Molecular biology is a relatively new and very successful branch of science but currently it faces challenges posed by very complex issues that cannot be addressed by a traditional reductionist approach. However, despite its origins in the providential shift of some theoretical physicists to biology, currently molecular biology is immersed in a blind trend in which high-throughput technology, able to generate trillions of data, is becoming the leading edge of a discipline that has traded rational (...) and critical thinking for computer power, data crunching and statistics in hope of finding, once more, its way. However, the complex questions facing MB may only be solved by a paradigm shift in the causal way of thinking, so that complex, emergent, non-linear phenomena may be effectively understood in top-down and not in bottom-up terms. (shrink)

In this paper, I defend the agent/patient distinction against critics who argue that causal interactions are symmetrical. Specifically, I argue that there is a widespread type of causal interaction between distinct entities, resulting in a type of ontological asymmetry that provides principled grounds for distinguishing agents from patients. The type of interaction where the asymmetry is found is when one of the entities undergoes a change in kind, structure, powers, or intrinsic properties as a result of the interaction while the (...) other does not. Interactions of this type are widespread in molecular biology and chemistry. I focus specifically on (i) the actions of enzymes on substrates and (ii) water molecules breaking the bonds of polarized molecules. Finally, I respond to objections by laying out and to a limited extent defending three commitments of my account: emergent entities and powers, realism about chemical kinds or structures, and the assumption of the agent/patient distinction in functional attributions in biology. (shrink)

The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More (...) specifically, I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

In 1926, Haldane published an essay titled 'On Being the Right Size' in which he argued that the structure, function, and behavior of an organism are strongly conditioned by the physical forces that exert the greatest impact at the scale at which it exists. This chapter puts Haldane’s insight to work in the context of contemporary cell and molecular biology. Owing to their minuscule size, cells and molecules are subject to very different forces than macroscopic organisms. In a (...) sense, macroscopic and microscopic entities inhabit different “worlds”: the former is ruled by gravity and inertia, whereas the latter is governed by Brownian motion. One implication is that we should be extremely skeptical of models and analogies that seek to explain properties of microscopic entities by appealing to properties of macroscopic ones. Unfortunately, this is precisely what the appeal to engineering metaphors in molecular biology attempts to do. Molecular biologists routinely resort to such metaphors because they are familiar and intuitively intelligible. But if our machines were the size of molecules it would be impossible for them to function the way they do. It follows that we should avoid distorting biological reality by construing it in engineering terms. In this chapter I examine four key metaphors in molecular biology – “genetic program,” “cellular circuitry,” “molecular machine,” and “molecular motor” – and I argue that their deficiencies derive from their neglect of scale. I also try to explain why many biologists today appear to have forgotten the importance of scale that Haldane drew attention to in his essay. I suggest that the reason has to do with the influence of Schrödinger’s argument in 'What is Life?' regarding the stability of the gene. (shrink)

The Aristotelian axiom that function follows form was beautifully instantiated in molecular biology by the discovery of DNA’s structure that immediately suggested how DNA might work as depository and vehicle for genetic information. However, later on molecular biology became infatuated with the gene that became the center of the universe. This gene-centered viewpoint is an obstacle for the emerging field of evo-devo aiming at finding the causal connections between evolution and biological development. Here it is argued (...) that molecular biology must reject the narrow-minded causality associated with the gene-centered view and so it must recover to the spirit of Aristotelian natural history and its four types of causation in order to foster the evo-devo field of research. (shrink)

An exploration of the nexus between ecology, evolutionary biology and molecular biology, via the concept of phenotypic plasticity.

Denis Nobel looks at four important misinterpretations of molecular biology concerning evolutionary processes and demonstrates that the new synthesis today looks rather outdated. The modern synthesis is nearly 80 years old. The proponents who worked out the modern synthesis had no access to the current knowledge on cell biology, genetics, epigenetics, RNA biology and virology. Therefore this contribution adds several aspects which Nobel’s article does not explicitly mention, providing some examples for a better understanding of evolutionary (...) novelty. (shrink)

