Santiago Schnell talks with
ScienceWatch.com and answers a few questions about
this month's Fast Moving Front in the field of Computer
Article: Stochastic approaches for modelling in
Journal: COMPUT BIOL CHEM, 28 (3): 165-178 JUL 2004
Addresses: Indiana Univ, Sch Informat, Informat Bldg,901 E
10th St, Bloomington, IN 47408 USA.
Oxford Ctr Ind & Appl Math, Math Inst, Oxford OX1 3LB,
Ctr Math Biol, Math Inst, Oxford OX1 3LB, England.
Univ Oxford Christ Church, Oxford OX1 1DP, England.
Univ Queensland, Dept Math, Adv Comp Modelling Ctr,
Brisbane, Qld 4072, Australia.
Why do you think your paper is highly
In our paper we unravel a new debate in the field of systems biology: What
is the appropriate modeling formulation to investigate reactions inside
cells? The biological revolution unleashed by systems biology is taking an
unfashionable stand against reductionism. Systems biologists are developing
sophisticated computational models of protein-protein interaction networks,
which require understanding the kinetic of enzyme catalyzed reactions.
The traditional approach in modeling enzyme kinetics, which encompasses the
law of mass action, has been the basis for kinetic modeling for over a
century. However, the adequacy of this approach has been questioned for
describing reactions inside the cells. The problem is aggravated further in
the literature, because there are numerous approaches for modeling
reactions inside cells. However, it is not entirely clear which approaches
are best suited for a particular set of reaction conditions inside cells.
Does it describe a new discovery, methodology, or
synthesis of knowledge?
Our article provides a state-of-the-art synthesis of stochastic approaches
available for modeling reactions inside the cells. We critically
investigated the current approaches available to model reaction
stochastically. When these models are applied to investigate reactions
inside cells, it is assumed that the random fluctuations in the reactions
are a consequence of the small number of reactant molecules inside cells.
"If we could miniaturize ourselves
and travel inside a cell, we would discover
that reactions occur
Although these models have been used successfully to describe certain
cellular physiological processes, they can lead to qualitatively different
physiological predictions. This is because they do not take into account
the compartmented and highly heterogeneous environment inside the cell.
We describe our recent efforts through Monte Carlo simulations to include
the fluctuations in reaction rates caused by the structural organization of
cells and limited diffusion of reacting molecules, due to large molecular
solvents inside cells. So, in our article, we emphasize the likely
contribution of non-reacting collisions with macromolecular solvents and
limited diffusion in the fluctuations of reactions inside cells.
Would you summarize the significance of your paper
in layman's terms?
The cell can be considered the unit of life, because the microscopic
interactions inside cells underlie the cause and order of the complex
processes occurring in an organism. This is only possible because the cell
is a dynamical entity, continuously changing. Almost everything that
happens inside the cell boils down to reactions between molecules. Cells
are able to process the input and output of reactions into hundreds of
specific cellular functions, such as secretion of molecules, cell movement,
or differentiation, into other cell types.
If we could miniaturize ourselves and travel inside a cell, we would
discover that reactions occur randomly. We would also find that the
interior of a cell is a very crowded jungle of molecules. Random
fluctuations in reaction rates are inevitable, because molecules occur in
low numbers inside the cell. Interactions occur because molecules are
constantly diffusing and colliding with the surrounding jungle of other
Traditionally, researchers considered observed fluctuations in the reaction
rates to be experimental noise, which could be ignored. However, we
recently discovered that the intrinsic noise has important information
vital to understanding the association, location, and function of molecules
inside the cells.
In our paper, we have brought the current controversies about the
appropriate mathematical formulations for investigating the dynamical
behavior of reactions inside cells to the attention of other scientists.
While there is some arbitrariness about the choice of mathematical
framework in any scientific enterprise, different mathematical formulations
are better able to capture one or more particular features of the data, and
represent the underlying hypotheses of a theory more or less faithfully.
We critically review the state-of-the-art approaches for describing the
fluctuations in chemical reactions inside the cells. We show that these
approaches are effective for modeling reactions exhibiting fluctuations due
to the small number of molecules inside the cells. However, we also show
that they fail to describe the fluctuations in reaction rates which result
from the random collisions with the jungle of solvent molecules. We
conclude our paper by presenting an alternative approach, which we
developed, to model reactions more realistically inside cells.
How did you become involved in this research and
were any particular problems encountered along the way?
Since I was an undergraduate student in the biomedical sciences, I have
been investigating the mathematical and computational approaches to
understanding the dynamical behavior of reactions under cellular
physiological conditions. At the time, biomedical scientists were not
paying enough attention to these problems. The main focus of attention was
uncovering the interaction map of genes and proteins.
I found myself rather isolated, and decided to move out of my research home
in a traditional biomedical department into the physical and mathematical
sciences. While the biomedical scientists were making spectacular advances
in the interaction maps, I focused my research attention on one key aspect
of biology that an interaction map cannot get us close to: the dynamical
behavior of biochemical reactions and physiological processes.
The transition from experimental to theoretical biology was very difficult,
because I had to learn advanced mathematical techniques and sophisticated
physical concepts to be successful in my research. However, it was worth
the effort! After revisiting the traditional approaches for modeling
reactions, I discovered that most of the mathematical approximations for
investigating reactions under physiological conditions are not valid. This
has been one of the focuses of my research for more than 10 years.
Where do you see your research leading in the
I will continue investigating the dynamical behavior of complex biological
processes. My main research interest is investigating cellular physiology
systems comprising many interacting components, where modeling and theory
may aid in the identification of the key mechanisms underlying the behavior
of the system as a whole. In my lab, we are currently focused on
investigating the dynamics and regulation mechanisms of the protein
synthesis and aggregation inside our cells. By using our new mathematical
and computational approaches to model reactions inside the cells, we expect
to construct more realistic models, which will help us to predict, prevent,
or remedy potential health problems caused by failure to produce functional
Do you foresee any social or political implications
for your research?
Not directly. However, the determination of the reaction dynamics inside
our cells will be invaluable for understanding how the interactions between
different biological molecules make a cell function. If we do not
understand the appropriate physicochemical laws describing the reaction
behavior inside cells, our understanding of life will be very limited.
Consolidating the detailed information we have gained from the gene and
protein interaction maps with dynamical models of cellular reactions and
physiological processes will propel us towards a true mechanistic
understanding of life. The practical impact will be considerable in
understanding disease and developing new drugs.
Santiago Schnell, D.Phil. (Oxon)
Associate Professor of Molecular and Integrative Physiology
Research Associate Professor of Computational Medicine and
Brehm Center for Type 1 Diabetes Research and Analysis
University of Michigan Medical School
Ann Arbor, MI, USA
KEYWORDS: COUPLED CHEMICAL-REACTIONS;
QUASI-STEADY-STATE; REACTION-KINETICS; ENZYME-KINETICS; GENE-EXPRESSION; 2
DIMENSIONS; SYSTEMS; SIMULATIONS; DIFFUSION; CELLS.