Innateness as an explanatory concept

Although many of the issues surrounding innateness have received a good deal of attention lately, the basic concept of token innateness has been largely ignored. In the present paper, I try to correct this imbalance by offering an account of the innateness of token traits. I begin by explaining Stephen Stich's account of token innateness and offering a counterexample to that account. I then clarify why the contemporary biological approaches to innateness will not be able to resolve the problems that beset Stich's account. From there, I develop an alternative understanding of the innateness of token traits, what I call a causal/explanatory account. The argument to be made is that token innateness is both a causal, and an explanatory, concept. After clarifying this understanding of innateness, and showing how it handles several counterexamples to other accounts, I end with some comments on what the causal/explanatory account suggests for our understanding of innateness in general.     

Causal specificity and the instructive–permissive distinction

I use some recent formal work on measuring causation to explore a suggestion by James Woodward: that the notion of causal specificity can clarify the distinction in biology between permissive and instructive causes. This distinction arises when a complex developmental process, such as the formation of an entire body part, can be triggered by a simple switch, such as the presence of particular protein. In such cases, the protein is said to merely induce or "permit" the developmental process, whilst the causal "instructions" for guiding that process are already prefigured within the cells. I construct a novel model that expresses in a simple and tractable way the relevant causal structure of biological development and then use a measure of causal specificity to analyse the model. I show that the permissive-instructive distinction cannot be captured by simply contrasting the specificity of two causes as Woodward proposes, and instead introduce an alternative, hierarchical approach to analysing the interaction between two causes. The resulting analysis highlights the importance of focusing on gene regulation, rather than just the coding regions, when analysing the distinctive causal power of genes.     

Causal Regularities in the Biological World of Contingent Distributions

Former discussions of biological generalizations have focused on the question of whether there are universal laws of biology. These discussions typically analyzed generalizations out of their investigative and explanatory contexts and concluded that whatever biological generalizations are, they are not universal laws. The aim of this paper is to explain what biological generalizations are by shifting attention towards the contexts in which they are drawn. I argue that within the context of any particular biological explanation or investigation, biologists employ two types of generations. One type identifies causal regularities exhibited by particular kinds of biological entities. The other type identifies how these entities are distributed in the biological world.     

Units and passages: A view for evolutionary biology and ecology

Many authors, including paleobiologists, cladists and so on, adopt a nested hierarchical viewpoint to examine the relationships among different levels of biological organization. Furthermore, species are often considered to be unique entities in functioning evolutionary processes and one of the individuals forming a nested hierarchy.I have attempted to show that such a hierarchical view is inadequate in evolutionary biology. We should define units depending on what we are trying to explain. Units that play an important role in evolution and ecology do not necessarily form a nested hierarchy. Also the relationships among genealogies at different levels are not simply nested. I have attempted to distinguish the different characteristics of passages when they are used for different purposes of explanation. In my analysis, species and monophyletic taxa cannot be uniquely defined as single units that function in ecological and evolutionary processes.The view discussed in this paper may provide a more general basis for testing competing theories in evolution, and provide new insights for future empirical studies.     

Association of Host and Microbial Species Diversity across Spatial Scales in Desert Rodent Communities

Relationships between host and microbial diversity have important ecological and applied implications. Theory predicts that these relationships will depend on the spatio-temporal scale of the analysis and the niche breadth of the organisms in question, but representative data on host-microbial community assemblage in nature is lacking. We employed a natural gradient of rodent species richness and quantified bacterial communities in rodent blood at several hierarchical spatial scales to test the hypothesis that associations between host and microbial species diversity will be positive in communities dominated by organisms with broad niches sampled at large scales. Following pyrosequencing of rodent blood samples, bacterial communities were found to be comprised primarily of broad niche lineages. These communities exhibited positive correlations between host diversity, microbial diversity and the likelihood for rare pathogens at the regional scale but not at finer scales. These findings demonstrate how microbial diversity is affected by host diversity at different spatial scales and suggest that the relationships between host diversity and overall disease risk are not always negative, as the dilution hypothesis predicts.     

