I propose that a sociological and historical examination of nanotechnologists can contribute more to an understanding of nanotechnology than an ontological definition. Nanotechnology emerged from the convergent evolution of numerous "technical knowledge communities"-networks of tightly-interconnected people who operate between disciplines and individual research groups. I demonstrate this proposition by sketching the co-evolution of computational chemistry and computational nanotechnology. Computational chemistry arose in the 1950s but eventually segregated into an ab initio, basic research, physics-oriented flavor and an industry-oriented, molecular modeling (...) and visualization, biochemical flavor. Computational nanotechnology arose in the 1990s as a synthesis of these two subgroups, infused by people and practices from computational materials science, engineering, computer science, and elsewhere. I show that to understand the aims and outcomes of computational nanotechnology-and nanotechnology more generally-we need to understand relationships between different, but related, nano knowledge communities and their dependence on particular practices, artifacts, and theories. (shrink)

Thanks to genetic and biochemical advances on the molecular mechanism of circadian rhythms in Drosophila, theoretical models closely related to experimental observations can be considered for the regulatory mechanism of the circadian clock in this organism. Modeling is based on the autoregulatory negative feedback exerted by a complex between PER and TIM proteins on the expression of per and tim genes. The model predicts the occurrence of sustained circadian oscillations in continuous darkness. When incorporating light‐induced TIM degradation, the (...) model accounts for damping of oscillations in constant light, entrainment of the rhythm by light‐dark cycles of varying period or photoperiod, and phase shifting by light pulses. The model further provides a molecular dynamical explanation for the permanent or transient suppression of circadian rhythmicity triggered in a variety of organisms by a critical pulse of light. Finally, the model shows that to produce a robust rhythm the various clock genes must be expressed at the appropriate levels since sustained oscillations only occur in a precise range of parameter values. BioEssays 22:84–93, 2000. ©2000 John Wiley & Sons, Inc. (shrink)

The processes of wound healing and bone regeneration and problems in tissue engineering have been an active area for mathematical modeling in the last decade. Here we review a selection of recent models which aim at deriving strategies for improved healing. In wound healing, the models have particularly focused on the inflammatory response in order to improve the healing of chronic wound. For bone regeneration, the mathematical models have been applied to design optimal and new treatment strategies for normal (...) and specific cases of impaired fracture healing. For the field of tissue engineering, we focus on mathematical models that analyze the interplay between cells and their biochemical cues within the scaffold to ensure optimal nutrient transport and maximal tissue production. Finally, we briefly comment on numerical issues arising from simulations of these mathematical models. (shrink)

Based on genetic and biochemical advances on the molecular mechanism of circadian rhythms, a computational model for the mammalian circadian clock is used to examine the dynamical bases of circadian‐clock‐related physiological disorders in humans. Entrainment by the light–dark cycle with a phase advance or a phase delay is associated with the Familial advanced sleep phase syndrome (FASPS) or the Delayed sleep phase syndrome (DSPS), respectively. Lack of entrainment corresponding to the occurrence of quasiperiodic oscillations with or without phase jump (...) can be associated with the non‐24 h sleep–wake syndrome. In the close vicinity of the entrainment domain, the model uncovers the possibility of infradian oscillations of very long period. Perturbation in the form of chronic jet lag, as used in mice, prevents entrainment of the circadian clock and results in chaotic or quasiperiodic oscillations. It is important to clarify the conditions for entrainment and for its failure because dysfunctions of the circadian clock may lead to physiological disorders, which pertain not only to the sleep–wake cycle but also to mood and cancer. BioEssays 30:590–600, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

Biochemical networks, including those containing signaling pathways, display a wide range of regulatory properties. These include the ability to propagate information across different time scales and to function as switches and oscillators. The mechanisms underlying these complex behaviors involve many interacting components and cannot be understood by experiments alone. The development of computational models and the integration of these models with experiments provide valuable insight into these complex systems‐level behaviors. Here we review current approaches to the development of computational (...) models of biochemical networks and describe the insights gained from models that integrate experimental data, using three examples that deal with ultrasensitivity, flexible bistability and oscillatory behavior. These types of complex behavior from relatively simple networks highlight the necessity of using theoretical approaches in understanding higher order biological functions. BioEssays 24:1110–1117, 2002. © 2002 Wiley‐Periodicals, Inc. (shrink)

HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is (...) often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement. (shrink)

Sabdabrahma Siddhanta, popularized by Patanjali and Bhartruhari will be scientifically analyzed. Sphota Vada, proposed and nurtured by the Sanskrit grammarians will be interpreted from modern physics and communication engineering points of view. Insight about the theory of language and modes of language acquisition and communication available in the Brahma Kanda of Vakyapadeeyam will be translated into modern computational terms. A flowchart of language processing in humans will be given. A gross model of human language acquisition, comprehension and communication process forming (...) the basis to develop software for relevant mind-machine modeling will be presented. The implications of such a model to artificial intelligence and cognitive sciences will be discussed. The essentiality and necessity of a physics, communication engineering , biophysical and biochemical insight as both complementary and supplementary to using mathematical and computational methods in delineating the theory of Sanskrit language is put forward. Natural language comprehension as distinct and different from natural language processing is pointed out. (shrink)

The desire to understand the mathematics of living systems is increasing. The widely held presupposition that the mathematics developed for modeling of physical systems as continuous functions can be extended to the discrete chemical reactions of genetic systems is viewed with skepticism. The skepticism is grounded in the issue of scientific invariance and the role of the International System of Units in representing the realities of the apodictic sciences. Various formal logics contribute to the theories of biochemistry and molecular (...) biology and genetics. Various paths of extension are invoked in these formal logics in order to express the information of biological apodicticism. Symbolizing the appropriate notations for invariant relations and for biological extensions of relations is fundamental to the exact generating functions of discrete algebraic biology. Aspects of philosophical perspectives of the relation scientific number systems are contrasted. The deep distinction between physical motion and biological motion is expressed in terms the roles of Aristotelian causes. The interior motion within perplex numbers is contrasted with the exterior motion of physical systems. The need for a new mathematics for biology is suggested. (shrink)

In this article, we provide a case study examining how integrative systems biologists build simulation models in the absence of a theoretical base. Lacking theoretical starting points, integrative systems biology researchers rely cognitively on the model-building process to disentangle and understand complex biochemical systems. They build simulations from the ground up in a nest-like fashion, by pulling together information and techniques from a variety of possible sources and experimenting with different structures in order to discover a stable, robust result. (...) Finally, we analyze the alternative role and meaning theory has in systems biology expressed as canonical template theories like Biochemical Systems Theory. (shrink)

Algorithmic chemistries are often based on a fixed formalism which limits the fragment of chemistry expressible in the domain of the models. This results in limited applicability of the models in contemporary mathematical chemistry and is due to the poor fit between the logic used for model construction and the system being modeled. In this paper, I propose a system-oriented methodology which selects a formalism through a mapping of chemical transformation rules to proof-theoretic structural rules. Using a formal specification framework (...) from the field of artificial chemistry, expressive adequacy is ensured by the choice of logic being based on the system properties to be modeled. To illustrate the methodology, a case study is provided that shows how the proposed approach selects linear logic for modeling resource sensitivity and the proof-theoretic interpretation facilitates translation to a programming language. Since the method results in a plurality of models, I conclude with a discussion on how the proposal contributes to multi-model paradigms in computational pharmacology. (shrink)

A biophysical and biochemical perspective of Brahmajnaana will be advanced by viewing Upanishads and related books as “Texts of Science on human mind”. A biological and cognitive science insight of Atman and Maya, the results of breathing process; constituting and responsible for human consciousness and mental functions will be developed. The Advaita and Dvaita phases of human mind, its cognitive and functional states will be discussed. These mental activities will be modeled as brain-wave modulation and demodulation processes. The energy-forms (...) and their transformations as ideas/moods/experiences/thoughts/feelings/utterances/knowing/perception/experience/mood; and a theory of human cognition and communication will be advanced. The sameness of these and processes taking place and steps involved in human language acquisition and communication processes will be highlighted taking ideas from Sabdabrahma Siddhanta and Sphota Vaada, for which the basis is Brahmajnaana only. In fine, a physiological psychological and neurological model of human consciousness and function of mind based on Indian spiritual thought will be derived and discussed using concepts from modern science and technology. The application of these derivations in the fields of physiological psychology, mind-machine modeling, natural language comprehension branch of artificial intelligence and neurology to model and imitate human mental functions will be hinted. -/- . (shrink)