Unless one embraces activities as foundational, understanding activities in mechanisms requires an account of the means by which entities in biological mechanisms engage in their activities—an account that does not merely explain activities in terms of more basic entities and activities. Recent biological research on molecular motors exemplifies such an account, one that explains activities in terms of free energy and constraints. After describing the characteristic “stepping” activities of these molecules and mapping the stages of those steps onto the (...) stages of the motors’ hydrolytic cycles, researchers pieced together from images of the molecules in different hydrolyzation states accounts of how the chemical energy in ATP is transformed in the constrained environments of the motors into the characteristic activities of the motors. We argue that New Mechanism’s standard set of analytic categories—entities, activities, and organization—should be expanded to include constraints and energetics. Not only is such an expansion required descriptively to capture research on molecular motors but, more importantly from a philosophical point of view, it enables a non-regressive account of activities in mechanisms. In other words, this expansion enables a philosophical account of mechanistic explanation that avoids a regress of entities and activities “all the way down.” Rather, mechanistic explanation bottoms out in constraints and energetics. (shrink)

The guide explains the basic concepts of natural, social and evolutionary components and methods of risk management and control of modern biotechnologies stemming from the general theory of human evolution. A a transdisciplinary approach is a feature of the presentation of the material The risk is considered from the point of view of evolutionary anthropology, as a basic element of a stable evolutionary strategy of our biological species and the basis of a combination of natural-scientific and humanitarian forms of presentation (...) and interpretation of the material. CONTENT BIOSAFETY AND BIOETHICS IN BIOTECHNOLOGY (Introduction). Part 1. BIOTECHNOLOGY AS A FACTOR OF EVOLUTION AND A SOURCE OF TECHNOGENIC RISK. Chapter 1. GENERAL THEORY OF TECHNOLOGICAL RISK. Chapter 2. RISK AS AN ATTRIBUTE OF STABLE HUMAN EVOLUTIONARY STRATEGY. Chapter 3. BIOTECHNOLOGY AS A SOURCE OF BIORISKS Chapter 4. COMPONENTS OF THE "BIOLOGICAL FACTOR" OF RISK. Chapter 5. BIOLOGICAL WEAPONS and BIOTERRORISM. Part 2. BIOLOGICAL, SOCIAL AND EVOLUTIONARY COMPONENTS OF THE RISK OF MODERN BIOTECHNOLOGIES Chapter 6. RISK MANAGEMENT DURING BIOTECHNOLOGICAL RESEARCH.. Chapter 7. BIOTECHNOLOGY AS A SOURCE OF SOCIAL INSTABILITY. Chapter 8. BIOTECHNOLOGY AS A SOURCE OF CIVILIZATIONAL RISK. -/- . (shrink)

1900 was a remarkable year for science. Several ground-breaking events took place, in physics, biology and psychology. Planck introduced the quantum concept, the work of Mendel was rediscovered, and Sigmund Freud published The Interpretation of Dreams . These events heralded the emergence of completely new areas of inquiry, all of which greatly affected the intellectual landscape of the 20 th century, namely quantum physics, genetics and psychoanalysis. What do these developments have in common? Can we discern a family likeness, (...) a basic affinity between them, so that we can use the one to deepen our understanding of the other? One common denominator is that they open up realms of inquiry that are significantly different from the world of everyday experience, namely the realm of elementary particles, of genes and genomes, and of the unconscious. But to what extent can we meaningfully argue, for instance, that the genome is the biological unconscious, and the unconscious the psychic genome? To address these questions, I will build on the work of two key intellectual figures who have explored the affinities of these developments in depth, namely Erwin Schrödinger (a quantum physicist and avid reader of Schopenhauer who initiated molecular biology) and Jacques Lacan (who reframed the specificity of psychoanalysis with the help of 20 th century science: the era of structural linguistics, but also of quantum physics, molecular biology, bioinformatics and DNA). (shrink)

The type-level abstraction is a formal way to represent molecular structures in biological practice. Graphical representations of molecular structures of biological objects are also used to identify functional processes of things. This paper will reveal that category theory is a formal mathematical language not only to visualize molecular structures of biological objects as type-level abstraction formally but also to understand how to infer biological functions from the molecular structures of biological objects. Category theory is a toolkit (...) to understand biological knowledge at the type-level formally, not individual token-level, as well as typical heuristic strategies in molecular biology. (shrink)

This essay considers how scholarly approaches to the development of molecular biology have too often narrowed the historical aperture to genes, overlooking the ways in which other objects and processes contributed to the molecularization of life. From structural and dynamic studies of biomolecules to cellular membranes and organelles to metabolism and nutrition, new work by historians, philosophers, and STS scholars of the life sciences has revitalized older issues, such as the relationship of life to matter, or of physicochemical (...) inquiries to biology. This scholarship points to a novel molecular vista that opens up a pluralist view of molecularizations in the twentieth century and considers their relevance to current science. (shrink)