Measuring Gender

Over the past several years, various operational definitions of gender have been used in studies of gender conformity in homosexual males. The goal of these studies is to demonstrate that childhood gender nonconformity (CGN) is either the proximate cause of adult homosexuality or an intermediate step in a biologically mediated process. The hypothesis of a causal connection between the development of gender and sexual orientation is embedded within the context of a biological (evolutionary) understanding of human behavior. Thus, testing the hypothesis of a causal connection between CGN and sexuality requires a concept of gender that is compatible with the basic principles of biological causation and our current understanding of evolutionary processes. I will argue that the concepts of gender used in the attempt to demonstrate a causal connection between CGN and sexual orientation are inappropriate because they provide no uniform, consistent method for identifying and measuring the biologically significant components of gender. I will also argue that the concept of gender that does emerge from these studies suggests an hypothesis about the connection between sexuality and gender that is not consistent with the cross-gendered theory of the etiology of homosexuality.     

Explaining embryological development: Should integration be the goal?

Two approaches to an integration of evolution and development are often distinguished, one “neo-Darwinian” and the other “structuralist”. Should these approaches in turn be integrated? Kelly Smith recently stated that we need a “more complete” theory of biological order, suggesting integration as the ideal. In response to him, I argue that a recognition of different types of scientific questions and causal explanation is more urgent. Do we understand development when we know the crucial factors in the process of differentiation, or rather when we know the laws that govern the transformations of fields? Without a recognition of these different explanatory ideals, “integration” is likely to have the character of annexation.     

Functional annotation of hierarchical modularity

In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology) and the association of individual genes or proteins with these concepts (e.g., GO terms), our method will assign a Hierarchical Modularity Score (HMS) to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our method using Saccharomyces cerevisiae data from KEGG and MIPS databases and several other computationally derived and curated datasets. The code and additional supplemental files can be obtained from http://code.google.com/p/functional-annotation-of-hierarchical-modularity/ (Accessed 2012 March 13).     

Agential autonomy and biological individuality

What is a biological individual? How are biological individuals individuated? How can we tell how many individuals there are in a given assemblage of biological entities? The individuation and differentiation of biological individuals are central to the scientific understanding of living beings. I propose a novel criterion of biological individuality according to which biological individuals are autonomous agents. First, I articulate an ecological–dynamical account of natural agency according to which, agency is the gross dynamical capacity of a goal-directed system to bias its repertoire to respond to its conditions as affordances. Then, I argue that agents or agential dynamical systems can be agentially dependent on, or agentially autonomous from, other agents and that this agential dependence/autonomy can be symmetrical or asymmetrical, strong or weak. Biological individuals, I propose, are all and only those agential dynamical systems that are strongly agentially autonomous. So, to determine how many individuals there are in a given multiagent aggregate, such as multicellular organism, a colony, symbiosis, or a swarm, we first have to identify how many agential dynamical systems there are, and then what their relations of agential dependence/autonomy are. I argue that this criterion is adequate to the extent that it vindicates the paradigmatic cases, and explains why the paradigmatic cases are paradigmatic, and why the problematic cases are problematic. Finally, I argue for the importance of distinguishing between agential and causal dependence and show the relevance of agential autonomy for understanding the explanatory structure of evolutionary developmental biology.     

Neural syntax: cell assemblies, synapsembles, and readers

A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as "cell assemblies," underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by "reader-actuator" mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights ("synapsembles"). The existing support for this tripartite framework is reviewed and strategies for experimental testing of its predictions are discussed.     

