1 Causality in Complex Systems The Probem of Modularity Mt. Tamborine, July 8, 2009 Sandra D. Mitchell Department of History and Philosophy of Science University of Pittsburgh

2 Dictyostelium – slime mold life cycle Human Brain Honey Bee Colony DynamicCompositional Evolved

3 Complex Biological Systems Evolved contingency Multi-level organization Multi-component causal interactions Modularity compositional structure Robustness in relation to internal and external changes

4 A manipulationist account of causation Mill’s “Method of Difference” “If an instance in which the phenomenon under investigation occurs, and an instance in which it does not occur, have every circumstance in common save one, that one occurring only in the former; the circumstance in which alone the two instances differ, is the effect, or the cause, or an indispensable part of the cause, of the phenomenon.” (Mill, 1888, page 280). The paradigmatic assertion in causal relationships is that manipulation of a cause will result in the manipulation of an effect. … Causation implies that by varying one factor I can make another vary. (Cook & Campbell, 1979, p. 36, emphasis in original.)

5 What would have happened if X had been different? Jim Woodward Making Things Happen Explanation requires only invariance, not universal truth. Invariance comes in gradations or degrees. Relation between variables F (X,Y) is not universal. Under certain “ideal interventions” where the value of X changes, the function will describe the value of Y. Hence X explains Y for those ranges where the functional relationship is invariant.

6 Features of causality Invariance Relation between dependent and independent variable Relation between purported cause and effect Modularity or Separability Is a relation among multiple functional equations describing a single system i.e. A relation of independent disruptability or contribution to overall effect of casual components Insensitivity Stability of causal relation across variations in background, context, or conditions external to system

7 Modularity essential to Causality “..this is implicit in the way people think about causation…this sort of independence is essential to the notion of causation. Causation is connected to manipulability and that connection entails that separate mechanisms are in principle independently disruptable.” (Hausman and Woodward 1999)

8 Modularity beyond equations Modular equations represent distinct, autonomous, context-insensitive causal mechanisms subject to independent disruptability. Modularity in biology, especially evo-devo is held out as potential theoretical unification.

9 Modularity and Exportation Modularity reflects one form of invariance – distinct, autonomous, internally context- insensitive causal mechanisms subject to independent disruptability. A module’s behavior in one system is sufficiently stable and insensitive to be exportable other systems.

10 Is Modularity essential to causation? Some causal networks have elements that do not behave in modular ways Do we want to infer that the elements are NOT causes? Do we have to move to a finer or coarser granularity to satisfy the modularity/independence required? Do we want to infer that modular causes do not exhaust causality?

11 Genetic knock-out experiments

12 Genetic Knockouts Principle assumption: normal function of a gene can be inferred directly from its mutant phenotype. Results are hard to interpret Sometimes “intervention” on one gene  lethal Sometimes “intervention” on one gene  change in phenotype Sometimes “intervention” on one gene  virtually no change in phenotype

13 Problem of Inference “The big surprise to date is that so many individual genes, each of which had been thought important, have been found to be nonessential for development” Robert Weinberg “I don’t believe in complete redundancy. If we knock out a gene and don’t see something, we’re not looking correctly” Mario Capecchi

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15 Redundancy and Degeneracy Redundancy When a gene is “knocked out” other elements of the same structure are activated. Built in “fail-safe”. Degeneracy or Robustness When a gene is “knocked out” elements of other structures respond flexibly to issue in a similar functional outcome.

16 What do we say of this system? The causal structure reorganized? With intervention – new causal roles for elements which are non independent or context dependent The initial representation was not complete. Response to intervention indicated that not ALL the causal relations were initially represented. Existing ones became active.

Responses 1. “bite the bullet” strategy the genes in the normal genetic pathways are not causes 2. “make the world fit your theory” redescribe the network in finer or coarser granularity 3. “pluralism of causes” modular causes do not exhaust all the types of causality 17

18 Actual Causes versus Causal Laws Domain of biology is historically and currently existing organic life – not what is possible, or potential given the constraints of physics and chemistry. Biology studies ACTUAL causes i.e. a subset of what is biologically possible. And…actual causes may not behavior modularly

19 Biological Modularity “Speaking loosely, biological modules are consortia that act autonomously to produce a single form or function and are redeployed within and across species, thereby creating novelty and fueling the development and evolution of biological complexity.” Meyers 2004 Nature

20 MODULARITY in biology Apparent paradox: same gene or gene complex  different structures in same organism same gene or gene complex  different structures in different taxa Module is part of a system whose internal constituents are strongly interactive while it is as least partially independent of other modules – weakly integrated.

21 Evolutionary and developmental modularity Evolutionary Modules selectable units, i.e. change can occur in one module without causing disruption in the others, thus increasing viability while permitting adaptive variation in complex organisms. Developmental Modules Morphological units i.e. discrete interactive systems of causes with stable context insensitive effects in contributing to development of an organism.

22 Problem: Modules are system and grain dependent “Although the Notch signaling pathway is context- independent module, in the sense that the molecular interactions between its members are conserved and invariant, the outcome of Notch signaling is highly context dependent” Celis, (produces neuron, hair, dermis) “Shh (super sonic hedghog) signalling is a context- independent module with conserved functon during vertibrate muscle development” Borycki Sometimes – invariance is structure, sometimes function. Evolutionary modules do not necessarily map onto developmental modules

23 Methodological implications Singe perturbation experiments even if they indicate causal contribution of gene, assume a fixed genetic background. If there is interaction with the background genes, the results may not be applicable to other contexts. Multifactorial experiments – use existing variation to generate multiple backgrounds and compare effects of gene perturbation across all realistic genetic variation

24 Conclusion and choices Accept that what is “well-behaved” causally varies with embedded context OR Accept that there are non-modular, context sensitive actual causes that explain the behavior of biological systems. The consequence is that exporting knowledge from one system to another requires more than generalization and instantiation – need to use more local knowledge