I will relate some social and epistemic aspects involved in the conceptual development of the genetic model in the early Molecular Biology. I will use the link between Biology and Physics as a framework, which supports such assumptions, providing new methodologies, but mainly as a framework that provides a model of objectivity developed within Modern Tradition. Finally, I will interpret this relationship from the pragmatist perspective to embrace Rorty’s thesis on «the ontological priority of the social» which (...) implies understanding biology as a part of a general cultural politics. (shrink)

Today, the lipid bilayer structure is nearly ubiquitous, taken for granted in even the most rudimentary introductions to cell biology. Yet the image of the lipid bilayer, built out of lipids with heads and tails, went from having obscure origins deep in colloid chemical theory in 1924 to being “obvious to any competent physical chemist” by 1935. This chapter examines how this schematic, strictly heuristic explanation of the idea of molecular orientation was developed within colloid physical chemistry, and (...) how the image was transformed into a reflection of the reality and agency of lipid molecules in the biological microworld. Whereas in physical and colloid chemistry these images considered secondary to instrumental measurement and mathematical modeling of surface phenomena, in biology the manipulable image of the lipid on paper became an essential tool for the molecularization of the cell. (shrink)

Although it may seem like a truism to assert that biology is the science that studies organisms, during the second half of the twentieth century the organism category disappeared from biological theory. Over the past decade, however, biology has begun to witness the return of the organism as a fundamental explanatory concept. There are three major causes: (a) the realization that the Modern Synthesis does not provide a fully satisfactory understanding of evolution; (b) the growing awareness of the (...) limits of reductionism in molecular biology; and (c) the renewed interest in the nature of life as a genuine scientific problem. This essay examines these recent developments and considers the new epistemological roles being played by the organism in each of them. It also reflects on what the present resurgence of the organism means for the philosophy of biology. (shrink)

Statements about the behavior of biochemical entities (e.g., about the interaction between two proteins) abound in the literature on molecular biology and are increasingly becoming the targets of information extraction and text mining techniques. We show that an accurate analysis of the semantics of such statements reveals a number of ambiguities that have to be taken into account in the practice of biomedical ontology engineering: Such statements can not only be understood as event reporting statements, but also as (...) ascriptions of dispositions or tendencies that may or may not refer to collectives of interacting molecules or even to collectives of interaction events. (shrink)

In this paper I first offer a systematic outline of a series of conceptual novelties in the life-sciences that have favoured, over the last three decades, the emergence of a more social view of biology. I focus in particular on three areas of investigation: (1) technical changes in evolutionary literature that have provoked a rethinking of the possibility of altruism, morality and prosocial behaviours in evolution; (2) changes in neuroscience, from an understanding of the brain as an isolated data (...) processor to the ultrasocial and multiply connected social brain of contemporary neuroscience; and (3) changes in molecular biology, from the view of the gene as an autonomous master of development to the ‘reactive genome’ of the new emerging field of molecular epigenetics. In the second section I reflect on the possible implications for the social sciences of this novel biosocial terrain and argue that the postgenomic language of extended epigenetic inheritance and blurring of the nature/nurture boundaries will be as provocative for neo-Darwinism as it is for the social sciences as we have known them. Signs of a new biosocial language are emerging in several social-science disciplines and this may represent an exciting theoretical novelty for twenty-first social theory. (shrink)

I consider three explanatory strategies from recent systems biology that are driven by mathematics as much as mechanistic detail. Analysis of differential equations drives the first strategy; topological analysis of network motifs drives the second; mathematical theorems from control engineering drive the third. I also distinguish three abstraction types: aggregations, which simplify by condensing information; generalizations, which simplify by generalizing information; and structurations, which simplify by contextualizing information. Using a common explanandum as reference point—namely, the robust perfect adaptation of (...) chemotaxis in Escherichia coli—I argue that each strategy invokes a different combination of abstraction types and that each targets its abstractions to different mechanistic details. (shrink)

Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the Modeling (...) Account of Normal Function (MA). MA comprises two claims. First, normal functions are activities whose performance by the function-bearing part contributes to the self-maintenance of the whole system and, thereby, results in the continued presence of that part. Second, MA holds that models of system-level activities that are (partly) constitutive of self-maintenance are improved by including a representation of the relevant function-bearing part and by making reference to the activity/activities that part performs which contribute(s) to those system-level activities. I contrast MA with two other accounts that seek to explicate the ascription of normal functions in biology, namely, the organizational account and the selected effects account. Both struggle to extend to cancer biology. However, I offer ecumenical readings which allow them to recover some ascriptions of normal function to parts of cancers. So, though I contend that MA excels in this respect, the purpose of this paper is served if it provides materials for bridging the gap between cancer biology, philosophy of cancer, and the literature on function. (shrink)

A strong motivation for the human genome project was to relate biological features to the structure and function of small sets of genes, and ideally to individual genes. However, it is now increasingly realized that many problems require a "systems" approach emphasizing the interplay of large numbers of genes, and the involvement of complex networks of gene regulation. This implies a new emphasis on integrative, systems theoretical approaches. It may be called 'holistic' if the term is used without irrational overtones, (...) in the general sense of directing attention to integrated features of organs and organisms. In the history of biology, seemingly conflicting reductionist and holistic notions have alternated, with bottom-up as well as top-down approaches eventually contributing to the solutions of basic problems. By now, there is no doubt that biological features and phenomena are rooted in physico-chemical processes of the molecules involved; and yet, integrated systems aspects are becoming more and more relevant in developmental biology, brain and behavioural science, and socio-biology. -/- . (shrink)

Ever since the advent of molecular biology in the 1970s, mechanical models have become the dogma in the field, where a "true" understanding of any subject is equated to a mechanistic description. This has been to the detriment of the biomedical sciences, where, barring some exceptions, notable new feats of understanding have arguably not been achieved in normal and disease biology, including neurodegenerative disease and cancer pathobiology. I argue for a "mechanism-plus-X" paradigm, where mainstay elements of mechanistic (...) models such as hierarchy and correlation are combined with nomological principles such as general operative rules and generative principles. Depending on the question at hand and the nature of the inquiry, X could range from proven physical laws to speculative biological generalizations, such as the notional principle of cellular synchrony. I argue that the "mechanism-plus-X" approach should ultimately aim to move biological inquiries out of the deadlock of oft-encountered mechanistic pitfalls and reposition biology to its former capacity of illuminating fundamental truths about the world. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the (...) analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

Since Aristotle the central question in biology was the origin of organic form; a question put in the backyard by neo-Darwinism that considers organic form as a side effect of the interactions between genes and their products. On the other hand, the fashionable notion of self-organization also fails to provide a true causal explanation for organic form. For Aristotle form is both a cause and the principle of intelligibility and this coupled to the classical concepts of potentiality and actuality (...) provides a rational framework,currently embodied in the contemporary concept of ‘attractor’, for understanding the origin of organic form. (shrink)

Although molecular biology, genetics, and related special disciplines represent a large amount of empirical data, a practical method for the evaluation and overview of current knowledge is far from being realized. The main concepts and narratives in these fields have remained nearly the same for decades and the more recent empirical data concerning the role of noncoding RNAs and persistent viruses and their defectives do not fit into this scenario. A more innovative approach such as applied biocommunication theory (...) could translate empirical data into a coherent perspective on the functions within and between biological organisms and arguably lead to a sustainable integrative biology. (shrink)

Are living organisms--as Descartes argued--just machines? Or is the nature of life such that it can never be fully explained by mechanistic models? In this thought-provoking and controversial book, eminent geophysicist Walter M. Elsasser argues that the behavior of living organisms cannot be reduced to physico-chemical causality. Suggesting that molecular biology today is at the same point as Newtonian physics on the eve of the quantum revolution, Elsasser lays the foundation for a theoretical biology that points the (...) way toward a natural philosophy of organic life. Explicitly repudiating "vitalism" (the notion that the laws of nature need to be modified when applied to living organisms), Elsasser argues instead that the structural complexity of even a single living cell is "transcomputational"--that is, beyond the power of any imaginable system to compute. Beginning from this insight, Elsasser leads the reader through a step-by-step process that ultimately arrives at the conclusion that living and non-living matter are separated by "a no-man's land of irrationality." Trained in Germany as a physicist, Elsasser first pondered the implications of quantum mechanics for biology as early as 1951. The more closely he studied the inherent complexity of life, the more skeptical he became of the reductionist view of organisms as tiny machines. "An organism," he concluded, "is a source of causal chains which cannot be traced beyond a terminal point because they are lost in the unfathomable complexity of the organism." Like the physicist who works within the bounds of an unfathomable universe, Elsasser argues, the biologist must seek answers within a system that is no less unfathomable. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on (...) the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

