On some theoretical grounds for an organism-centered biology: Property emergence, supervenience, and downward causation

In this paper, we investigate some theoretical grounds for bridging the gap between an organism-centered biology and the chemical basis of biological explanation, as expressed in the prevailing molecular perspective in biological research. First, we present a brief survey of the role of the organism concept in biological thought. We advance the claim that emergentism (with its fundamental tenets: ontological physicalism, qualitative novelty, property emergence, theory of levels, irreducibility of the emergents, and downward causation) can provide a metaphysical basis for a coherent sort of organicism. Downward causation (DC) is the key notion in emergentist philosophy, as shown by the tension between the aspects of dependence and nonreducibility in the concept of supervenience, preferred by many philosophers to emergence as a basis for nonreductive physicalism. As supervenience physicalism does not lead, arguably, to a stable nonreductive physicalist account, we maintain that a philosophical alternative worthy of investigation is that of a combination of supervenience and property emergence in the formulation of such a stance. Taking as a starting-point O’Connor’s definition of an emergent property, we discuss how a particular interpretation of downward causation (medium DC), inspired by Aristotelian causal modes, results in an explanation of property emergence compatible with both physicalism and non-reductionism. In this account of emergence, one may claim that biology, as a science of living organization, is and remains a science of the organism, even if completely explained by the laws of chemistry. We conclude the paper with a new definition of an emergent property.     

Making sense of downward causation in manipulationism: illustrations from cancer research

Many researchers consider cancer to have molecular causes, namely mutated genes that result in abnormal cell proliferation (e.g. Weinberg 1998). For others, the causes of cancer are to be found not at the molecular level but at the tissue level where carcinogenesis consists of disrupted tissue organization with downward causation effects on cells and cellular components (e.g. Sonnenschein and Soto 2008). In this contribution, I ponder how to make sense of such downward causation claims. Adopting a manipulationist account of causation (Woodward 2003), I propose a formal definition of downward causation and discuss further requirements (in light of Baumgartner 2009). I then show that such an account cannot be mobilized in support of non-reductive physicalism (contrary to Raatikainen 2010). However, I also argue that such downward causation claims might point at particularly interesting dynamic properties of causal relationships that might prove salient in characterizing causal relationships (following Woodward 2010).     

Rethinking Causation for Data-intensive Biology: Constraints,Cancellations, and Quantized Organisms

Complex organisms thwart the simple rectilinear causality paradigm of “necessary and sufficient,” with its experimental strategy of “knock down and overexpress.” This Essay organizes the eccentricities of biology into four categories that call for new mathematical approaches; recaps for the biologist the philosopher's recent refinements to the causation concept and the mathematician's computational tools that handle some but not all of the biological eccentricities; and describes overlooked insights that make causal properties of physical hierarchies such as emergence and downward causation straightforward. Reviewing and extrapolating from similar situations in physics, it is suggested that new mathematical tools for causation analysis incorporating feedback, signal cancellation, nonlinear dependencies, physical hierarchies, and fixed constraints rather than instigative changes will reveal unconventional biological behaviors. These include “eigenisms,” organisms that are limited to quantized states; trajectories that steer a system such as an evolving species toward optimal states; and medical control via distributed “sheets” rather than single control points.     

Causation and space-time

This paper considers the physical accounts of causation in terms of conserved quantities in the light of the theory of general relativity. As it is rather well-known among physicists, there are several difficulties with the notions of conservation and localization of the (gravitational) energy-momentum within general relativity. We first begin to review the so-called conserved quantity theory of causation mainly due to Dowe and Salmon, then we discuss some consequences of these difficulties for this physical account of causation. We argue that these difficulties are due to the fundamental nature of the space-time structure as described by GR, which the conserved quantity theory of causation does not account for.     

Zooming in on Downward Causation

An attempt is made to identify a concept of ‘downward causation’ that will fit the claims of some recent writers and apply to interesting cases in biology and cognitive theory, but not to trivial cases. After noting some difficulties in achieving this task, it is proposed that in interesting cases commonly used to illustrate ‘downward causation’, (a) regularities hold between multiply realizable properties and (b) the explanation of the parallel regularity at the level of the realizing properties is non-trivial. It is argued that the relation between a realizable property and the property that realizes its effect in a particular case is not usefully regarded as a species of causation and that use of the concept of downward causation deflects our attention from our central explanatory tasks.     

