Inferential explanations in biology

Among philosophers of science, there is now a widespread agreement that the DN model of explanation is poorly equipped to account for explanations in biology. Rather than identifying laws, so the consensus goes, researchers explain biological capacities by constructing a model of the underlying mechanism.We think that the dichotomy between DN explanations and mechanistic explanations is misleading. In this article, we argue that there are cases in which biological capacities are explained without constructing a model of the underlying mechanism. Although these explanations do not conform to Hempel’s DN model (they do not deduce the explanandum from laws of nature), they do invoke more or less stable generalisations. Because they invoke generalisations and have the form of an argument, we call them inferential explanations. We support this claim by considering two examples of explanations of biological capacities: pigeon navigation and photoperiodism. Next, we will argue that these non-mechanistic explanations are crucial to biology in three ways: (i) sometimes, they are the only thing we have (there is no alternative available), (ii) they are heuristically useful, and (iii) they provide genuine understanding and so are interesting in their own right.In the last sections we discuss the relation between types of explanations and types of experiments and situate our views within some relevant debates on explanatory power and explanatory virtues.     

Statistical interpretation of the interplay between noise and chaos in the stochastic logistic map

A recurring problem in population biology - as well as other stochastic dynamical systems in biology, the physical and social sciences - is the distinction between the ‘true’ dynamics of a system and observational noise: i.e. can we from present data reliably infer e.g. biological mechanisms, or are signals swamped by noise.Here, we approach this problem using the canonical model for simple systems that exhibit complex behaviour, the logistic map. At each time-point noise is added, which allows us to study the long-term behaviour of a system which exhibits both non-linear dynamics and intrinsic noise.We show that the interplay between deterministic non-linear dynamics and simple Gaussian noise results in a perplexingly simple system when viewed statistically.In particular we show that for the case of Gaussian noise it is possible to derive at very reliable approximations for the time until the system has reached an absorbing state. This generic model allows us, for example, to study the life-time of molecular species involved in noisy feedback loops.     

Agent-based modelling as scientific method: a case study analysing primate social behaviour

A scientific methodology in general should provide two things: first, a means of explanation and, second, a mechanism for improving that explanation. Agent-based modelling (ABM) is a method that facilitates exploring the collective effects of individual action selection. The explanatory force of the model is the extent to which an observed meta-level phenomenon can be accounted for by the behaviour of its micro-level actors. This article demonstrates that this methodology can be applied to the biological sciences; agent-based models, like any other scientific hypotheses, can be tested, critiqued, generalized or specified. We review the state of the art for ABM as a methodology for biology and then present a case study based on the most widely published agent-based model in the biological sciences: Hemelrijk's DomWorld, a model of primate social behaviour. Our analysis shows some significant discrepancies between this model and the behaviour of the macaques, the genus used for our analysis. We also demonstrate that the model is not fragile: its other results are still valid and can be extended to compensate for these problems. This robustness is a standard advantage of experiment-based artificial intelligence modelling techniques over analytic modelling.     

Photobleaching and photoactivation: following protein dynamics in living cells

Cell biology is being transformed by the use of fluorescent proteins as fusion tags to track protein behaviour in living cells. Here, we discuss the techniques of photobleaching and photoactivation, which can reveal the location and movement of proteins. Widespread applications of these fluorescent-based methods are revealing new aspects of protein dynamics and the biological processes that they regulate.     

Information and Efficiency in the Nervous System—A Synthesis

In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components—like genetic circuits, biochemical cascades, and ion channels, among others—enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode—with almost 20–60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.     

The evolution of human biosocial behaviour

The analysis of human behaviour can be approached from three different but complementary perspectives: the first includes the eternal debate about the degree of environmental or genetic determinism of social behaviour; the second, in the context of evolutionary ecology, concerns the evaluation of behavioural responses to morphological and/or environmental changes that have been the key to success in our species; the third, in the context of the analysis of the biology of the health of modern day populations, deals with the biological consequences of social behavioural changes. The secret of the success of a species resides in its capacity to respond behaviourally to the morphological and environmental changes produced during its biological history. What is unique about humans concerns our brains and their derived function: flexible behaviour. The morphological, physiological and behavioural changes that allow thecreation and maintenance of such a large brain are intimately connected to reproduction and therefore to the biosociology of women, the members of the speciesHomo sapiens in whom are combined, with notable success, a prolonged life-cycle, some anatomical and physiological traits and flexible reproductive behaviour, which together confer a decisive demographic advantage on them over other hominids. In summary, as well as contributing to the spread of this information by which women can make informed decisions based on a knowledge of causes, with the aim of optimizing our health it is necessary to consider the possibility of approaching some aspects of our biology and behaviour as patterns fixed by evolution. Three factors would be the most easily adjustable for this purpose: a) to delay the age of sexual maturation, b) to bring forward the age of first maternity, and c) to encourage breastfeeding on demand without substitutes and for a 3–5 month period.     