Unlike in physics, the category of thought experiment is not very common in biology. At least there are no classic examples that are as important and as well-known as the most famous thought experiments in physics, such as Galileo’s, Maxwell’s or Einstein’s. The reasons for this are far from obvious; maybe it has to do with the fact that modern biology for the most part sees itself as a thoroughly empirical discipline that engages either in real natural history (...) or in experimenting on real organisms rather than fictive ones. While theoretical biology does exist and is recognized as part of biology, its role within biology appears to be more marginal than the role of theoretical physics within physics. It could be that this marginality of theory also affects thought experiments as sources of theoretical knowledge. Of course, none of this provides a sufficient reason for thinking that thought experiments are really unimportant in biology. It is quite possible that the common perception of this matter is wrong and that there are important theoretical considerations in biology, past or present, that deserve the title of thought experiment just as much as the standard examples from physics. Some such considerations may even be widely known and considered to be important, but were not recognized as thought experiments. In fact, as we shall see, there are reasons for thinking that what is arguably the single most important biological work ever, Charles Darwin’s On the Origin of Species, contains a number of thought experiments. There are also more recent examples both in evolutionary and non-evolutionary biology, as we will show. Part of the problem in identifying positive examples in the history of biology is the lack of agreement as to what exactly a thought experiment is. Even worse, there may not be more than a family resemblance that unifies this epistemic category. We take it that classical thought experiments show the following characteristics: They serve directly or indirectly in the non-empirical epistemic evaluation of theoretical propositions, explanations or hypotheses. Thought experiments somehow appeal to the imagination. They involve hypothetical scenarios, which may or may not be fictive. In other words, thought experiments suppose that certain states of affairs hold and then try to intuit what would happen in a world where these suppositions are true. We want to examine in the following sections if there are episodes in the history of biology that satisfy these criteria. As we will show, there are a few episodes that might satisfy all three of these criteria, and many more if the imagination criterion is dropped or understood in a lose sense. In any case, this criterion is somewhat vague in the first place, unless a specific account of the imagination is presupposed. There will also be issues as to what exactly “non-empirical” means. In general, for the sake of discussion we propose to understand the term “thought experiment” here in a broad rather than a narrow sense here. We would rather be guilty of having too wide a conception of thought experiment than of missing a whole range of really interesting examples. (shrink)

‘‘Theoretical biology’’ is a surprisingly heter- ogeneous field, partly because it encompasses ‘‘doing the- ory’’ across disciplines as diverse as molecular biology, systematics, ecology, and evolutionary biology. Moreover, it is done in a stunning variety of different ways, using anything from formal analytical models to computer sim- ulations, from graphic representations to verbal arguments. In this essay I survey a number of aspects of what it means to do theoretical biology, and how they compare with (...) the allegedly much more restricted sense of theory in the physical sciences. I also tackle a recent trend toward the presentation of all-encompassing theories in the biological sciences, from general theories of ecology to a recent attempt to provide a conceptual framework for the entire set of biological disciplines. Finally, I discuss the roles played by philosophers of science in criticizing and shap- ing biological theorizing. (shrink)

In this paper, I will reread the history of molecular genetics from a psychoanalytical angle, analysing it as a case history. Building on the developmental theories of Freud and his followers, I will distinguish four stages, namely: (1) oedipal childhood, notably the epoch of model building (1943–1953); (2) the latency period, with a focus on the development of basic skills (1953–1989); (3) adolescence, exemplified by the Human Genome Project, with its fierce conflicts, great expectations and grandiose claims (1989–2003) and (...) (4) adulthood (2003–present) during which revolutionary research areas such as molecular biology and genomics have achieved a certain level of normalcy—have evolved into a normal science. I will indicate how a psychoanalytical assessment conducted in this manner may help us to interpret and address some of the key normative issues that have been raised with regard to molecular genetics over the years, such as ‘relevance’, ‘responsible innovation’ and ‘promise management’. (shrink)

Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through (...) detailed studies of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. (shrink)

The theory of evolution, which provides the conceptual framework for all modern research in organismal biology and informs research in molecular bi- ology, has gone through several stages of expansion and refinement. Darwin and Wallace (1858) of course proposed the original idea, centering on the twin concepts of natural selection and common descent. Shortly thereafter, Wallace and August Weismann worked toward the complete elimination of any Lamarckian vestiges from the theory, leaning in particular on Weismann’s (1893) concept of (...) the separation of soma and germ, resulting in what is some- times referred to as “neo-Darwinism”. (shrink)