A computable expression of closure to efficient causation

In this paper, we propose a mathematical expression of closure to efficient causation in terms of λ-calculus; we argue that this opens up the perspective of developing principled computer simulations of systems closed to efficient causation in an appropriate programming language. An important implication of our formulation is that, by exhibiting an expression in λ-calculus, which is a paradigmatic formalism for computability and programming, we show that there are no conceptual or principled problems in realizing a computer simulation or model of closure to efficient causation. We conclude with a brief discussion of the question whether closure to efficient causation captures all relevant properties of living systems. We suggest that it might not be the case, and that more complex definitions could indeed create crucial some obstacles to computability.     

Explanatory unification and natural selection explanations

The debate between the dynamical and the statistical interpretations of natural selection is centred on the question of whether all explanations that employ the concepts of natural selection and drift are reducible to causal explanations. The proponents of the statistical interpretation answer negatively, but insist on the fact that selection/drift arguments are explanatory. However, they remain unclear on where the explanatory power comes from. The proponents of the dynamical interpretation answer positively and try to reduce selection/drift arguments to some of the most prominent accounts of causal explanation. In turn, they face the criticism raised by statisticalists that current accounts of causation have to be violated in some of their core conditions or otherwise used in a very loose manner in order to account for selection/drift explanations. We propose a reconciliation of both interpretations by conveying evolutionary explanations within the unificationist model of scientific explanation. Therefore, we argue that the explanatory power in natural selection arguments is a result of successful unification of individual- and population-level facts. A short case study based on research on sympatric speciation will be presented as an example of how population- and individual-level facts are unified to explain the morphological mosaic of bill shape in island scrub jays (Aphelocoma insularis).     

The cement of medical thought. Evolutionary emergence and downward causation.

The aetio-pathogenetic sequences and the physio-pathological patterns of diabetes, emphysema, cholera, circulatory shock and thrombosis have been analysed with respect to an evolutionary interpretation. The diseases, although reflecting alterations of processes that can always be described in physico-chemical language, occur only at the level of biological systems which reflects the decodification of genomic project: the teleonomic projects that have been developed during evolution. The concepts of evolutionary emergence and of downward causation have been used to discuss the relationship between the molecular events responsible for the initiation of the disease, and the subsequent events responsible for the aetio-pathogenesis, for the systemic disarrangement and for the additional alterations of tissues and cells independent of the initial molecular events. In diabetes the systemic disarrangement, glycosuria and hyperglycemia, reflect the evolutionary emergence of the processes regulating carbohydrate metabolism, whereas the cardiovascular and neurological alterations are effects of the systemic disarrangement by a mechanism of downward causation. The complexity of the aetio-pathogenesis and of the physio-pathological patterns of diseases is due to the generation of information during the evolution of multi-hierarchical entities. The evolutionary epistemology approach is useful to explain the behaviour of complex systems.     

The probability of causation under a stochastic model for individual risk

In this paper we offer a mathematical definition for the probability of causation that formalizes the legal and ordinary-language meaning of the term. We show that, under this definition, even the average probability of causation among exposed cases is not identifiable from epidemiologic data. This is because the probability of causation depends both on the unknown mechanisms by which exposure affects disease risk and competing risks, and on the unknown degree of heterogeneity in the background disease risk of the exposed population. We derive the maximum and minimum values for the probability of causation consistent with the observable population quantities. We also derive the relationship of the "assigned share" (excess incidence rate as a proportion of total incidence rate) to the probability of causation.     

More on how and why: a response to commentaries

We are grateful to the commentators for taking the time to respond to our article. Too many interesting and important points have been raised for us to tackle them all in this response, and so in the below we have sought to draw out the major themes. These include problems with both the term ‘ultimate causation’ and the proximate-ultimate causation dichotomy more generally, clarification of the meaning of reciprocal causation, discussion of issues related to the nature of development and phenotypic plasticity and their roles in evolution, and consideration of the need for an extended evolutionary synthesis.     

