to study genome-wide transcriptional regulation. Few studies,
however, address the combinatorial nature of transcription, a
well-established phenomenon in eukaryotes. Here we describe a new
approach using microarray data to uncover novel functional motif
combinations in the promoters of Saccharomyces cerevisiae. In addition
to identifying novel motif combinations that affect expression
patterns during the cell cycle, sporulation and various stress
responses, we observed regulatory cross-talk among several of these
processes. We have also generated motif-association maps that provide
a global view of transcription networks. The maps are highly
connected, suggesting that a small number of transcription factors are
responsible for a complex set of expression patterns in diverse
conditions. This approach may be useful for modeling transcriptional
regulatory networks in more complex eukaryotes.
- 2/15
Prof. Hod Lipson
Evolutionary robotics and Computational Design
** note: this week's meeting starts at 12:30
The fields of evolutionary computation and evolutionary
robotics study adaptive mechanisms based on natural
selection, with the aim of algorithmically applying these
ideas to solve hard problems like nonlinear optimization and
engineering design, as well as shedding light on the
evolution of natural systems. One of the difficult questions,
both algorithmically and biologically, is the emergence of
complexity: Evolutionary processes based on accumulation of
random mutations are less likely to result in improvement as
individuals grow more complex, because of the exponential
nature of the search space. This talk will present some new
directions in evolutionary computation that address this
scaling problem. Some recent new approaches based on
co-evolution, modularity, hierarchical composition and
symbiosis will be overviewed, as well as and their
application to evolution of robots. The talk will also
outline some of the research methodologies used in this
field, and some of the currently open questions.
IMAGINE A LEGO SET AT YOUR DISPOSAL: Bricks, rods, wheels,
motors and sensors are your atomic building blocks, and you
must find a way to put them together to achieve a given
high-level functionality: A machine that can move itself,
say. You know the physics of the individual components'
behaviors; you know the repertoire of pieces available, and
you know how they are allowed to connect. But how do you
determine the combination that gives you the desired
functionality? This is the problem of Open Ended Synthesis.
Although we have two proofs of feasibility: Practicing Engineers do
it, and Nature does it, we still know little about how this process
can be automated for arbitrarily complex tasks.
For further readings, see:
-
Lipson H., 2001, "Uncontrolled Engineering: A review of
Evolutionary Robotics", Artificial Life, to appear, book review.
pdf
-
Lipson, H., Pollack J. B., 2000, "Automatic Design and
Manufacture of Artificial Lifeforms", Nature 406, pp.
974-978.
pdf
- 2/22
Prof. Mary Beth Ruskai
An Introduction to Quantum Computation
Standard methods for both computation and communication encode
information in strings of 0 and 1. Quantum particles can be used
to encode information in "qubits" which, in addition to having states
corresponding to 0 and 1 can be placed in superpositions which contain
both 0 and 1 with certain probabilities. Strings of n such qubits can
simultaneously encode all possible 2^n combinations of 0 and 1, making
quantum computers inherently massively parallel. However, extracting
useful output information is constrained by the principles of quantum
measurement. In essence, the computational complexity problem has been
shifted to the output measurement.
It is quite remarkable that algorithms have been constructed which allow
one to exploit the power of quantum computers. Shor's 1994 development
of an efficient algorithm for factoring large numbers, one of a class
of algorithms based on the quantum Fourier transform, stimulated
interest and further developments.
This talk will introduce the audience to the fundamental principles of
quantum theory which make quantum computation possible and potentially
more powerful than classical communication. Rather than describing
a particular algorithm in detail, we will compare the classical discrete
Fourier transform and quantum Fourier transform in a way that
illustrates
the power of superpositions, its limitations and the fundamental role of
the quantum measurement process.
- 3/8
Prof. Simon Kasif
Systems Biology: Active Learning of Cellular Architectures
In this talk I will describe the application of computational learning and
computer science algorithms to reverse engineering, predictive modeling
and analysis of biological cell function.
Genes and their post-translational interactions provide the basic scripts
for biological cell behavior. The problems we address include comparing of
the genomic content of human and mouse genomes, creating qualitative
interpretations of gene expression data, and constructing functional
gene-based models of biological cells.
In many cases the architectures we construct resemble simplistic computer
circuits and networks. I would argue that cells can teach us a lot
about computation, and the need to unravel the mystery of living
organisms provides an endless supply of challenging basic research problems
in computing.
Ultimately, we would like to develop the capability to program biological
systems to do useful things or monitor and repair undesirable events which
requires a computational understanding of the biological components
and their interaction on different levels of resolution.
- 3/29
Prof. Bruce Tidor
Solvation effects on protein folding, binding and design:
Exploring the Electrostatic Balance
Electrostatic interactions are prevalent at protein interfaces and are
important for protein-protein and protein-ligand association.