The problem with reductionism in biology is not the reduction, but the implicit attitude of determinism that usually accompanies it. Methodological reductionism is supported by deterministic beliefs, but making such a connection is problematic when it is based on an idea of determinism as fixed predictability. Conflating determinism with predictability gives rise to inaccurate models that overlook the dynamic complexity of our world, as well as ignore our epistemic limitations when we try to model it. Furthermore, the assumption of (...) a strictly deterministic framework is unnecessarily hindering to biology. By removing the dogma of determinism, biological methods, including reductive methods, can be expanded to include stochastic models and probabilistic interpretations. Thus, the dogma of reductionism can be saved once its ties with determinism are severed. In this paper, I analyze two problems that have faced molecular biology for the last 50 years—protein folding and cancer. Both cases demonstrate the long influence of reductionism and determinism on molecular biology, as well as how abandoning determinism has opened the door to more probabilistic and unconstrained reductive methods in biology. (shrink)

Jacques Monod (1971) argued that certain molecular processes rely critically on the property of chemical arbitrariness, which he claimed allows those processes to “transcend the laws of chemistry”. It seems natural, as some philosophers have done, to interpret this in modal terms: a biological relationship is chemically arbitrary if it is possible, within the constraints of chemical “law”, for that relationship to have been otherwise than it is. But while modality is certainly important for understanding chemical arbitrariness, understanding its (...) biological role also requires an account of the concrete causal-functional features that distinguish arbitrary from non-arbitrary phenomena. In this paper I elaborate on this under-emphasised aspect by offering a general account of these features: arbitrary relations are instantiated by mechanisms that involve molecular adapters, which causally couple two properties or processes which would otherwise be uncorrelated. Additionally, adapters work by acting as intermediate rather than cooperating causes. (shrink)

The term “Complex Systems Biology” was introduced a few years ago [Kaneko, 2006] and, although not yet of widespread use, it seems particularly well suited to indicate an approach to biology which is well rooted in complex systems science. Although broad generalizations are always dangerous, it is safe to state that mainstream biology has been largely dominated by a gene-centric view in the last decades, due to the success of molecular biology. So the one gene (...) - one trait approch, which has often proved to be effective, has been extended to cover even complex traits. This simplifying view has been appropriately criticized, and the movement called systems biology has taken off. Systems biology [Noble, 2006] emphasizes the presence of several feedback loops in biological systems, which severely limit the range of validity of explanations based upon linear causal chains (e.g. gene → behaviour). Mathematical modelling is one the favourite tools of systems biologists to analyze the possible effects of competing negative and positive feedback loops which can be observed at several levels (from molecules to organelles, cells, tissues, organs, organisms, ecosystems). Systems biology is by now a well-established field, as it can be inferred by the rapid growth in number of conferences and journals devoted to it, as well as by the existence of several grants and funded projects.Systems biology is mainly focused upon the description of specific biological items, like for example specific organisms, or specific organs in a class of animals, or specific genetic-metabolic circuits. It therefore leaves open the issue of the search for general principles of biological organization, which apply to all living beings or to at least to broad classes. We know indeed that there are some principles of this kind, biological evolution being the most famous one. The theory of cellular organization also qualifies as a general principle. But the main focus of biological research has been that of studying specific cases, with some reluctance to accept (and perhaps a limited interest for) broad generalizations. This may however change, and this is indeed the challenge of complex systems biology: looking for general principles in biological systems, in the spirit of complex systems science which searches for similar features and behaviours in various kinds of systems. The hope to find such general principles appears well founded, and I will show in Section 2 that there are indeed data which provide support to this claim. Besides data, there are also general ideas and models concerning the way in which biological systems work. The strategy, in this case, is that of introducing simplified models of biological organisms or processes, and to look for their generic properties: this term, borrowed from statistical physics, is used for those properties which are shared by a wide class of systems. In order to model these properties, the most effective approach has been so far that of using ensembles of systems, where each member can be different from another one, and to look for those properties which are widespread. This approach was introduced many years ago [Kauffman, 1971] in modelling gene regulatory networks. At that time one had very few information about the way in which the expression of a given gene affects that of other genes, apart from the fact that this influence is real and can be studied in few selected cases (like e.g. the lactose metabolism in E. coli). Today, after many years of triumphs of molecular biology, much more has been discovered, however the possibility of describing a complete map of gene-gene interactions in a moderately complex organism is still out of reach. Therefore the goal of fully describing a network of interacting genes in a real organism could not be (and still cannot be) achieved. But a different approach has proven very fruitful, that of asking what are the typical properties of such a set of interacting genes. Making some plausible hypotheses and introducing some simplifying assumptions, Kauffman was able to address some important problems. In particular, he drew attention to the fact that a dynamical system of interacting genes displays selforganizing properties which explain some key aspects of life, most notably the existence of a limited number of cellular types in every multicellular organism (these numbers are of the order of a few hundreds, while the number of theoretically possible types, absent interactions, would be much much larger than the number of protons in the universe). In section 3 I will describe the ensemble based approach in the context of gene regulatory networks, and I will show that it can describe some important experimental data. Finally, in section 4 I will discuss some methodological aspects. (shrink)