A standard platitude about the function of causal knowledge or theories is that they are valuable because they support prediction, explanation, and control. Knowledge of predator-prey relations enables us to predict future animal populations, as well as design policies or interventions that help influence those populations. If we understand the underlying biochemical mechanisms of some disease, then we can predict who is at risk for it, explain why it produces particular symptoms, and develop interventions to try to reduce its (...) prevalence or the symptom severity. Of course, there are many situations in which one has, for practical reasons, only some of these desiderata; for example, control might be infeasible for technical or ethical reasons. But these remain, for many researchers, the ideal for why causal knowledge is a valuable end of scientific inquiry, including biological inquiry. There are, however, certain types of systems—in particular, chaotic systems—in which it appears that these ends are unattainable, and these systems appear to be widespread in the biological domain, broadly construed. In this paper, we will show why it is natural to think that causal models of chaotic systems cannot satisfy any of the three functions. But we will also show why this natural thought is wrong: we can have usable causal knowledge about even chaotic systems. Moreover, the ways in which we can have such knowledge lead us naturally to rethink a standard understanding of how causal learning and modeling proceed. In particular, just as we often must find the appropriate variables for a causal system, we also must determine the proper level or granularity of description for the dynamics of that system. (shrink)

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation and development of (...) mechanistic models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)

Bioscientists generate far more data than their minds can handle, and this trend is likely to continue. With the aid of a small set of versatile tools for mathematical modeling and statistical assessment, bioscientists can explore their real-world systems without experiencing data overflow. This article outlines an approach for combining modern high-throughput, low-cost, but non-selective biospectroscopy measurements with soft, multivariate biochemometrics data modeling to overview complex systems, test hypotheses, and making new discoveries. From preliminary, broad hypotheses and goals, (...) many relevant samples are selected and measured with respect to many informative variables. The resulting tables represent a “cacophony” of data. From these, the most relevant and reliable “underlying harmonies and rhythms” are extracted and tested statistically, displayed for interpretation, and used for prediction. Outliers are detected automatically. Interesting subsets of samples can then be chosen for in-depth analyses in subsequent research cycles. This pragmatic, top-down approach takes advantage of developments in both “soft,” data-driven modeling and “hard,” knowledge-driven cultures. Data analytical examples show how information-rich biospectroscopy can be used for characterizing and quantifying known and unknown chemical constituents and physical phenomena in intact biosamples. This is based on a combination of deductive “hard” and inductive “soft” modeling. The examples represent NIR spectra of biochemical mixtures, FTIR spectra of a microbiological fermentation process, and FTIR analysis of fatty acids in milk for functional genomics. (shrink)

Four modes of language acquisition and communication are presented translating ancient Indian expressions on human consciousness, mind, their form, structure and function clubbing with the Sabdabrahma theory of language acquisition and communication. The modern scientific understanding of such an insight is discussed. . A flowchart of language processing in humans will be given. A gross model of human language acquisition, comprehension and communication process forming the basis to develop software for relevantmind-machine modeling will be presented. The implications of such (...) a model to artificial intelligence and cognitive sciences will be discussed. The essential nature and necessity of a physics, communication engineering , biophysical and biochemical insight as both complementary and supplementary to using mathematical and computational methods in delineating the theory of language processing in humans is put forward. (shrink)

The highly ordered neuronal projections from the retina to the tectum mesencephali (optic tectum) in several vertebrate groups have been intensively studied. Several hypotheses so far have been proposed, suggesting mechanisms to explain the topographical and biochemical specificity of the retinotectal projections during ontogeny. In the present paper we compare the main hypotheses of retinotectal development with respect to the nature of specificity envisaged, the activity-dependence versus inheritance criterium and the strategy of argument, in casu the descriptive versus interferential (...) type of argument. Matching of the current developmental hypotheses and mechanisms for elimination or persistance of contralateral, respectively ipsilateral connections, is attempted with respect to the known neuroanatomical connectivity data in the amphibian optic tectum and with respect to the symmetry relations between ipsilateral and contralateral connection patterns described in amphibians and other vertebrate groups. Local mechanisms influencing the survival potential of synaptic contacts between retinal afferents and tectal neurons are probably essential for generating global symmetry patterns. Finally, a global topological approach is discussed with respect to its applicability in the amphibian retinotectal projection system. Basic assumptions of topological modeling appear to rely on anatomical and functional properties of the visual system like left-right symmetry, dichotomy, (absence of) convergence and segregation of fibers and neurons into columnar information units. It is concluded however, that more neuroanatomical, physiological and ultrastructural data are needed to establish a formalized operation model of the amphibian retinotectal system. (shrink)

