Biological Modeling

There is some consensus among orthodox category theorists that the concept of adjoint functors is the most important concept contributed to mathematics by category theory. We give a heterodox treatment of adjoints using heteromorphisms that parses an adjunction into two separate parts. Then these separate parts can be recombined in a new way to define a cognate concept, the brain functor, to abstractly model the functions of perception and action of a brain. The treatment uses relatively simple category theory and (...) is focused on the interpretation and application of the mathematical concepts. (shrink)

Advances in biology, at least over the past two centuries, have mostly relied on theories that were subsequently revised, expanded or eventually refuted using experimental and other means. The field of theoretical biology used to primarily provide a basis, similar to theoretical physics in the physical sciences, to rationally examine the frameworks within which biological experiments were carried out and to shed light on overlooked gaps in understanding. Today, however, theoretical biology has generally become synonymous with computational and mathematical biology. (...) This could in part be explained by a relatively recent tendency in which a "data first", rather than a "theory first", approach is preferred. Moreover, generating hypotheses has at times become procedural rather than theoretical, therefore perhaps inadvertently leading some hypotheses to become perfunctory in nature. This situation leaves our understanding enmeshed in data, which should be disentangled from much noise. Given the many unresolved questions in biology and medicine, big and small, ranging from the problem of protein folding to unifying causative frameworks of complex non-Mendelian human diseases, it seems apt to revive the role of pure theory in the biological sciences. This paper, using the current biomedical literature and historical precedents, makes the case for a "philosophical biology" (philbiology), distinct from but quite complementary to philosophy of biology (philobiology), which would entail biological investigation through philosophical approaches. Philbiology would thus be a reincarnation of theoretical biology, adopting the true sense of the word "theory" and making use of a rich tradition of serious philosophical approaches in the natural sciences. A philbiological investigation, after clearly defining a given biological problem, would aim to propose a set of empirical questions, along with a class of possible solutions, about that problem. Importantly, whether or not the questions can be tested using current experimental paradigms would be secondary to whether the questions are inherently empirical or not. These issues will be illustrated using a range of specific examples. The final goal of a philbiological investigation would be to develop a theoretical framework that can lead observational and/or interventional experimental studies of the defined problem, a framework that is structured, generative and expandable, and, crucially, one that simplifies some aspect(s) of the said problem. (shrink)

The biochemistry of geotropism in plants and gravisensing in e.g. cyanobacteria or paramacia is still not well understood today [1]. Perhaps there are more ways than one for organisms to sense gravity. The two best known relatively old explanations for gravity sensing are sensing through the redistribution of cellular starch statoliths and sensing through redistribution of auxin. The starch containing statoliths in a gravity field produce pressure on the endoplasmic reticulum of the cell. This enables the cell to sense direction. (...) Alternatively, there is the redistribution of auxin under the action of gravity. This is known as the Cholodny-Went hypothesis [2], [3]. The latter redistribution coincides with a redistribution of electrical charge in the cell. With the present study the aim is to add a mathematical unified field explanation to gravisensing. (shrink)

Disagreement about how best to think of the relation between theories and the realities they represent has a longstanding and venerable history. We take up this debate in relation to the free energy principle (FEP) - a contemporary framework in computational neuroscience, theoretical biology and the philosophy of cognitive science. The FEP is very ambitious, extending from the brain sciences to the biology of self-organisation. In this context, some find apparent discrepancies between the map (the FEP) and the territory (target (...) systems) a compelling reason to defend instrumentalism about the FEP. We take this to be misguided. We identify an important fallacy made by those defending instrumentalism about the FEP. We call it the literalist fallacy: this is the fallacy of inferring the truth of instrumentalism based on the claim that the properties of FEP models do not literally map onto real-world, target systems. We conclude that scientific realism about the FEP is a live and tenable option. (shrink)

Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, (...) we describe three different kinds of claim -- labelled mathematical, empirical and general -- routinely made by proponents of the framework, and suggest dialectical links between them. Overall, we aim to increase philosophical understanding of active inference so that it may be more readily evaluated. -/- This is a manuscript draft of the Introduction to the Topical Collection "The Free Energy Principle: From Biology to Cognition", forthcoming in Biology & Philosophy. (shrink)

Abstract: Without directly taking sides in the design/anti-design debate, this paper defends the following position: the assertion that biological evolution “is” design-free presupposes the possibility to model biological evolution in a design-free way. Certainly, there are design-free models of evolution based on cumulative selection. But “to model” is a verb denoting “modeling” as the process leading to a model. So any modeling – trivially – needs “previous human design.” Nevertheless, contrary to other scientific activities which legitimately consider models while ignoring (...) the process leading to them, evolution theory must take into account the human activity of modeling required by its corresponding models. (shrink)