This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy of these models (...) is due to their failure to include relevant spatial and structural information in a way that does not render the model non-explanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory. (shrink)

There are two fundamentally distinct kinds of biological theorizing. "Formal biology" focuses on the relations, captured in formal laws, among mathematically abstracted properties of abstract objects. Population genetics and theoretical mathematical ecology, which are cases of formal biology, thus share methods and goals with theoretical physics. "Compositional biology," on the other hand, is concerned with articulating the concrete structure, mechanisms, and function, through developmental and evolutionary time, of material parts and wholes. Molecular genetics, biochemistry, developmental (...) class='Hi'>biology, and physiology, which are examples of compositional biology, are in serious need of philosophical attention. For example, the very concept of a "part" is understudied in both philosophy of biology and philosophy of science. ;My dissertation is an attempt to clarify the distinction between formal biology and compositional biology and, in so doing, provide a clear philosophical analysis, with case studies, of compositional biology. Given the social, economic, and medical importance of compositional biology, understanding it is urgent. For my investigation, I draw on the philosophical fields of metaphysics and epistemology, as well as philosophy of biology and philosophy of science. I suggest new ways of thinking about some classic philosophy of science issues, such as modeling, laws of nature, abstraction, explanation, and confirmation. I hint at the relevance of my study of two kinds of biological theorizing to debates concerning the disunity of science. (shrink)

I go deep into the biology of the human organism to argue that the psychological features and functions of persons are realized by cellular and molecular parallel distributed processing networks dispersed throughout the whole body. Persons supervene on the computational processes of nervous, endocrine, immune, and genetic networks. Persons do not go with brains.

The purpose of this paper is to discuss the biological plausibility of the pace of creation written in the genesis. A fascinating hypothesis is made on the central role of serotonin as a guide, as the director of the phenomena that enable the best use of light by the plant world, the growth, the regulation of mood in the complex molecular interactions that characterize the varying levels of consciousness. This hypothesis provides biological interpretations of the c.

Biomedical research is increasingly a matter of the navigation through large computerized information resources deriving from functional genomics or from the biochemistry of disease pathways. To make such navigation possible, controlled vocabularies are needed in terms of which data from different sources can be unified. One of the most influential developments in this regard is the so-called Gene Ontology, which consists of controlled vocabularies of terms used by biologists to describe cellular constituents, biological processes and molecular functions, organized into (...) hierarchies via the relation of class subsumption. Here we seek to provide a rigorous account of the logic of classification that underlies GO and similar biomedical ontologies. Drawing on Aristotle, we develop a system of axioms and definitions for the treatment of biological classes and instances. (shrink)

We characterize a type of functional explanation that addresses why a homologous trait originating deep in the evolutionary history of a group remains widespread and largely unchanged across the group’s lineages. We argue that biologists regularly provide this type of explanation when they attribute conserved functions to phenotypic and genetic traits. The concept of conserved function applies broadly to many biological domains, and we illustrate its importance using examples of molecular sequence alignments at the intersection of evolution and cell (...) biology. We use these examples to show how the study of conserved functions can integrate knowledge of a trait’s causal effects on fitness and its history of natural selection without invoking adaptation. We also show how conserved function provides a novel basis for addressing objections against evolutionary functions raised by Robert Cummins. (shrink)

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, (...) the recent introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes. (shrink)