The prediction of protein–protein interactions based on independently obtained structural information for each interacting partner remains an important challenge in computational chemistry. Procedures where hypothetical interaction models (or decoys) are generated, then ranked using a biochemically relevant scoring function have been garnering interest as an avenue for addressing such challenges. The program PatchDock has been shown to produce reasonable decoys for modeling the association between pig alpha-amylase and the VH-domains of camelide antibody raised against it. We designed a biochemically (...) relevant method by which PatchDock decoys could be ranked in order to separate near-native structures from false positives. Several thousand steps of energy minimization were used to simulate induced fit within the otherwise rigid decoys and to rank them. We applied a partial free energy function to rank each of the binding modes, improving discrimination between near-native structures and false positives. Sorting decoys according to strain energy increased the proportion of near-native decoys near the bottom of the ranked list. Additionally, we propose a novel method which utilizes regression analysis for the selection of minimization convergence criteria and provides approximation of the partial free energy function as the number of algorithmic steps approaches infinity. (shrink)

Kärin Nickelsen’s Explaining Photosynthesis: Models of Biochemical Mechanisms, 1840–1960 gives a much needed historical and philosophical account of one of the major research projects in modern plant sciences. This look at the scientific models of photosynthesis expands upon Nickelsen’s previous work with eighteenth century botanical illustration which she viewed as a type of modeling activity. Weaving between active research and the discourses around it, Nickelsen boldly attempts to find some bridge between the history of science and the philosophy (...) of science by identifying heuristic strategies based on her historical representation of photosynthesis research. While philosophically productive and rich in historical content, Nickelsen's text demonstrates, in opposition to its stated intention, that the historian’s goal of avoiding grand narratives may be incompatible with drawing philosophical conclusions based on historical work. (shrink)

Almost 40 years since the discovery of microtubule dynamic instability, the molecular mechanisms underlying microtubule dynamics remain an area of intense research interest. The “standard model” of microtubule dynamics implicates a “cap” of GTP‐bound tubulin dimers at the growing microtubule end as the main determinant of microtubule stability. Loss of the GTP‐cap leads to microtubule “catastrophe,” a switch‐like transition from microtubule growth to shrinkage. However, recent studies, using biochemical in vitro reconstitution, cryo‐EM, and computational modeling approaches, challenge the (...) simple GTP‐cap model. Instead, a new perspective on the mechanisms of microtubule dynamics is emerging. In this view, highly dynamic transitions between different structural conformations of the growing microtubule end – which may or may not be directly linked to the nucleotide content at the microtubule end – ultimately drive microtubule catastrophe. (shrink)

In the 1960’s Brian Goodwin published a couple of mathematical models showing how feedback inhibition can lead to oscillations and discussed possible implications of this behaviour for the physiology of the cell. He also presented key ideas about the rich dynamics that may result from the coupling between such biochemical oscillators. Goodwin’s work motivated a series of theoretical investigations aiming at identifying minimal mechanisms to generate limit cycle oscillations and deciphering design principles of biological oscillators. The three-variable Goodwin model (...) (adapted by Griffith) can be seen as a core model for a large class of biological systems, ranging from ultradian to circadian clocks. We summarize here main ideas and results brought by Goodwin and review a couple of modeling works directly or indirectly inspired by Goodwin’s findings. (shrink)

The view that the initiation of branching into two sympatric species may not require natural selection emerged in Victorian times. In the 1980s paleontologist Stephen Jay Gould gave a theoretical underpinning of this nongenic “chromosomal” view, thus reinstating Richard Goldschmidt’s “heresy” of the 1930s. From modeling studies with computer-generated “biomorphs,” zoologist Richard Dawkins also affirmed Goldschmidt, proclaiming the “evolution of evolvability.” However, in the 1990s, while Gould and Dawkins were recanting, bioinformatic, biochemical, and cytological studies were providing a (...) deeper underpinning. In 2001 this came under attack from leaders in the field who favored Dawkins’ genic emphasis. Now, with growing evidence for the uncoupling of speciation from adaptation, we can reinstate again Goldschmidt’s view and clarify its 19th-century roots. (shrink)

