Dynamic mechanistic explanation: computational modeling of circadian rhythms as an exemplar for cognitive science

Design principles as minimal models

In this essay I suggest that we view design principles in systems biology as minimal models, for a design principle usually exhibits universal behaviors that are common to a whole range of heterogeneous (living and nonliving) systems with different underlying mechanisms. A well-known design principle in systems biology, integral feedback control, is discussed, showing that it satisfies all the conditions for a model to be a minimal model. This approach has significant philosophical implications: it not only accounts for how design principles explain, but also helps clarify one dispute over design principles, e.g., whether design principles provide mechanistic explanations or a distinct kind of explanations called design explanations.

Enactivism Meets Mechanism: Tensions & Congruities in Cognitive Science

Enactivism advances an understanding of cognition rooted in the dynamic interaction between an embodied agent and their environment, whilst new mechanism suggests that cognition is explained by uncovering the organised components underlying cognitive capacities. On the face of it, the mechanistic model’s emphasis on localisable and decomposable mechanisms, often neural in nature, runs contrary to the enactivist ethos. Despite appearances, this paper argues that mechanistic explanations of cognition, being neither narrow nor reductive, and compatible with plausible iterations of ideas like emergence and downward causation, are congruent with enactivism. Attention to enactivist ideas, moreover, may serve as a heuristic for mechanistic investigations of cognition. Nevertheless, I show how enactivism and approaches that prioritise mechanistic modelling may diverge in starting assumptions about the nature of cognitive phenomena, such as where the constitutive boundaries of cognition lie.

Mechanistic decomposition and reduction in complex, context-sensitive systems

Standard arguments in philosophy of science infer from the complexity of biological and neural systems to the presence of emergence and failure of mechanistic/reductionist explanation for those systems. I argue against this kind of argument, specifically focusing on the notion of context-sensitivity. Context-sensitivity is standardly taken to be incompatible with reductionistic explanation, because it shows that larger-scale factors influence the functioning of lower-level parts. I argue that this argument can be overcome if there are mechanisms underlying those context-specific reorganizations. I argue that such mechanisms are frequently discovered in neuroscience.

Design principles and mechanistic explanation

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard (2010) contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all mechanisms are more or less general (or abstract), and on the other, many (if not all) design principles are causal systems.

Context-sensitive computational mechanistic explanation in cognitive neuroscience

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers - including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.

Toward a Logic of the Organism: A Process Philosophical Consideration

Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems biology has focused on models consisting of static systems of differential equations operating with fixed control parameters that are measured or fitted to experimental data. However, the structure of real organisms is a highly dynamic process, the internal causality of which can only be captured by continuously changing systems of equations. In addition, in real physiological settings kinetic parameters can vary by orders of magnitude, i.e., organisms vary the value of internal quantities that in models are represented by fixed control parameters. Both the plasticity of organisms and the state dependence of kinetic parameters adds indeterminacy to the picture and asks for a new statistical perspective. This requirement could be met by the arising Biological Statistical Mechanics project, which promises to do more justice to the nature of real organisms than contemporary modeling. This article concludes that Biological Statistical Mechanics allows for a wider range of organismic ontologies than does the tacitly followed ontology of contemporary theoretical and systems biology, which are implicitly and explicitly based on systems theory.

Apophatic science: how computational modeling can explain consciousness

This study introduces a novel methodology for consciousness science. Consciousness as we understand it pretheoretically is inherently subjective, yet the data available to science are irreducibly intersubjective. This poses a unique challenge for attempts to investigate consciousness empirically. We meet this challenge by combining two insights. First, we emphasize the role that computational models play in integrating results relevant to consciousness from across the cognitive sciences. This move echoes Alan Newell’s call that the language and concepts of computer science serve as a lingua franca for integrative cognitive science. Second, our central contribution is a new method for validating computational models that treats them as providing negative data on consciousness: data about what consciousness is not. This method is designed to support a quantitative science of consciousness while avoiding metaphysical commitments. We discuss how this methodology applies to current and future research and address questions that others have raised.

