Systems Biology, Mechanisms and the
Integration of Localised and Distributed Causal
Explanations
Jonathan Davies
Fondazione Bruno Kessler, Trento, Italy
11/09/2009
Introduction
• Localised Causal Explanations (LCEs) in
biology – Operon model of gene regulation
• Distributed Causal Explanations (DCEs) in
biology – Stuart Kauffman’s model of gene
regulatory networks
• Systems Biology (SB) and the integration of
LCEs and DCEs
• Against the holism/reductionism dichotomy
LCEs in (molecular) biology
• Identification of locus (loci) of control
• If multiple loci of control, separate and assign
functional/causal roles (decomposition and
localisation)
• Functional/causal role of component
accounted for in terms of intrinsic properties
• “…the protein inserts into the major groove of the
DNA helix and makes a series of molecular contacts
with the base pairs. The protein forms hydrogen
bonds, ionic bonds, and hydrophobic interactions with
the edges of the bases […] many of the proteins
responsible for gene regulation contain one of
several particularly stable folding patterns. These fit
into the major groove of the DNA double helix”
(Alberts et al. 2004 pp.271-2).
DCEs in Biology
• No functional decomposition possible
• “The hallmark of these cases is that, given a
principled structural analysis, the activities of
the parts seem to be different in kind from […]
those performed by the whole.” (Bechtel and
Richardson 1993)
• Component activities and relations
“described” with formal (mathematical) model
Gene regulatory network stability
•
•
•
•
Network consists of simple nodes (2 possible states)
Binary interactions between nodes
State transitions Boolean operations
With network connectivity (K) close to 2 network
encounters stable cycles
• Individual node functions
not dependent on intrinsic
properties of nodes
• No functional modules
• “Unlike complex systems of simple elements, in
which functions emerge from the properties of the
networks they form rather than from any specific
element, functions in biological systems rely on a
combination of the network and specific elements
involved” (Kitano 2002 p.206)
• “Heterogeneity in biological systems is not just a
complication added on top of essential properties, it
is constitutive of these essential properties …
Heterogeneity is the stuff out of which life evolved
[…] to ignore [it] is to risk exactly the kind of biological
irrelevance that has historically been the fate of so
many mathematical models in biology” (Keller 2006
p.8-9).
Systems Biology (SB)
Two streams
• Pragmatic – Molecular biology on a larger
scale. Explanatory strategy unchanged.
Continued commitment to the adequacy of
(mechanistic) LCEs.
• Systems Theoretic – Qualitative change
incorporating the integration of formal models
and molecular data.
SB and the integration of LCEs and DCEs
• Grounding the theoretic models in the
molecular detail (Kitano)
• Integration of data-rich fields with data-poor
modeling strategies informed by “higher-level”
theories (Krohs & Callebaut)
• Iterative spiral of observation and theoretical
refinement (Westerhoff and Kell)
Five Stage SB Strategy
1. Define all of the components of the system, using prior
molecular and biochemical knowledge to formulate an initial
(potentially very rough) model (in silico).
2. Systematically perturb and monitor components of the model
system and observe (measure) corresponding model
responses (globally and of other components).
3. Reconcile experimentally observed responses in vitro or in vivo
with those predicted by the model.
4. Design and perform new perturbation experiments to
distinguish between multiple or competing model hypotheses.
5. Repeat steps 2 to 4. (Ideker et al. 2001)
Conclusions
• LCEs and DCEs not a dichotomy but extremes on a
continuum of explanatory strategies
• DCEs need not be holist or non-mechanistic
• Biological systems characterised by components,
mechanisms and processes of variable modes of
interaction and degrees of context sensitivity
• Focus on the spatio-temporal distribution of causal
element in system facilitates a better understanding of
mechanistically explicable emergent phenomena
12
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