Synthetic Genomics and Synthetic Biology Applications Between Hopes and Concerns

New organisms and biological systems designed to satisfy human needs are among the aims of synthetic genomics and synthetic biology. Synthetic biology seeks to model and construct biological components, functions and organisms that do not exist in nature or to redesign existing biological systems to perform new functions. Synthetic genomics, on the other hand, encompasses technologies for the generation of chemically-synthesized whole genomes or larger parts of genomes, allowing to simultaneously engineer a myriad of changes to the genetic material of organisms. Engineering complex functions or new organisms in synthetic biology are thus progressively becoming dependent on and converging with synthetic genomics. While applications from both areas have been predicted to offer great benefits by making possible new drugs, renewable chemicals or clean energy, they have also given rise to concerns about new safety, environmental and socio-economic risks – stirring an increasingly polarizing debate. Here we intend to provide an overview on recent progress in biomedical and biotechnological applications of synthetic genomics and synthetic biology as well as on arguments and evidence related to their possible benefits, risks and governance implications.     

Calculating life? Duelling discourses in interdisciplinary systems biology

A high profile context in which physics and biology meet today is in the new field of systems biology. Systems biology is a fascinating subject for sociological investigation because the demands of interdisciplinary collaboration have brought epistemological issues and debates front and centre in discussions amongst systems biologists in conference settings, in publications, and in laboratory coffee rooms. One could argue that systems biologists are conducting their own philosophy of science. This paper explores the epistemic aspirations of the field by drawing on interviews with scientists working in systems biology, attendance at systems biology conferences and workshops, and visits to systems biology laboratories. It examines the discourses of systems biologists, looking at how they position their work in relation to previous types of biological inquiry, particularly molecular biology. For example, they raise the issue of reductionism to distinguish systems biology from molecular biology. This comparison with molecular biology leads to discussions about the goals and aspirations of systems biology, including epistemic commitments to quantification, rigor and predictability. Some systems biologists aspire to make biology more similar to physics and engineering by making living systems calculable, modelable and ultimately predictable-a research programme that is perhaps taken to its most extreme form in systems biology's sister discipline: synthetic biology. Other systems biologists, however, do not think that the standards of the physical sciences are the standards by which we should measure the achievements of systems biology, and doubt whether such standards will ever be applicable to 'dirty, unruly living systems'. This paper explores these epistemic tensions and reflects on their sociological dimensions and their consequences for future work in the life sciences.     

Understanding dynamics using sensitivity analysis: caveat and solution

Background  

Parametric sensitivity analysis (PSA) has become one of the most commonly used tools in computational systems biology, in which the sensitivity coefficients are used to study the parametric dependence of biological models. As many of these models describe dynamical behaviour of biological systems, the PSA has subsequently been used to elucidate important cellular processes that regulate this dynamics. However, in this paper, we show that the PSA coefficients are not suitable in inferring the mechanisms by which dynamical behaviour arises and in fact it can even lead to incorrect conclusions.     

Models in biology: form and function

The relationships between physical and biological sciences are important in science education. This is shown in the links between the structure of biological science and the use of models. Although the physical sciences contain many principles of wide application, much of biology consists of very distinct examples. When these examples are used as models of organisms or processes, misunderstanding can occur if the characteristics of the model are used to make inaccurate generalizations. In biological education, stress on the importance of unique features must continually accompany the demonstration of similarities.

Theoretical models are constructed and reconstructed by students learning science, particularly in relation to broadly applicable principles. In biology a student may build a theoretical model of a subject which is itself a model used as an example. Distinct features of biological science may influence a variety of learning situations including problem solving.     

Hierarchical semantic composition of biosimulation models using bond graphs

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.     

Morphology and behaviour: functional links in development and evolution

Development and evolution of animal behaviour and morphology are frequently addressed independently, as reflected in the dichotomy of disciplines dedicated to their study distinguishing object of study (morphology versus behaviour) and perspective (ultimate versus proximate). Although traits are known to develop and evolve semi-independently, they are matched together in development and evolution to produce a unique functional phenotype. Here I highlight similarities shared by both traits, such as the decisive role played by the environment for their ontogeny. Considering the widespread developmental and functional entanglement between both traits, many cases of adaptive evolution are better understood when proximate and ultimate explanations are integrated. A field integrating these perspectives is evolutionary developmental biology (evo-devo), which studies the developmental basis of phenotypic diversity. Ultimate aspects in evo-devo studies--which have mostly focused on morphological traits--could become more apparent when behaviour, 'the integrator of form and function', is integrated into the same framework of analysis. Integrating a trait such as behaviour at a different level in the biological hierarchy will help to better understand not only how behavioural diversity is produced, but also how levels are connected to produce functional phenotypes and how these evolve. A possible framework to accommodate and compare form and function at different levels of the biological hierarchy is outlined. At the end, some methodological issues are discussed.     