The concepts developed using Upanishadic insight regarding human consciousness, mind and mental processes and their applications in information acquisition and transmission by, through and in human body will be used to model human cognitive processes. A sequential reversible process by the stepwise transformation of (i) infrasonic form of energy and transformation of information already stored in (ii) biochemical form within as memory, and retrieved as inner mental world into (iii) electrochemical and then into (iv) mechanical form while communicating and (...) the reverse of it as the (i) input stimuli from outside world as light, sound, chemical, mechanical and heat forms, into (ii) electrochemical, (iii) biochemical and finally into (iv) infra sonic form while acquiring and understanding processes is put forward and discussed. Comparisons will be made between energy transformations in electronics communication processes and these psychic energy transformations that give rise to cognitive processes. This comparison gives an insight and theory of origin, structure, function and cessation of human mental functions. A comparative diagram that describes application of infrasonic energy transformations associated with bionics as biochemical, electrochemical and mechanical forms will be given. (shrink)

A classic analytic approach to biological phenomena seeks to refine definitions until classes are sufficiently homogenous to support prediction and explanation, but this approach founders on cases where a single process produces objects with similar forms but heterogeneous behaviors. I introduce object spaces as a tool to tackle this challenging diversity of biological objects in terms of causal processes with well-defined formal properties. Object spaces have three primary components: (1) a combinatorial biological process such as protein synthesis that generates objects (...) with parts that are modular, independent, and organized according to an invariant syntax; (2) a notion of “distance” that relates the objects according to rules of change over time as found in nature or useful for algorithms; (3) mapping functions defined on the space that map its objects to other spaces or apply an evaluative criterion to measure an important quality, such as parsimony or biochemical function. Once defined, an object space can be used to represent and simulate the dynamics of phenomena on multiple scales; it can also be used as a tool for predicting higher-order properties of the objects, including stitching together series of causal processes. Object spaces are the basis for a strategy of theorizing, discovery, and analysis in biology: as heuristic idealizations of biology, they help us transform inchoate, intractable problems into articulated, well-structured ones. Developing an object space is a research strategy with a long, successful history under many other names, and it offers a unifying but not overreaching approach to biological theory. (shrink)

The syllables and series of sounds composing Gayatri Mantra, and the sense and meaning attached to them are analyzed using Upanishadic Wisdom, Advaitha Philosophy and Sabdabrahma Siddhanta. The physical structure of mind as revealed by this analysis is presented. An insight of various phases of mind, their rise and set, their significance and implications to cognitive sciences and natural language comprehension branch of artificial intelligence are discussed. The possible applications of such an insight in the fields of cognitive sciences, (...) class='Hi'>modeling of human mental processes and language learning/communication processes are hinted. (shrink)

Function talk is a constant across different life sciences. From macro-evolution to genetics, functions are mentioned everywhere. For example, a limb’s function is to allow movement and RNA polymerases’ function is to transcribe DNA. Biochemistry is not immune from such a characterization; the biochemical world seems to be a chemical world embedded within biological processes. Specifically, biochemists commonly ascribe functions to biomolecules and classify them accordingly. This has been noticed in the recent philosophical literature on biochemical kinds. But (...) while a lot has been written on biological and psychological functions, little has been said explicitly about biochemical fuctions. Here, I explore functional attribution to biochemical molecules. I argue that if we accept this attribution, then biochemical functions are constituted by chemical dispositional properties that causally contribute to selected biological processes. In section 2, I discuss the controversy concerning the characterization of biochemical functions. In section 3, I consider whether a biological or a chemical under- standing of function can be applied to biochemical functions, illustrating the account with the example of vitamin B12. The result is that none of the theories on their own provides an adequate characterization. In section 4, I present an account of biochemical functions. In section 5, I assess this account taking into consideration common requirements for a theory of functions and an objection. (shrink)