Bio-agency and the problem of action

The Aristotle-Kant tradition requires that autonomous activity must originate within the self and points toward a new type of causation (different from natural efficient causation) associated with teleology. Notoriously, it has so far proven impossible to uncover a workable model of causation satisfying these requirements without an increasingly unsatisfying appeal to extra-physical elements tailor-made for the purpose. In this paper we first provide the essential reason why the standard linear model of efficient causation cannot support the required model of agency: its causal thread model of efficient causation cannot support the core requirement that an action is determined by, and thus an expression of, the agent’s nature. We then provide a model that corrects these deficiencies, constructed naturalistically from within contemporary biology, and argue that it provides an appropriate foundation for all the features of genuine agency. Further, we provide general characterisations of freedom and reason suitable to this bio-context (but that also capture the core classical conceptions) and show how this model reconciles them.

A biological relativity view of the relationships between genomes and phenotypes

This article explores the relativistic principle that there is no privileged scale of causality in biology to clarify the relationships between genomes and phenotypes. The idea that genetic causes are primary views the genome as a program. Initially, that view was vindicated by the discovery of mutations and knockouts that have large and specific effects on the phenotype. But we now know that these form the minority of cases. Many changes at the genome level are buffered by robust networks of interactions in cells, tissues and organs. The ‘differential’ view of genetics therefore fails because it is too restrictive. An ‘integral’ view, using reverse engineering from systems biological models to quantify contributions to function, can solve this problem. The article concludes by showing that far from breaking the supervenience principle, downward causation requires that it should be obeyed.     

"Are we there yet?": Deciding when one has demonstrated specific genetic causation in complex diseases and quantitative traits

Although mathematical relationships can be proven by deductive logic, biological relationships can only be inferred from empirical observations. This is a distinct disadvantage for those of us who strive to identify the genes involved in complex diseases and quantitative traits. If causation cannot be proven, however, what does constitute sufficient evidence for causation? The philosopher Karl Popper said, "Our belief in a hypothesis can have no stronger basis than our repeated unsuccessful critical attempts to refute it." We believe that to establish causation, as scientists, we must make a serious attempt to refute our own hypotheses and to eliminate all known sources of bias before association becomes causation. In addition, we suggest that investigators must provide sufficient data and evidence of their unsuccessful efforts to find any confounding biases. In this editorial, we discuss what "causation" means in the context of complex diseases and quantitative traits, and we suggest guidelines for steps that may be taken to address possible confounders of association before polymorphisms may be called "causative."     

Synthetic biology and genetic causation

Synthetic biology research is often described in terms of programming cells through the introduction of synthetic genes. Genetic material is seemingly attributed with a high level of causal responsibility. We discuss genetic causation in synthetic biology and distinguish three gene concepts differing in their assumptions of genetic control. We argue that synthetic biology generally employs a difference-making approach to establishing genetic causes, and that this approach does not commit to a specific notion of genetic program or genetic control. Still, we suggest that a strong program concept of genetic material can be used as a successful heuristic in certain areas of synthetic biology. Its application requires control of causal context, and may stand in need of a modular decomposition of the target system. We relate different modularity concepts to the discussion of genetic causation and point to possible advantages of and important limitations to seeking modularity in synthetic biology systems.     

Causation at different levels: tracking the commitments of mechanistic explanations

This paper tracks the commitments of mechanistic explanations focusing on the relation between activities at different levels. It is pointed out that the mechanistic approach is inherently committed to identifying causal connections at higher levels with causal connections at lower levels. For the mechanistic approach to succeed a mechanism as a whole must do the very same thing what its parts organised in a particular way do. The mechanistic approach must also utilise bridge principles connecting different causal terms of different theoretical vocabularies in order to make the identities of causal connections transparent. These general commitments get confronted with two claims made by certain proponents of the mechanistic approach: William Bechtel often argues that within the mechanistic framework it is possible to balance between reducing higher levels and maintaining their autonomy at the same time, whereas, in a recent paper, Craver and Bechtel argue that the mechanistic approach is able to make downward causation intelligible. The paper concludes that the mechanistic approach imbued with identity statements is no better candidate for anchoring higher levels to lower ones while maintaining their autonomy at the same time than standard reductive accounts are, and that what mechanistic explanations are able to do at best is showing that downward causation does not exist.     