Scaling and structure of dicotyledonous leaf venation networks

There have been numerous attempts to derive general models for the structure and function of resource delivery networks in biology. Such theories typically predict the quantitative structure of vascular networks across scales. For example, fractal branching models of plant structure predict that the network dimensions within plant stems or leaves should be scale-free. However, very few empirical examples of such networks are available with which to evaluate such hypotheses. Here, we apply recently developed leaf network extraction software to a global leaf dataset. We find that leaf networks are neither entirely scale-free nor governed entirely by a characteristic scale. Indeed, we find many network properties, such as vein length distributions, which are governed by characteristic scales, and other network properties, notably vein diameter distributions, which are typified by power-law behaviour. Our findings suggest that theories of network structure will remain incomplete until they address the multiple constraints on network architecture.     

Robustness: confronting lessons from physics and biology

The term robustness is encountered in very different scientific fields, from engineering and control theory to dynamical systems to biology. The main question addressed herein is whether the notion of robustness and its correlates (stability, resilience, self‐organisation) developed in physics are relevant to biology, or whether specific extensions and novel frameworks are required to account for the robustness properties of living systems. To clarify this issue, the different meanings covered by this unique term are discussed; it is argued that they crucially depend on the kind of perturbations that a robust system should by definition withstand. Possible mechanisms underlying robust behaviours are examined, either encountered in all natural systems (symmetries, conservation laws, dynamic stability) or specific to biological systems (feedbacks and regulatory networks). Special attention is devoted to the (sometimes counterintuitive) interrelations between robustness and noise. A distinction between dynamic selection and natural selection in the establishment of a robust behaviour is underlined. It is finally argued that nested notions of robustness, relevant to different time scales and different levels of organisation, allow one to reconcile the seemingly contradictory requirements for robustness and adaptability in living systems.     

From computational quantum chemistry to computational biology: experiments and computations are (full) partners

Computations are being integrated into biological research at an increasingly fast pace. This has not only changed the way in which biological information is managed; it has also changed the way in which experiments are planned in order to obtain information from nature. Can experiments and computations be full partners? Computational chemistry has expanded over the years, proceeding from computations of a hydrogen molecule toward the challenging goal of systems biology, which attempts to handle the entire living cell. Applying theories from ab initio quantum mechanics to simplified models, the virtual worlds explored by computations provide replicas of real-world phenomena. At the same time, the virtual worlds can affect our perception of the real world. Computational biology targets a world of complex organization, for which a unified theory is unlikely to exist. A computational biology model, even if it has a clear physical or chemical basis, may not reduce to physics and chemistry. At the molecular level, computational biology and experimental biology have already been partners, mutually benefiting from each other. For the perception to become reality, computation and experiment should be united as full partners in biological research.     

Development and validation of computational models of cellular interaction

In this paper we take the view that computational models of biological systems should satisfy two conditions – they should be able to predict function at a systems biology level, and robust techniques of validation against biological models must be available. A modelling paradigm for developing a predictive computational model of cellular interaction is described, and methods of providing robust validation against biological models are explored, followed by a consideration of software issues.     

Des gènes à la culture : réflexions finales

Imagination pushes humanity well beyond its natural biological capacities. An approach of co-evolution, between molecular biology and behaviour, is thus required to explain these combined processes. Technological performances define spiritual conquests and formulas whose sole function is cultural. Humanity operates according to intentional social modes defined by the group, but these immediately become conditions for their own evolution. These choices are the motor for the history of humanity, and thus humanity can itself choose its own path. Anatomic modernisation is only a distant reflection of bipedalism, but freeing the hands leads to the development of abstract thought. The relationship between anatomy and awareness allows infinite variations in the harmonious adaptation to nature, animals and other societies. In Europe, humanity arrived in abrupt bursts because it was the result of external evolution, continuous and distant, dispersed across Asia. By its unceasing audacity, humanity combats biological determinism, imposing “moral” rules, or imperatives. The balance between biology and culture is reflected in the rules for sharing food: the biological life itself is coded by social sharing. These systems take on ternary values as soon as they include animal behaviours. Built shelters integrate societies with the cosmos because they delimit cultural spaces within natural chaos. Mastery of cognitive mechanisms gives humanity an entirely new responsibility, that of being able to define its destiny. Our united disciplines are transformed into ethical requirements.     

Technicolour transgenics: imaging tools for functional genomics in the mouse

Over the past decade, a battery of powerful tools that encompass forward and reverse genetic approaches have been developed to dissect the molecular and cellular processes that regulate development and disease. The advent of genetically-encoded fluorescent proteins that are expressed in wild type and mutant mice, together with advances in imaging technology, make it possible to study these biological processes in many dimensions. Importantly, these technologies allow direct visual access to complex events as they happen in their native environment, which provides greater insights into mammalian biology than ever before.