Hi, my name is Rama Ranganathan, I'm a Professor at the Green Center for Systems Biology
and affiliated with the departments of physics and pharmacology
at UT Southwestern Medical Center
And I'll be doing a talk in this series
called "great questions in life sciences, intersection between biology, computation, and physics
Specifically, the problem I would like to ask
is a very interesting one, it's about finding the effective variables
in biological systems. So what I like to do is to describe the nature of this problem, to tell you why
finding these effective variables is an important yet a difficult problem
I'll try to propose an approach that leads to specific problems of interest
people coming from quantitative sciences into biology. So a grand goal in biology
is to quantitatively explain the behavior and evolutionary origin of so called
complex systems at many scale. I'm showing biological systems at a variety of scales
from the atomic scale, that is the structure and function of proteins
to things at the cellular scale, the designate of cellular macromolecules and systems
like the flagellum, to the design of cells and tissues
and ultimately the design and principles of ecosystems
the goal is to explain the behavior and the origin at all these scales
but the problem is difficult for 3 reasons. The first is
the problem of heterogeneity. In all these scales, system components, the things that make the parts of
make up these systems are highly heterogeneous where some of the parts and connections
are much more important than others.
For example, we know that in proteins, not all of the amino acids act equally
In fact, there a heterogeneity in the importance of amino acids
towards the central properties of proteins such as foldability, biochemical functions, and so on
the same thing is true at a subcellular systems and a macromolecular systems
the importance of the parts is heterogeneous and often is not obvious
when looking at those systems which are the parts that are important, which are the parts that are less important
the second problem is that of cooperativity
that is the systems are made up of parts, but the parts work in a way that cannot predict the whole
cannot be predicted in the activities of the parts when calculated individually
there is also potentially nonlinearity in the system
so that the behavior of the system taken overall is not an obviously decomposition into the activity of the parts taken individually
the third problem is the problem of modularity in that different groups of a component can be grouped in a quasi-independent way
to provide different aspect of function at all these scales
So the existence of the properties, the facts that these parts that make up these systems
the fact that these amino acids make up proteins, proteins and small molecules that make up macromolecular complexes and cells
cells that make up tissues, and species that make up ecosystems
the fact that these component descriptions involves heterogeneous, cooperative groups imply that what we need is a
fundamental re-parameterization of these systems to redo the
fine units of function or what I call the effective variables
as not these parts, but groups of these parts that act cooperatively, and ideally ranked by their relevance
the question is how can we do that?
The goal in doing this is to provide some sense of what natural description of what a system is. It may be intuitive
think about a protein as made up of amino acids, but in terms of the function of the protein,
It may not be the actual description. Groups of amino acids work cooperatively may be a more natural description
of what the system is. And in some sense, one could argue that solving this problem, providing a more natural description of what a system is
should be a necessary preclude for mechanistic studies of how it works
another words, understanding how it works may be simpler if we understand the effective variables that make up a system
The question is how can we do this practically?
Well it turns out with the massive expansion of techniques for massive data collection
for all these scales, people are collecting methods for collecting many examples of protein structures
and protein sequences and measurements of protein function
of course, many are working at the cellular scale to characterize many different cell types under the background of perturbation for example mutations
and small molecule perturbations
and even at the ecosystem scale, more recently people have developed synthetic ecosystem laboratories
with many replicates so people can look at the system's behavior at that scale
so with the ability to collect data in nature. The proposal is to look not at individual systems
but at statistics and many realizations of this system to see what is conserved or invariant over the ensemble
fundamentally, the variants of properties of an ensemble of realizations of a system
is itself a relevance in systems components and reactions
things that are invariant over an ensemble tends to be relevant
the second thing to do since we are interested in the cooperativity of components
is to look at correlations and epistasis between the conserved system components and reactions
and this is a basis, such a correlational analysis is a basis for decomposing into the proper cooperative modular units
that is the effective variables. Let me give an example of this, at the scale of atoms that we've been talking about.
but the approach should be more general than just this case study
So here is an example where the protein can be thought of as made up of a large collection of amino acids
that interact together, but to understand which amino acids are relevant and what are the cooperative units
this kind of approach, one collects an appropriate statistical ensemble
of instances of a protein, in this case, a protein family that shares the property of folding into that structure and carrying out the biochemical activities
and from such a statistical ensemble, one can make a matrix of relevant correlations of all the amino acids that make up the protein
and with that correlational matrix at hand
one can apply techniques such as structural decomposition
very simple way of information content in correlation matrices
to deduce collective groups of amino acids that now more appropriately represent the determinants
of protein structure and function. So this is an example at the atomic scale
but I want to point out that there are non-trivial quantitative problems at every stage of analysis
in devising this point of approach more generally for all biological complex systems
and let me just illustrate the nature of the problems that need to be worked on
For example it's not obvious when you take a statistical approach how to make an ensemble that represents
the properties of interest. In case of proteins, it seems nature that a protein family
that shares the function of folding and function and an evolutionary history
should be an appropriate ensemble over which the statistics is computed
but it's not necessarily obvious in many other systems, at a cellular scale, at the scale of ecosystems and so on
what should be an appropriate ensemble of data to collect
from which statistics can reveal the underlying variables of a system
that's a working problem to solve. The second problem is what type of statistical system one should apply
to make measures of relevance and correlation given an ensemble and the reason for this is
it depends on how the ensemble is collected for protein sequences. one of the complications for making appropriate statistics is that
the sequences are not randomly selected from the space of all sequences
these sequeces have a relationship to each other that comes from the phylogeny of all the species that contain that sequences
in other words, sampling is not random, but sampling is on some phylogenetic tree, and in such instances
it not necessary obvious what are appropriate statistical methods that define the relevance of the outcomes at positions or correlations between positions
So in general, one needs to work on what are the statistical methods that are appropriate to represent a statistical ensemble
the final problem is, how to decompose these correlational matrices to deduce the effective variables
in the simplest case, one apply linear methods such as spectral decomposition
but it's not so obvious that those decompositions are good decompositions of this information. In general, information in these correlational
in biological systems is hierarchical in nature. And for a hierarchically embedded system,
it's not yet clear what are good methods are in finding the effective variables
so these are problems are ripe to work on now for people coming into biology from quantitative sciences
because if these sort of questions can be addressed in general, then we have the possibility leading to really new understandings
of the so called design principles of complex biological systems
to understand the effective variables underlying these systems, as I said a preload
to ultimately deep mechanistic studies of these systems
so this is an example of a problem that is appropriate and ripe today for people in physics and quantitative sciences
coming into biology. Thank you very much!