All the central assumptions of the Modern Synthesis (Neo-Darwinism) have been disproven. [1, 2] An article with the title, "Rocking the foundations of molecular genetics,” appearing in the prestigious Proceedings of the National Academy of Sciences at the end of 2012 [3] would have not been possible a decade ago. Groundbreaking experimental evidence of epigenetic maternal inheritance over several generations was published in the same journal, throwing the whole foundation of 21st century molecular genetics into question. Neo-Darwinism attributed (...) genetic change to random events, in which physiology was assumed to play little role. "The germ line was thought to be isolated from any influence by the rest of the organism and its response to the environment. [3] The fundamental concepts that constitute the foundations of contemporary systems biology include holism, emergentism, and robustness, compared to the concepts of reductionism, mechanism, and homeostasis, that form the foundations of molecular biology. [18] Holism is to be contrasted with reductionism which considers a system as merely composed of a sum of parts. Emergentism, the appearance of hierarchical levels of organization, is contrasted with mechanism of independent linear events. Robustness refers to the preservation of the functionality of a system to a certain degree despite external or internal changes, while homeostasis refers to maintaining the stability of the state of a system. The Vedantic view also proposes viewing life from the Organic Whole perspective, in which consciousness forms the supporting basis. The conscious agent is an important part of that view, but the absolute conception of a unifying center is not to be omitted if a proper conception is to be achieved. (shrink)

To naturalize the concept of teleological causality in biology it is not enough to avoid assuming backward causation or positing the existence of an inscrutable te- leological essence like the élan vital. We must also specify how the causality of or- ganisms is distinct from the causality of designed artifacts like thermostats or asym- metrically oriented processes like the ubiquitous increase of entropy. Historically, the concept of teleological causality in biology has been based on an analogy to the (...) familiar experience of purposeful action. This is experienced by us as a disposition to achieve a general type of end that is represented in advance, and which regulates the selection of efficient means to achieve it. Inspired by this analogy, to bridge the gap between biology and human agency we describe a simple molecular pro- cess called autogenesis that shows how two linked complementary self-organizing processes can give rise to higher-order relations that resemble purposeful disposi- tions, though expressed in terms of constraints on molecular processes. Because the autogenic model is described in sufficient detail to be empirically realizable, it provides a proof of principle demonstrating a simple form of teleological causality. (shrink)

Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish between causes, not just (...) causes from noncauses. In particular, the specificity of drugs is precisely what underwrites their value as experimental interventions on biological processes. (shrink)

Symmetries play a major role in physics, in particular since the work by E. Noether and H. Weyl in the first half of last century. Herein, we briefly review their role by recalling how symmetry changes allow to conceptually move from classical to relativistic and quantum physics. We then introduce our ongoing theoretical analysis in biology and show that symmetries play a radically different role in this discipline, when compared to those in current physics. By this comparison, we stress (...) that symmetries must be understood in relation to conservation and stability properties, as represented in the theories. We posit that the dynamics of biological organisms, in their various levels of organization, are not just processes, but permanent (extended, in our terminology) critical transitions and, thus, symmetry changes. Within the limits of a relative structural stability (or interval of viability), variability is at the core of these transitions. (shrink)

While many different mechanisms contribute to the generation of spatial order in biological development, the formation of morphogenetic fields which in turn direct cell responses giving rise to pattern and form are of major importance and essential for embryogenesis and regeneration. Most likely the fields represent concentration patterns of substances produced by molecular kinetics. Short range autocatalytic activation in conjunction with longer range “lateral” inhibition or depletion effects is capable of generating such patterns (Gierer and Meinhardt, 1972). Non-linear reactions (...) are required, and mathematical criteria were derived to design molecular models capable of pattern generation. The classical embryological feature of proportion regulation can be incorporated into the models. The conditions are mathematically necessary for the simplest two-factor case, and are likely to be a fair approximation in multi-component systems in which activation and inhibition are systems parameters subsuming the action of several agents. Gradients, symmetric and periodic patterns, in one or two dimensions, stable or pulsing in time, can be generated on this basis. Our basic concept of autocatalysis in conjunction with lateral inhibition accounts for self-regulatory biological features, including the reproducible formation of structures from near-uniform initial conditions as required by the logic of the generation cycle. Real tissue form, for instance that of budding Hydra, may often be traced back to local curvature arising within an initially relatively flat cell sheet, the position of evagination being determined by morphogenetic fields. Shell theory developed for architecture may also be applied to such biological processes. (shrink)