Homology of process: developmental dynamics in comparative biology

Comparative biology builds up systematic knowledge of the diversity of life, across evolutionary lineages and levels of organization, starting with evidence from a sparse sample of model organisms. In developmental biology, a key obstacle to the growth of comparative approaches is that the concept of homology is not very well defined for levels of organization that are intermediate between individual genes and morphological characters. In this paper, we investigate what it means for ontogenetic processes to be homologous, focusing specifically on the examples of insect segmentation and vertebrate somitogenesis. These processes can be homologous without homology of the underlying genes or gene networks, since the latter can diverge over evolutionary time, while the dynamics of the process remain the same. Ontogenetic processes like these therefore constitute a dissociable level and distinctive unit of comparison requiring their own specific criteria of homology. In addition, such processes are typically complex and nonlinear, such that their rigorous description and comparison requires not only observation and experimentation, but also dynamical modelling. We propose six criteria of process homology, combining recognized indicators (sameness of parts, morphological outcome and topological position) with novel ones derived from dynamical systems modelling (sameness of dynamical properties, dynamical complexity and evidence for transitional forms). We show how these criteria apply to animal segmentation and other ontogenetic processes. We conclude by situating our proposed dynamical framework for homology of process in relation to similar research programmes, such as process structuralism and developmental approaches to morphological homology.

Network Physiology of Exercise: Vision and Perspectives

The basic theoretical assumptions of Exercise Physiology and its research directions, strongly influenced by reductionism, may hamper the full potential of basic science investigations, and various practical applications to sports performance and exercise as medicine. The aim of this perspective and programmatic article is to: (i) revise the current paradigm of Exercise Physiology and related research on the basis of principles and empirical findings in the new emerging field of Network Physiology and Complex Systems Science; (ii) initiate a new area in Exercise and Sport Science, Network Physiology of Exercise (NPE), with focus on basic laws of interactions and principles of coordination and integration among diverse physiological systems across spatio-temporal scales (from the sub-cellular level to the entire organism), to understand how physiological states and functions emerge, and to improve the efficacy of exercise in health and sport performance; and (iii) to create a forum for developing new research methodologies applicable to the new NPE field, to infer and quantify nonlinear dynamic forms of coupling among diverse systems and establish basic principles of coordination and network organization of physiological systems. Here, we present a programmatic approach for future research directions and potential practical applications. By focusing on research efforts to improve the knowledge about nested dynamics of vertical network interactions, and particularly, the horizontal integration of key organ systems during exercise, NPE may enrich Basic Physiology and diverse fields like Exercise and Sports Physiology, Sports Medicine, Sports Rehabilitation, Sport Science or Training Science and improve the understanding of diverse exercise-related phenomena such as sports performance, fatigue, overtraining, or sport injuries.

Mechanisms in psychology: The road towards unity?

The focus of this special issue of Theory & Psychology is on explanatory mechanisms in psychology, especially on problems of particular prominence for psychological science such as theoretical integration and unification. Proponents of the framework of mechanistic explanation claim, in short, that satisfactory explanations in psychology and related fields are causal. They stress the importance of explaining phenomena by describing mechanisms that are responsible for them, in particular by elucidating how the organization of component parts and operations in mechanisms gives rise to phenomena in certain conditions. We hope for cross-pollination between philosophical approaches to explanation and experimental psychology, which could offer methodological guidance, in particular where mechanism discovery and theoretical integration are at issue. Contributions in this issue pertain to theoretical integration and unification of psychology as well as the growing importance of causal mechanistic explanations in psychological science.

Ecological mechanisms in cognitive science

In 2010, Bechtel and Abrahamsen defined and described what it means to be a dynamic causal mechanistic explanatory model. They discussed the development of a mechanistic explanation of circadian rhythms as an exemplar of the process and challenged cognitive science to follow this example. This article takes on that challenge. A mechanistic model is one that accurately represents the real parts and operations of the mechanism being studied. These real components must be identified by an empirical programme that decomposes the system at the correct scale and localises the components in space and time. Psychological behaviour emerges from the nature of our real-time interaction with our environments—here we show that the correct scale to guide decomposition is picked out by the ecological perceptual information that enables that interaction. As proof of concept, we show that a simple model of coordinated rhythmic movement, grounded in information, is a genuine dynamical mechanistic explanation of many key coordination phenomena.

Modelling gene regulation: (De)compositional and template-based strategies

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine - using the mechanistic philosophy of science as an analytic springboard - the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene regulatory networks, but in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally.

Modularity, criticality, and evolvability of a developmental gene regulatory network

The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network-the gap gene system of dipteran insects-using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.