In silico model‐based inference: A contemporary approach for hypothesis testing in network biology

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model‐based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model‐based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:1247–1261, 2014     

Perspective: maternal kin groups and the origins of asymmetric genetic systems-genomic imprinting, haplodiploidy, and parthenogenesis

The genetic systems of animals and plants are typically eumendelian. That is, an equal complement of autosomes is inherited from each of two parents, and at each locus, each parent's allele is equally likely to be expressed and equally likely to be transmitted. Genetic systems that violate any of these eumendelian symmetries are termed asymmetric and include parent-specific gene expression (PSGE), haplodiploidy, thelytoky, and related systems. Asymmetric genetic systems typically arise in lineages with close associations between kin (gregarious siblings, brooding, or viviparity). To date, different explanatory frameworks have been proposed to account for each of the different asymmetric genetic systems. Haig's kinship theory of genomic imprinting argues that PSGE arises when kinship asymmetries between interacting kin create conflicts between maternally and paternally derived alleles. Greater maternal than paternal relatedness within groups selects for more "abstemious" expression of maternally derived alleles and more "greedy" expression of paternally derived alleles. Here, I argue that this process may also underlie origins of haplodiploidy and many origins of thelytoky. The tendency for paternal alleles to be more "greedy" in maternal kin groups means that maternal-paternal conflict is not a zero-sum game: the maternal optimum will more closely correspond to the optimum for family groups and demes and for associated entities such as symbionts. Often in these circumstances, partial or complete suppression of paternal gene expression will evolve (haplodiploidy, thelytoky), or other features of the life cycle will evolve to minimize the conflict (monogamy, inbreeding). Maternally transmitted cytoplasmic elements and maternally imprinted nuclear alleles have a shared interest in minimizing agonistic interactions between female siblings and may cooperate to exclude the paternal genome. Eusociality is the most dramatic expression of the conflict-reducing effects of haplodiploidy, but its original and more widespread function may be suppression of intrafamilial cannibalism. In rare circumstances in which paternal gene products gain access to maternal physiology via a placenta, PSGE with greedy paternal gene expression can persist (e.g., in mammals).     

Mapping indicator models: From intuitive problem structuring to quantified decision-making in sustainable forest management

Most commonly, sustainability indicator sets presented as lists do not take into account interactions among indicators in a systematic manner. Vice versa, existing environmental indicator systems do not provide a formalized approach for problem structuring and quantitative decision support. In this paper, techniques for considering indicator relationships are highlighted and a coupled approach between a qualitative and a quantitative method is analysed. Cognitive mapping (CM) is used for structuring indicators and three different causal maps are derived based on established sustainability concepts: (a) criteria and indicators (C&I hierarchy), (b) indicator network, and (c) Driving Force-Pressure-State-Impact-Response (DPSIR) system. These maps are transferred to the Analytic Network Process (ANP) to allow their application in multi-criteria decision analysis (MCDA).In an application example, Pan-European indicators for sustainable forest management (SFM) are utilized in an ANP-based assessment. The effects of the model structure on the overall evaluation result are demonstrated by means of three reporting periods on Austrian forestry.In a comparative analysis of CM and ANP it is tested whether their measures of indicator significance do correspond. Both centrality in CM and single limited priorities in ANP have been reported to identify key indicators that play an important role in networks. We found out that the correspondence between CM and ANP is the stronger the more rigidly cause-effect relationships are interpreted, which is the case for the DPSIR system of SFM indicators.It is demonstrated that using indicator sets without consideration of the indicator interactions will cause shortcomings for evaluation and assessment procedures in SFM. Given strict and consistent definition of causal indicator relationships, a coupled use of CM and ANP is recommendable for both enhancing the process of problem structuring as well as supporting preference-based evaluation of decision alternatives.     

Species as Explanatory Hypotheses: Refinements and Implications

The formal definition of species as explanatory hypotheses presented by Fitzhugh (Marine Biol 26:155–165, 2005a, b) is emended. A species is an explanatory account of the occurrences of the same character(s) among gonochoristic or cross-fertilizing hermaphroditic individuals by way of character origin and subsequent fixation during tokogeny. In addition to species, biological systematics also employs hypotheses that are ontogenetic, tokogenetic, intraspecific, and phylogenetic, each of which provides explanatory hypotheses for distinctly different classes of causal questions. It is suggested that species hypotheses can not be applied to organisms with obligate asexual, parthenogenetic, and self-fertilizing modes of reproduction. Hypotheses explaining shared characters among such organisms are, instead, strictly phylogenetic. Several implications of this emended definition are examined, especially the relations between species, intraspecific, and phylogenetic hypotheses, as well as the limitations of species names to be applied to temporally different characters within populations.     