Chemical kinds are generally treated as having timelessly fixed identities. Biological kinds are generally treated as evolved and/or evolving entities. So what kind of kind is a biochemical kind? This article defends the thesis that biochemical molecules are clustered chemical kinds, some of which—namely, evolutionarily conserved units—are also biological kinds. On this thesis, a number of difficulties that have recently occupied philosophers concerned with proteins and kinds are shown to be either resolved or dissolved. 1 Introduction2 Conflicting Intuitions (...) about Kinds of Proteins3 Against Permissive Pluralism4 Against Nominalism, Toward a Duality of Kinds5 Biological Kinds, Chemical Kinds, and Their Relations6 Implications for Biological Individuals7 Implications for Monism and Scientific Practice. (shrink)

Possibility semantics offers an elegant framework for a semantic analysis of modal logic that does not recruit fully determinate entities such as possible worlds. The present papers considers the application of possibility semantics to the modeling of the indeterminacy of the future. Interesting theoretical problems arise in connection to the addition of object-language determinacy operator. We argue that adding a two-dimensional layer to possibility semantics can help solve these problems. The resulting system assigns to the two-dimensional determinacy operator a (...) well-known logic (coinciding with the logic of universal modalities under global consequence). The paper concludes with some preliminary inroads into the question of how to distinguish two-dimensional possibility semantics from the more established branching framework. (shrink)

Chemical kinds (e.g. gold) are generally treated as having timelessly fixed identities. Biological kinds (e.g. goldfinches) are generally treated as evolved and/or evolving entities. So what kind of kind is a biochemical kind? This paper defends the thesis that biochemical molecules are clustered chemical kinds, some of which–namely, evolutionarily conserved units–are also biological kinds.On this thesis, a number of difficulties that have recently occupied philosophers concerned with proteins and kinds are shown to be resolved or dissolved.

The present thesis explores some metaphysical issues concerning biochemical kinds and the relations between chemical and biological properties and phenomena. The main result of this thesis is that there is something sui generis about biochemical kinds. This result is motivated by two theoretical steps. The first is characterising biochemical functions as weakly emergent from the chemical structure [Chapter 3, Chapter 6]. The second is via an account for which biochemical kinds are natural categories [Chapter 4, Chapter (...) 7]. The thesis comprises four parts. Part I [Chapter 1, Chapter 2] aims to offer the methodological and conceptual tools that underpin this research. Chapter 1 presents the debate on the unity of science and the motivation of the research. Chapter 2 presents the account of natural kinds that will be used throughout the thesis. Part II [Chapter 3, Chapter 4] offers a detailed metaphysical analysis of the molecular gene. Chapter 3 argues that molecular genes are weakly emergent from nucleic acids. Chapter 4 argues that the category "molecular gene" can be deemed a natural kind, following the account presented in Chapter 2. Part III [Chapter 5, Chapter 6, Chapter 7] explores some of the themes related to biochemical kinds and generalises some of the results from Part II. Chapter 5 considers biochemical functions and how functional attribution should be interpreted for biochemical molecules. Chapter 6 considers the relation between biochemical functions and structure, spelt in terms of weak emergence and explores unity in biochemistry. Chapter 7 considers the topic of biochemical kinds per se and argues that biochemical kinds are natural categories. Part IV [Chapter 8] elucidates the main outcome and the implications that this research can have for future discussion in the philosophy and metaphysics of biochemistry and the unity of science. (shrink)

Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are (...) in general neither predictive nor explanatory and introduce avoidable conceptual confusions. I conclude that a correct understanding of natural selection requires explicitly causal models of reproductive success. *Received May 2006; revised December 2006. †To contact the author, please write to: Department of Philosophy, Kansas State University, 201 Dickens Hall, Manhattan, KS 66506; e‐mail: [email protected] . (shrink)

Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A ∨ B) > C at a causal model M as a (...) weighted average of the probability of C in those submodels that truthmake A∨ B (Briggs 2012; Fine 2016, 2017). The weights of the submodels are given by the inverse distance to the original model M, based on a distance metric proposed by Eva, Stern, and Hartmann (2019). Apart from solving a major problem in the epistemology of counterfactuals, our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined. (shrink)

Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is (...) conceptual clarification, with a redefinition of concepts central to the understanding of the phenomenon and the conditions of its occurrence. (shrink)

The topics of modeling and information come together in at least two ways. Computational modeling and simulation play an increasingly important role in science, across disciplines from mathematics through physics to economics and political science. The philosophical questions at issue are questions as to what modeling and simulation are adding, altering, or amplifying in terms of scientific information. What changes with regard to information acquisition, theoretical development, or empirical confirmation with contemporary tools of computational modeling? In (...) this sense the title of this article is read in the following way: What kind of information is modeling information? What kind of information does modeling give us? (shrink)