Mathematical modeling of circadian rhythms

Circadian rhythms are endogenous ~24-hr oscillations usually entrained to daily environmental cycles of light/dark. Many biological processes and physiological functions including mammalian body temperature, the cell cycle, sleep/wake cycles, neurobehavioral performance, and a wide range of diseases including metabolic, cardiovascular, and psychiatric disorders are impacted by these rhythms. Circadian clocks are present within individual cells and at tissue and organismal levels as emergent properties from the interaction of cellular oscillators. Mathematical models of circadian rhythms have been proposed to provide a better understanding of and to predict aspects of this complex physiological system. These models can be used to: (a) manipulate the system in silico with specificity that cannot be easily achieved using in vivo and in vitro experimental methods and at lower cost, (b) resolve apparently contradictory empirical results, (c) generate hypotheses, (d) design new experiments, and (e) to design interventions for altering circadian rhythms. Mathematical models differ in structure, the underlying assumptions, the number of parameters and variables, and constraints on variables. Models representing circadian rhythms at different physiologic scales and in different species are reviewed to promote understanding of these models and facilitate their use. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Mechanism diagrams and abstraction-by-aggregation

Mechanism diagrams exhibit visually the organized parts and operations of a biological mechanism. A mechanism diagram can facilitate mechanistic research by providing a mechanistic explanation of the phenomenon of interest. Much research has been focusing on the mechanistic explanation and the explanatory mechanistic models. As a specific type of scientific diagram, a simple mechanism diagram can be explanatory by drawing on the rich explanatory resources of non-depicted background knowledge. The relationship between the visually depicted and the background knowledge is underexplored. It is unclear how the non-depicted background knowledge of a mechanism diagram contributes to providing a better-informed explanation of the phenomenon of interest in biological sciences. With the aim to explore this relationship, I articulate that a mechanism diagram provides a mechanistic explanation by a process called abstraction-by-aggregation. Through visual cues, the unified relevant background knowledge provides an epistemic access to a better-informed explanation.

The incommensurability of emergence and modularity in complex systems: A comment on Wastell (2014)

To answer the interaction problem, dual-process theories of reasoning must explain how seemingly disparate reasoning systems affect each other and underlie the apparent unity of subjective experience. Wastell (2014) proposes complex emergence modular theory, which asserts that complex virtual reasoning modules emerge from basic reasoning modules. We contend that Wastell’s proposal fails to address the interaction problem. First, we claim that the attempt to integrate emergence with virtual modules proliferates the interaction problem instead of solving it. Second, we argue that there is no interaction problem in human reasoning if “emergence” is employed in accordance with typical applications of complex systems theory in cognitive science and psychology. Alternatively, we suggest that in order to understand human reasoning within a complex systems framework, researchers should forego conceiving of reasoning as informationally encapsulated modular systems, and instead investigate system state transitions.

How Stuttering Develops: The Multifactorial Dynamic Pathways Theory

Purpose

We advanced a multifactorial, dynamic account of the complex, nonlinear interactions of motor, linguistic, and emotional factors contributing to the development of stuttering. Our purpose here is to update our account as the multifactorial dynamic pathways theory.

Method

We review evidence related to how stuttering develops, including genetic/epigenetic factors; motor, linguistic, and emotional features; and advances in neuroimaging studies. We update evidence for our earlier claim: Although stuttering ultimately reflects impairment in speech sensorimotor processes, its course over the life span is strongly conditioned by linguistic and emotional factors.

Results

Our current account places primary emphasis on the dynamic developmental context in which stuttering emerges and follows its course during the preschool years. Rapid changes in many neurobehavioral systems are ongoing, and critical interactions among these systems likely play a major role in determining persistence of or recovery from stuttering.

Conclusion

Stuttering, or childhood onset fluency disorder (Diagnostic and Statistical Manual of Mental Disorders, 5th edition; American Psychiatric Association [APA], 2013), is a neurodevelopmental disorder that begins when neural networks supporting speech, language, and emotional functions are rapidly developing. The multifactorial dynamic pathways theory motivates experimental and clinical work to determine the specific factors that contribute to each child's pathway to the diagnosis of stuttering and those most likely to promote recovery.

Probabilistic language models in cognitive neuroscience: Promises and pitfalls

Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We highlight the advantages and potential pitfalls of this approach and indicate avenues for future research.

A Role for Endogenous Brain States in Organizational Research: Moving Toward a Dynamic View of Cognitive Processes

The dominant view in neuroscience, including functional neuroimaging, is that the brain is an essentially reactive system, in which some sensory input causes some neural activity, which in turn results in some important response such as a motor activity or some hypothesized higher-level cognitive or affective process. This view has driven the rise of neuroscience methods in management and organizational research. However, the reactive view offers at best a partial understanding of how living organisms function in the real world. In fact, like any neural system, the human brain exhibits a constant ongoing activity. This intrinsic brain activity is produced internally, not in response to some environmental stimulus, and is thus termed endogenous brain activity (EBA). In the present article we introduce EBA to organizational research conceptually, explain its measurement, and go on to show that including EBA in management and organizational theory and empirical research has the potential to revolutionize how we think about human choice and behavior in organizations.