Biochemical systems theory: operational differences among variant representations and their significance

An appropriate language or formalism for the analysis of complex biochemical systems has been sought for several decades. The necessity for such a formalism results from the large number of interacting components in biochemical systems and the complex non-linear character of these interactions. The Power-Law Formalism, an example of such a language, underlies several recent attempts to develop an understanding of integrated biochemical systems. It is the simplest representation of integrated biochemical systems that has been shown to be consistent with well-known growth laws and allometric relationships--the most regular, quantitative features that have been observed among the systemic variables of complex biochemical systems. The Power-Law Formalism provides the basis for Biochemical Systems Theory, which includes several different strategies of representation. Among these, the synergistic-system (S-system) representation is the most useful, as judged by a variety of objective criteria. This paper first describes the predominant features of the S-system representation. It then presents detailed comparisons between the S-system representation and other variants within Biochemical Systems Theory. These comparisons are made on the basis of objective criteria that characterize the efficiency, power, clarity and scope of each representation. Two of the variants within Biochemical Systems Theory are intimately related to other approaches for analyzing biochemical systems, namely Metabolic Control Theory and Flux-Oriented Theory. It is hoped that the comparisons presented here will result in a deeper understanding of the relationships among these variants. Finally, some recent developments are described that demonstrate the potential for further growth of Biochemical Systems Theory and the underlying Power-Law Formalism on which it is based.     

The need for co-product allocation in the life cycle assessment of agricultural systems—is “biophysical” allocation progress?

Purpose

Several new “biophysical” co-product allocation methodologies have been developed for LCA studies of agricultural systems based on proposed physical or causal relationships between inputs and outputs (i.e. co-products). These methodologies are thus meant to be preferable to established allocation methodologies such as economic allocation under the ISO 14044 standard. The aim here was to examine whether these methodologies really represent underlying physical relationships between the material and energy flows and the co-products in such systems, and hence are of value.

Methods

Two key components of agricultural LCAs which involve co-product allocation were used to provide examples of the methodological challenges which arise from adopting biophysical allocation in agricultural LCA: (1) the crop production chain and (2) the multiple co-products produced by animals. The actual “causal” relationships in these two systems were illustrated, the energy flows within them detailed, and the existing biophysical allocation methods, as found in literature, were critically evaluated in the context of such relationships.

Results and discussion

The premise of many biophysical allocation methodologies has been to define relationships which describe how the energy input to agricultural systems is partitioned between co-products. However, we described why none of the functional outputs from animal or crop production can be considered independently from the rest on the basis of the inputs to the system. Using the example of manure in livestock systems, we also showed why biophysical allocation methodologies are still sensitive to whether a system output has economic value or not. This sensitivity is a longstanding criticism of economic allocation which is not resolved by adopting a biophysical approach.

Conclusions

The biophysical allocation methodologies for various aspects of agricultural systems proposed to date have not adequately explained how the physical parameters chosen in each case represent causal physical mechanisms in these systems. Allocation methodologies which are based on shared (but not causal) physical properties between co-products are not preferable to allocation based on non-physical properties within the ISO hierarchy on allocation methodologies and should not be presented as such.

     

Probabilistic causation and the explanatory role of natural selection

The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that five explananda have been assigned to the theory of natural selection and that four of them may be actually considered explananda of natural selection. For the second question, we claim that a probabilistic conception of causality and the statistical relevance concept of explanation are both good models for understanding the explanatory role of natural selection. We review the biological and philosophical disputes about the explanatory role of natural selection and formalize some explananda in probabilistic terms using classical results from population genetics. Most of these explananda have been discussed in philosophical terms but some of them have been mixed up and confused. We analyze and set the limits of these problems.