Noise-Driven Causal Inference in Biomolecular Networks

Single-cell RNA and protein concentrations dynamically fluctuate because of stochastic ("noisy") regulation. Consequently, biological signaling and genetic networks not only translate stimuli with functional response but also random fluctuations. Intuitively, this feature manifests as the accumulation of fluctuations from the network source to the target. Taking advantage of the fact that noise propagates directionally, we developed a method for causation prediction that does not require time-lagged observations and therefore can be applied to data generated by destructive assays such as immunohistochemistry. Our method for causation prediction, "Inference of Network Directionality Using Covariance Elements (INDUCE)," exploits the theoretical relationship between a change in the strength of a causal interaction and the associated changes in the single cell measured entries of the covariance matrix of protein concentrations. We validated our method for causation prediction in two experimental systems where causation is well established: in an E. coli synthetic gene network, and in MEK to ERK signaling in mammalian cells. We report the first analysis of covariance elements documenting noise propagation from a kinase to a phosphorylated substrate in an endogenous mammalian signaling network.     

The extended evolutionary synthesis and the role of soft inheritance in evolution

In recent years, a number of researchers have advocated extending the modern synthesis in evolutionary biology. One of the core arguments made in favour of an extension comes from work on soft inheritance systems, including transgenerational epigenetic effects, cultural transmission and niche construction. In this study, we outline this claim and then take issue with it. We argue that the focus on soft inheritance has led to a conflation of proximate and ultimate causation, which has in turn obscured key questions about biological organization and calibration across the life span to maximize average lifetime inclusive fitness. We illustrate this by presenting hypotheses that we believe incorporate the core phenomena of soft inheritance and will aid in understanding them.     

Head Tilt and Fertility Contribute to Different Aspects of Female Facial Attractiveness

Subjective attractiveness ratings of facial portraits of women taken at the fertile phase of the menstrual cycle are higher than those of portraits of the same women taken during non‐fertile periods. As female faces tilted downward are rated as more attractive and female courtship behaviours change across the menstrual cycle, we investigated whether systematic downward tilt of women's faces during the fertile phase might be responsible for increased attractiveness ratings. In the original study (Proc. R. Soc. Lond. B, 271, 2004, S272), the fertile‐phase portrait of each woman was deemed more attractive in 56–62% of cases. When the portraits were reclassified by head pitch, the more downward‐tilted portrait was preferred in 64–73% of cases. The fertile‐phase portrait was no more likely to be the downward‐tilted one, however, suggesting that effects of fertility on attractiveness are not simply due to changes in head position. We also had these portraits rated (N = 130) for physical attractiveness and behavioural allure. Fertile‐phase portraits were rated as more physically attractive than non‐fertile portraits, while more downward‐tilted portraits were rated as more behaviourally alluring than less downward‐tilted ones. These data not only confirm reported effects of head tilt and fertility on perceived female attractiveness, but also suggest that these factors influence different components of the attractiveness percept.     

Organisational closure in biological organisms

The central aim of this paper consists in arguing that biological organisms realize a specific kind of causal regime that we call "organisational closure"; i.e., a distinct level of causation, operating in addition to physical laws, generated by the action of material structures acting as constraints. We argue that organisational closure constitutes a fundamental property of biological systems since even its minimal instances are likely to possess at least some of the typical features of biological organisation as exhibited by more complex organisms. Yet, while being a necessary condition for biological organization, organisational closure underdetermines, as such, the whole set of requirements that a system has to satisfy in order to be taken as a paradigmatic example of organism. As we suggest, additional properties, as modular templates and control mechanisms via dynamical decoupling between constraints, are required to get the complexity typical of full-fledged biological organisms.     

The Science and Philosophy of a Method for Assessing Environmental Causes

When an environmental impairment has been identified, it becomes necessary to identify the cause so that an appropriate action can be planned. However, causation is difficult to establish—both conceptually and in practice. To ensure that the U.S. Environmental Protection Agency's (USEPA's) method for causal assessment is appropriate and defensible, we reviewed concepts of causation from philosophers, statisticians, epidemiologists, and others. This article summarizes the results of that review and explains how it relates to the USEPA's method. We include a five-step process: (1) identify alternative candidate causes; (2) logically eliminate when possible; (3) diagnose when possible; (4) analyze the strength of evidence for remaining candidate causes; and (5) identify the most likely cause. We also encourage three practices: (1) use a consistent process; (2) do not claim proof of causation; and (3) document the evidence and inferences. This approach allows assessors to identify the most likely cause or, failing that, to reduce the set of possible causes and identify information needs for another iteration of causal assessment.