Data graphs and mechanistic explanation

It is a widespread assumption in philosophy of science that representations of data are not explanatory-that they are mere stepping stones towards an explanation, such as a representation of a mechanism. I draw on instances of representational and explanatory practice from mammalian chronobiology to suggest that this assumption is unsustainable. In many instances, biologists employ representations of data in explanatory ways that are not reducible to constraints on or evidence for representations of mechanisms. Data graphs are used to represent relationships between quantities across conditions, and often these representations are necessary for explaining particular aspects of the phenomena under study. The benefit of the analysis is two-fold. First, it provides a more accurate account of explanatory practice in broadly mechanistic investigation in biology. Second, it suggests that there is not an explanatorily "fundamental" type of representation in biology. Rather, the practice of explanation consists in the construction of different types of representations and their employment for distinct explanatory purposes.

Using computational models to discover and understand mechanisms

Areas of biology such as cell and molecular biology have been dominated by research directed at constructing mechanistic explanations that identify parts and operations that when organized appropriately are responsible for the various phenomena they investigate. Increasingly the mechanisms hypothesized involve non-sequential organization of non-linear operations and so exceed the ability of researchers to mentally rehearse their behavior. Accordingly, scientists rely on tools of computational modeling and dynamical systems theory in advancing dynamic mechanistic explanations. Using circadian rhythm research as an exemplar, this paper explores the variety of roles computational modeling is playing. They serve not just to determine whether the mechanism will produce the desired behavior, but in the discovery process of hypothesizing mechanisms and in understanding why proposed mechanisms behave as they do.

Dynamical models: an alternative or complement to mechanistic explanations?

While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors.

Can mechanistic explanation be reconciled with scale-free constitution and dynamics?

This paper considers two objections to explanations that appeal to mechanisms to explain biological phenomena. Marom argues that the time-scale on which many phenomena occur is scale-free. There is also reason to suspect that the network of interacting entities is scale-free. The result is that mechanisms do not have well-delineated boundaries in nature. I argue that bounded mechanisms should be viewed as entities scientists posit in advancing scientific hypotheses. In positing such entities, scientists idealize. Such idealizations can be highly productive in developing and improving scientific explanations even if the hypothesized mechanisms never precisely correspond to bounded entities in nature. Mechanistic explanations can be reconciled with scale-free constitution and dynamics even if mechanisms as bounded entities don't exist.

Philosophy for the rest of cognitive science

Cognitive science has always included multiple methodologies and theoretical commitments. The philosophy of cognitive science should embrace, or at least acknowledge, this diversity. Bechtel's (2009a) proposed philosophy of cognitive science, however, applies only to representationalist and mechanist cognitive science, ignoring the substantial minority of dynamically oriented cognitive scientists. As an example of nonrepresentational, dynamical cognitive science, we describe strong anticipation as a model for circadian systems (Stepp & Turvey, 2009). We then propose a philosophy of science appropriate to nonrepresentational, dynamical cognitive science.

Circadian Rhythms and Mood Disorders: Are the Phenomena and Mechanisms Causally Related?

This paper reviews some of the compelling evidence of disrupted circadian rhythms in individuals with mood disorders (major depressive disorder, seasonal affective disorder, and bipolar disorder) and that treatments such as bright light, designed to alter circadian rhythms, are effective in treating these disorders. Neurotransmitters in brain regions implicated in mood regulation exhibit circadian rhythms. A mouse model originally employed to identify a circadian gene has proven a potent model for mania. While this evidence is suggestive of an etiological role for altered circadian rhythms in mood disorders, it is compatible with other explanations, including that disrupted circadian rhythms and mood disorders are effects of a common cause and that genes and proteins implicated in both simply have pleiotropic effects. In light of this, the paper advances a proposal as to what evidence would be needed to establish a direct causal link between disruption of circadian rhythms and mood disorders.

Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology

In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a "systems-level understanding."

Temporal decomposition: a strategy for building mathematical models of complex metabolic systems

In 'Discovering complexity' Bechtel and Richardson (1993) highlighted the connection between how biologists investigate the world and the type of explanations they give. This paper extends their account of how we investigate the world by examining the strategies used by researchers to build mathematical models of complex metabolic systems between the 1970s and 1990s. Bechtel and Richardson analysed how researchers decompose complex systems by reducing the number of variables included in the model, thus simplifying them and making them suitable objects for research and understanding. Bechtel and Abrahamsen (2005) later distinguished two types of decomposition: 1) Structural decomposition, starting with the identification of the relevant component parts and 2) functional decomposition, starting with the identification of the relevant component operations. I use my case studies to argue that temporal decomposition should be recognised as an additional strategy for investigating complex metabolic systems. Temporal decomposition involves the identification of the relevant dynamic variables. Existing accounts of decomposition are based on the assumption of a spatial hierarchy which classifies modules according to the frequency of interactions between components. Temporal decomposition is based on the assumption of a time hierarchy which classifies variables as dynamic or constant according to the relative speed with which properties of the system change.