A great variety of medical conditions are subject to the placebo effect. Although there is mounting evidence to suggest that the placebo effect is related to the expectation of clinical benefit, little is still known about the biochemical bases underlying placebo responses. Positron emission tomography studies have recently shown that the placebo effect in Parkinson’s disease, pain, and depression is related to the activation of the limbic circuitry. The observation that placebo administration induces the release of dopamine in the (...) ventral striatum of patients with Parkinson’s disease suggests a link between the placebo effect and reward mechanisms. In addition to Parkinson’s disease, the placebo-reward model may also apply to other disorders. However, the relative contribution of the different neurotransmitters and neuropeptides that are known to be involved in modulating the activity of the limbic system may be disease-specific. Thus, while the placebo-induced clinical benefit observed in Parkinson’s disease would mostly reflect the release of dopamine in the dorsal striatum, the activation of opioid and serotonin pathways could be particularly implicated in mediating placebo responses encountered in pain and depression, respectively. (shrink)

A great variety of medical conditions are subject to the placebo effect. Although there is mounting evidence to suggest that the placebo effect is related to the expectation of clinical benefit, little is still known about the biochemical bases underlying placebo responses. Positron emission tomography studies have recently shown that the placebo effect in Parkinson’s disease, pain, and depression is related to the activation of the limbic circuitry. The observation that placebo administration induces the release of dopamine in the (...) ventral striatum of patients with Parkinson’s disease suggests a link between the placebo effect and reward mechanisms. In addition to Parkinson’s disease, the placebo-reward model may also apply to other disorders. However, the relative contribution of the different neurotransmitters and neuropeptides that are known to be involved in modulating the activity of the limbic system may be disease-specific. Thus, while the placebo-induced clinical benefit observed in Parkinson’s disease would mostly reflect the release of dopamine in the dorsal striatum, the activation of opioid and serotonin pathways could be particularly implicated in mediating placebo responses encountered in pain and depression, respectively. (shrink)

We review the most prominent modeling approaches in social epistemology aimed at understand- ing the functioning of epistemic communities and provide a philosophy of science perspective on the use and interpretation of such simple toy models, thereby suggesting how they could be integrated with conceptual and empirical work. We highlight the need for better integration of such models with relevant findings from disciplines such as social psychology and organization studies.

Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as (...) a bona fide object, specified by the modeler and used to represent and reason about some portion of the concrete empirical world. On some indirect views, model systems are abstract entities, such as mathematical structures, while on other views they are concrete hypothetical things. Here I assess these views and offer a novel account of models. I argue that regarding models as abstracta results in some significant tensions with the practice of modeling, especially in areas where non-mathematical models are common. Furthermore, viewing models as concrete hypotheticals raises difficult questions about model-world relations. The view I argue for treats models as direct, albeit simplified, representations of targets in the world. I close by suggesting a treatment of model-world relations that draws on a recent work by Stephen Yablo concerning the notion of partial truth. (shrink)

Critics of contemporary metaphysics argue that it attempts to do the hard work of science from the ease of the armchair. Physics, not metaphysics, tells us about the fundamental facts of the world, and empirical psychology is best placed to reveal the content of our concepts about the world. Exploring and understanding the world through metaphysical reflection is obsolete. In this paper, I will show why this critique of metaphysics fails, arguing that metaphysical methods used to make claims about the (...) world are similar to scientific methods used to make claims about the world, but that the subjects of metaphysics are not the subjects of science. Those who argue that metaphysics uses a problematic methodology to make claims about subjects better covered by natural science get the situation exactly the wrong way around: metaphysics has a distinctive subject matter, not a distinctive methodology. The questions metaphysicians address are different from those of scientists, but the methods employed to develop and select theories are similar. In the first section of the paper, I will describe the sort of subject matter that metaphysics tends to engage with. In the second section of the paper, I will show how metaphysical theories are classes of models and discuss the roles of experience, common sense and thought experiments in the construction and evaluation of such models. Finally, in the last section I will discuss the way these methodological points help us to understand the metaphysical project. Getting the right account of the metaphysical method allows us to better understand the relationship between science and metaphysics, to explain why doing metaphysics successfully involves having a range of different theories, to understand the role of thought experiments involving fictional worlds, and to situate metaphysical realism in a scientifically realist context. (shrink)

Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility (...) of models to non-programming researchers is essentially non-existent, and even for other modelers, the profusion of source code in a multitude of programming languages, written without programming guidelines, makes it almost impossible to access, check, explore, re-use, or continue to develop. It is high time to change this situation, especially since the tools are now readily available to do so. We propose that the modeling community adopt three simple guidelines that would ensure that computational models would be accessible to the broad range of researchers in cognitive science. We further emphasize the pivotal role that journal editors must play in making computational models accessible to readers of their journals. (shrink)

Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in (...) for a physically different kind of process. I discuss the advantages of this approach in the context of evolutionary biology. (shrink)

PurposeThis study aims to investigate the impact of perceived service quality on tourist satisfaction and behavioral intentions and explore the potential mediating role of tourist satisfaction in the relationship between service quality and behavioral intentions in the yoga tourism context during the COVID-19 pandemic. Further, this is to examine to what extent yoga tourist satisfaction directly affects their behavioral intentions.Design/methodology/approachBased on a review of literature, the study proposes a conceptual model to test four hypothesized relationships among the constructs of perceived (...) service quality, tourist satisfaction, and behavioral intentions. Data was collected by using a self-administrated questionnaire that was developed and directed to a convenience sample of yoga tourists. Structural equation modeling was employed to determine the relationship between study constructs.FindingsThe results of SEM illustrated that all the hypothesized relationships are supported. The findings confirm that yoga tourists’ behavioral intentions are significantly affected directly and indirectly by perceived service quality. Additionally, tourist satisfaction significantly partially mediates the relationship between PSQ and tourists’ behavioral intentions.Research limitationsThe subject of this study was yoga tourists staying in yoga retreats/studios in Egyptian destinations. Future research... (shrink)

It is largely acknowledged that natural languages emerge not just from human brains but also from rich communities of interacting human brains (Senghas, ). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here, we take a step toward investigating the precise role of community structure in the emergence of linguistic (...) conventions with both naturalistic empirical data and computational modeling. We first show conventionalization of lexicons in two different classes of naturally emerging signed systems: (a) protolinguistic “homesigns” invented by linguistically isolated Deaf individuals, and (b) a natural sign language emerging in a recently formed rich Deaf community. We find that the latter conventionalized faster than the former. Second, we model conventionalization as a population of interacting individuals who adjust their probability of sign use in response to other individuals' actual sign use, following an independently motivated model of language learning (Yang, , ). Simulations suggest that a richer social network, like that of natural (signed) languages, conventionalizes faster than a sparser social network, like that of homesign systems. We discuss our behavioral and computational results in light of other work on language emergence, and other work of behavior on complex networks. (shrink)

Optimization models have often been useful in attempting to understand the adaptive significance of behavioral traits. Originally such models were applied to isolated aspects of behavior, such as foraging, mating, or parental behavior. In reality, organisms live in complex, ever-changing environments, and are simultaneously concerned with many behavioral choices and their consequences. This target article describes a dynamic modeling technique that can be used to analyze behavior in a unified way. The technique has been widely used in behavioral studies (...) of insects, fish, birds, mammals, and other organisms. The models use biologically meaningful parameters and variables, and lead to testable predictions. Limitations arise because nature's complexity always exceeds our modeling capacity. (shrink)

Two strategies for using a model as “null” are distinguished. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. When these strategies are conflated, models are illegitimately privileged as accepted until rejected. I illustrate this using the neutral theory of ecology and draw general lessons from this case. First, (...) scientists cannot draw certain conclusions using null modeling. Second, these conclusions follow using baseline modeling, but doing so requires more evidence. (shrink)

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection (...) is the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism. (shrink)

The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful (...) motivation for choosing the strategy of modeling. Second, we show that the development of the model follows a trajectory from a “how possibly” to a “how actually” model. We discuss how and to what extent Volterra and D’Ancona were able to advance their model along that trajectory. It turns out they were unable to establish that their model was fully applicable to any system. Third, we consider another instance of model-based science: Darwin’s model of the origin and distribution of coral atolls in the Pacific Ocean. Darwin argued more successfully that his model faithfully represents the causal structure of the target system, and hence that it is a “how actually” model. (shrink)