Prof. Hans Westerhoff: Full Abstract
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Prof. Hans Westerhoff:
“Bottom-up Systems Biology“
Systems Biology is the science that aims to lead us from macromolecules to biological function. It is about the dynamic organization of those molecules, the
consequent nonlinear interactions and about how these generate properties that are not yet present in the individual molecules. Since Life requires a minimum
genome, Systems Biology has to connect ultimately to everything in that genome that affects this emergence of function
(www.systembiology.net). Bottom-up Systems Biology starts from a limited set of molecules and examines what
new may come from their interactions. Using biology, physical chemistry and mathematics it deduces from known or experimentally determined molecular properties
and from known or empirical organization, the functional properties that emerge. In this, bottom-up Systems Biology attempts to discover new laws and principles
that govern biological systems. By testing its predictions, it then discovers unknown molecular properties, or verifies the understanding achieved.
Since its focus is on the emergence of new function from already known interactive properties of biological macromolecules, initially new experimentation is not always
needed. One may make a replica of the biological system in terms of all its components, their activities and their interactions and then have a computer calculate the
emerging behavior. The activities are specified by rate-equations (or equilibrium equations where appropriate) that indicate how processes change when the interactions
change. Balance equations specify how component concentrations change due to the activities. The computer merely integrates the equations in time (and space) or
solves them for steady state. Such 'computer replica' are being made of an increasing number of pathways in more and more organisms. They are collected in
the silicon cell live modelbase, where they are accessible to everyone for direct experimentation in silico through the web. I shall show
how one can calculate through the wwweb which may be the best drug target vis-à-vis the sleeping sickness caused by Trypanosomes. This live modelbase is also
useful for those wishing to get a feel for the system behavior of various pathways. www.siliconcell.net houses teaching modules
to aid in this.
Using the silicon cell one can also do experiments in silico. Thus one may calculate the extent to which each enzyme (or molecular process) in a pathway controls the behavior
of the pathway. Metabolic Control Analysis has defined control coefficients in order to quantify this. I shall use this to illustrate how one can discover laws that govern this
control vis-à-vis flux, concentrations, electric potentials, and transient times. I shall illustrate this with applications to EGF signaling, anti-tumor drug research, and obesity
and type 2 diabetes. Control analysis is also a way to relate the system property of control, to the properties of the components and the topology of the network. Connectivity
laws govern these relations. I shall show how the mathematics of the laws of Control Analysis translate into rules of thumb that are close to but sometimes at odds with intuition.
One should expect control of functional processes to reside also at the meta-levels of mRNA metabolism and protein metabolism. And indeed Hierarchical Control Analysis shows
that this may miss much of the relevant interactions. The same goes for regulation: when a living organism meets a challenge it often responds by regulating its own
functioning. All too often functional genomics studies, though genome-wide, are limited to a single 'layer' of cellular regulation. They presuppose that only transcriptional regulation
matters. On the other hand, there is a rich history of studies of metabolic regulation. Are we to suppose that the latter is irrelevant, or the former?
In reality Systems Biology is 'vertical' as well as 'horizontal': it does not so much involve the interactions of mRNAs, in so-called gene networks, as that it involves the interactions
between mRNAs, proteins and metabolites involved in the same functional pathway. I shall demonstrate a method, called 'Hierarchical Regulation Analysis' that enables one to
determine quantitatively how much of regulations occurs at the transcription level, how much at the translation level and how much at the metabolic level. The method prescribes
a certain type of experimentation, which turns out to be quite feasible and doubly informative thanks to yet another law, now of for regulation rather than control. Illustrating this
for yeast starved for nitrogen or carbon, we shall see that the organism employs a rich mixture of regulation strategies: it has certain enzymes biting the bullet by being regulated
strongly through gene expression and others following, through weaker metabolic regulation. Sometimes metabolic regulation works against the change in flux; parts of the pathway
appear to try to prevent over-regulation by other parts.
Doing bottom-up systems biology of the entire living cells seems an arduous task, knowing that model unicellular organism previously elected because of their simplicity, have
thousands of genes and hence thousands of processes. It will be essential therefore to develop a methodology to work on subsystems first and then to combine the results into
an understanding of the whole system. Modular kinetic and control analysis are such methodologies and I will demonstrate these for an analysis of the effects of fatty acyl CoA esters
on the performance of mitochondria.
All in all, there is now ample methodology for the implementation of bottom-up systems biology, here is no excuse is left for not going the extra mile from functional genomics to the
understanding of the basis of biological functioning( and malfunctioning!). For those who want to engage more in this, there is books, advanced courses, training
centers (www.systembiology.net), and integrative and coherent systems biology programs such as on the liver cell, yeast, ageing and host
parasite interactions (see www.systembiology.net).