Closing in on the constitution of consciousness

The science of consciousness is a nascent and thriving field of research that is founded on identifying the minimally sufficient neural correlates of consciousness. However, I have argued that it is the neural constitution of consciousness that science seeks to understand and that there are no evident strategies for distinguishing the correlates and constitution of (phenomenal) consciousness. Here I review this correlation/constitution distinction problem and challenge the existing foundations of consciousness science. I present the main analyses from a longer paper in press on this issue, focusing on recording, inhibition, stimulation, and combined inhibition/stimulation strategies, including proposal of the Jenga analogy to illustrate why identifying the minimally sufficient neural correlates of consciousness should not be considered the ultimate target of consciousness science. Thereafter I suggest that while combined inhibition and stimulation strategies might identify some constitutive neural activities—indeed minimally sufficient constitutive neural activities—such strategies fail to identify the whole neural constitution of consciousness and thus the correlation/constitution distinction problem is not fully solved. Various clarifications, potential objections and related scientific and philosophical issues are also discussed and I conclude by proposing new foundational claims for consciousness science.

Radical embodied cognitive neuroscience: addressing "grand challenges" of the mind sciences

It is becoming ever more accepted that investigations of mind span the brain, body, and environment. To broaden the scope of what is relevant in such investigations is to increase the amount of data scientists must reckon with. Thus, a major challenge facing scientists who study the mind is how to make big data intelligible both within and between fields. One way to face this challenge is to structure the data within a framework and to make it intelligible by means of a common theory. Radical embodied cognitive neuroscience can function as such a framework, with dynamical systems theory as its methodology, and self-organized criticality as its theory.

Models and the mosaic of scientific knowledge. The case of immunology

A survey of models in immunology is conducted and distinct kinds of models are characterized based on whether models are material or conceptual, the distinctiveness of their epistemic purpose, and the criteria for evaluating the goodness of a model relative to its intended purpose. I argue that the diversity of models in interdisciplinary fields such as immunology reflects the fact that information about the phenomena of interest is gathered from different sources using multiple methods of investigation. To each model is attached a description specifying how information about a phenomenon of interest has been acquired, highlighting points of commonality and difference between the methodological and epistemic histories of the information encapsulated in different models. These points of commonality and difference allow investigators to integrate findings from different models into more comprehensive explanatory accounts, as well as to troubleshoot anomalies and faulty accounts by going back to the original building blocks.

From molecules to behavior and the clinic: Integration in chronobiology

Chronobiology, especially the study of circadian rhythms, provides a model scientific field in which philosophers can study how investigators from a variety of disciplines working at different levels of organization are each contributing to a multi-level account of the responsible mechanism. I focus on how the framework of mechanistic explanation integrates research designed to decompose the mechanism with efforts directed at recomposition that relies especially on computation models. I also examine how recently the integration has extended beyond basic research to the processes through which the disruption of circadian rhythms contributes to disease, including various forms of cancer. Understanding these linkages has been facilitated by discoveries about how circadian mechanisms interact with mechanisms involved in other physiological processes, including the cell cycle and the immune system.

Systems biology and the integration of mechanistic explanation and mathematical explanation

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models-which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and their qualitative interactions) have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena (rather than the explanation of quantitative details), where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism's ability to respond to perturbations (beyond its actual operation). I offer general conclusions for philosophical accounts of explanation.

Developmental systems theory: what does it explain, and how does it explain it?

We examine developmental systems theory (DST) with two questions in mind: What does DST explain? How does DST explain it? To answer these questions, we start by reviewing major contributions to the origins of DST: the introduction of the idea of a "developmental system", the idea of probabilistic epigenesis, the attention to the role of information in the developmental system, and finally the explicit identification of a DST. We then consider what DST is not, contrasting it with two approaches that have been foils for DST: behavioral genetics and nativist cognitive psychology. Third, we distill out two core concepts that have defined DSTthroughout its history: epigenesis and developmental dynamics. Finally, we turn to how DST explains, arguing that it explains by elucidating mechanisms.

Basic science through engineering? Synthetic modeling and the idea of biology-inspired engineering

Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more "biology inspired."