However, due to the large desolvation penalty incurred by polar and
charged groups upon binding, on balance it is unclear whether
electrostatic interactions can be strongly stabilizing in aqueous
solution near room temperature. Continuum electrostatic calculations
suggest that effects of polar and charged groups are generally
destabilizing. A novel procedure has been developed for designing
ligand-charge distributions that optimize the balance between
unfavorable desolvation and favorable interaction. Application of the
method to protein systems demonstrates the importance of optimizing
electrostatic interactions. Charge optima reveal that electrostatics
can be moderately stabilizing and that one strategy to enhance
stability involves engineering intra-ligand interactions that become
strengthened upon desolvation and binding.
- 4/5
Prof. Leon Glass
"Dynamical" vs. "genetic" disease -
What do complex rhythms reveal about pathophysiology?
Recent discoveries in bioinformatics and genomics are revolutionizing
our understanding of the genetic basis of disease. Yet, in many
diseases the body displays complex abnormal rhythms that are amenable
to mathematical analysis. The abnormal rhythms often arise as a
consequence of changes in key parameters of an underlying
physiological control system, and as such the dynamics give important
clues about underlying mechanisms and therapy. This talk reviews
applications of the concept of dynamical disease with emphasis on
abnormal cardiac and neurological rhythms.
- 4/12
Prof. John Moult
Protein Structure Prediction
An analysis of CASP results, identifying what works, what does not, and
what the possible ways forward are.
The Scope of Structural Genomics
An analysis of how many structures will need to be solved
experimentally in order to be able to model 'all' structures with
different degrees of accuracy. Combines sequence family analysis,
coverage of structure space, and modeling reliability. An emphasis on
how useful a model is for obtaining information about function. Tends
to go off into evidence for the frequent appearance of new protein
families during evolution.
-
4/19
Prof. Leslie Kay
Context and chemistry in olfactory computation
The computational architecture of the olfactory bulb is intriguing, as
it combines a relatively ordered set of inputs from the peripheral
olfactory nerve with a massive array of inputs from many other brain
areas. When animals are anesthetized or passively exposed to odors,
the activity of the principal neurons (mitral cells) appears to be
driven by a relatively ordered set of relationships dependent on
similarities in chemical structure, which is suggestive of chemotopy.
Neural recordings from awake animals, trained to associate a
behavioral meaning with an odor stimulus, present a different picture
of odor "representation." In these experiments we find that the
activity of individual mitral cells is driven primarily by the
behavioral requirements of the stimulus. Odor representation, when
seen, is also driven by meaning. When the behavioral requirements of
the stimulus changes, so does a cell's odor selectivity. These
changes are most likely driven by input from other brain areas, one
candidate of which we have found to be the entorhinal cortex as part
of the hippocampal system. Behavioral experiments, which we have
designed to test the behavioral relevance of chemotopy, also show that
an animal's prior experience with odors influences to a large degree
the ability to recall a learned odor from among a set of chemically
similar odors. However, there is some influence of the odor's
chemical class (e.g., all alcohols smell similar, even though
individual alcohols can be easily distinguished). We also find that
in special circumstances, the chemical structures of mixture
components, combined with olfactory receptor biophysics, can determine
the perceptual quality of the mixture. These results elucidate the
computational structure of the olfactory system, which involves a
dynamic interplay between chemistry, anatomy, meaning and behavior.
-
Kay, L.M. and Laurent, G. (1999) Odor- and context-dependent modulation
of
mitral cell firing in behaving rats. Nature Neurscience,
2(11): 1003-1009.
- Kay, L.M. and Freeman, W.J. (1998) Bidirectional processing in the
olfactory-limbic axis during olfactory behavior. Behavioral
Neuroscience, 112(3): 541-553.
- Kay, L.M., Lancaster, L.R., and Freeman, W.J. (1996) Reafference and
attractors in the olfactory system during odor recognition.
International Journal of Neural Systems, 7 (4): 489-495.
-
4/26
Prof. Tony Zador
ni
The cocktail party problem: Animal models of computation
in the auditory cortex
The neurons that provide the substrate for cortical computation
appear quite noisy (although just how noisy remains a subject of some
controversy), suggesting that the cortex uses a strategy rather
different from that of a digital computer. The cortex is nevertheless
able to solve hard computational problems, such as the cocktail party
problem. (sometimes called the "symphony problem," it an auditory
analog of the object recognition problem), that remain beyond the
capacity of the fastest computers. This problem is a particular
interest because it related to the problem of selective attention, and
hence consciousness. We have been combining experimental and
theoretical approaches to study the special characteristics of
cortical "wetware" that endow it with the capacity to solve such hard
problems. I will discuss some recent experimental results in which we
have studied how acoustic signals are represented in the rodent
primary auditory cortex. While I confess these experiments have not
yet resolved how rats solves the cocktail party problem, I hope they
do provide a first step.