A modern scientific awareness of the famous advaitic expression Brahma sat, jagat mithya, jivo brahmaiva na aparah is presented. The one ness of jiva and Brahman are explained from modern science point of view. The terms dristi, adhyasa, vivartanam, aham and idam are understood in modern scientific terms and a scientific analysis is given. -/- Further, the forward (purodhana) and reverse (tirodhana) transformation of maya as jiva, prapancham, jagat and viswam, undergoing vivartanam is understood and explained using concepts from physics (...) and electronics. The application of such an understanding to the field of bionics, the electro-chemical neural communication processes is discussed. The possible use of this insight to build software for modeling human cognition and language learning and communication processes is hinted. -/- . (shrink)

The immune system does not just fight pathogens but also engages in interactions with beneficial microbes and non-immune cells of the body to harmonize their behavior by means of cytokines, antibodies and effector cells (Dinarello, 2007; Moticka, 2015, pp. 217e226, 261e267). However, the importance of these “housekeeping” functions has not been fully appreciated (Cohen, 2000). In his new book Immunity: The Evolution of an Idea Alfred I. Tauber traces the history of fundamental ideas in immunology and refers to recent advances (...) in the field to overturn the notion that immunity is the ability of an autonomous organism to protect itself against pathogens. Indeed, as suggested by the author, the autonomy of the organism is illusive because individuals develop as holobionts: functionally integrated multispecies consortia (p. 98). Similarly, he suggests that pathogenicity also emerges from the lack of subordination of a microbe to social welfare of the holobiont rather than from context-independent properties of this organism (pp. 165, 181). Emphasizing the contextual character of the parasite and the host, Tauber redefines immunity as an active process of coordination of a cellular ecosystem, which allows this collective to navigate through challenges of the environment and to face demands of the communal living (pp. 10e16). From this point of view, the failure to modulate intercellular interactions by the immune system entails mitigating immunity and impairing resistance to infection and disease (pp. 103e104). Immunity is a groundbreaking work establishing a new framework for understanding the function and activity of the immune system. Its value also lies in the fact that it serves as an important stepping-stone towards an integrated philosophical, ecological and immunological study of our embedment in the microbial world. (shrink)

In aggressive interactions, animals often use a discrete set of signals, while the properties being signalled are likely to be continuous, for example fighting ability or value of victory. Here we investigate a particular model of fighting which allows for conventional signalling of subjective resource value to occur. The result shows that neither perfect nor no signalling are evolutionarily stable strategies in the model. Instead, we find ESSs in which partial information is communicated, with discrete displays signalling a range of (...) values rather than a precise one. The result also indicates that communication should be more precise in conflicts over small resources. Signalling strategies can exist in fighting because of the common interest in avoiding injuries, but communication is likely to be limited because of the fundamental conflict over the resource. Our results reflect a compromise between these two factors. Data allowing for a thorough test of the model are lacking; however, existing data seem consistent with the obtained theoretical results. (shrink)

We challenge Bruineberg et al's view that Markov blankets are illicitly reified when used to describe organismic boundaries. We do this both on general methodological grounds, where we appeal to a form of structural realism derived from Bayesian cognitive science to dissolve the problem, and by rebutting specific arguments in the target article.

Over the last two decades, network-focused approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While the network approach continues to grow very rapidly, some of its 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. Consequently, the central focus of this theme issue will be on (...) the definition, motivation and application of key concepts in biological network science, such as levels, hierarchies, and explanatory directionality. A unification will be achieved by formulating norms for delimiting the distinctively network-topological class of explanations that connect general as well as very specific biological research questions. The impact of this theme issue is broad and encompassing, as it is highly interdisciplinary and opens a uniquely normative perspective on the foundational aspects of network-based explanations and modelling. This theme issue is intended to become a landmark publication for practical research as well as funding policy decisions, by unifying network approaches in biological sciences in terms of fundamental concepts and informing the public understanding of the network science. (shrink)

Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...) I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation. (shrink)

Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...) the merits of a CBN representation of mechanisms. (shrink)

We address the need for a model by considering two competing theories regarding the origin of life: (i) the Metabolism First theory, and (ii) the RNA World theory. We discuss two interrelated points, namely: (i) Models are valuable tools for understanding both the processes and intricacies of origin-of-life issues, and (ii) Insights from models also help us to evaluate the core objection to origin-of-life theories, called “the inefficiency objection”, which is commonly raised by proponents of both the Metabolism First theory (...) and the RNA World theory against each other. We use Simpson’s Paradox (SP) as a tool for challenging this objection. We will use models in various senses, ranging from taking them as representations of reality to treating them as theories/accounts that provide heuristics for probing reality. In this paper, we will frequently use models and theories interchangeably. Additionally, we investigate Conway’s Game of Life and contrast it with our SP-based approach to emergence-of-life issues. Finally, we discuss some of the consequences of our view. A scientific model is testable in three senses: (i) a logical sense, (ii) a nomological sense, and (iii) a current technological sense. The SP-based model is testable in the first two senses but it is not feasible to test it using current technology. (shrink)

Endocrinologists apply the idea of feedback loops to explain how hormones regulate certain bodily functions such as glucose metabolism. In particular, feedback loops focus on the maintenance of the plasma concentrations of glucose within a narrow range. Here, we put forward a different, organicist perspective on the endocrine regulation of glycaemia, by relying on the pivotal concept of closure of constraints. From this perspective, biological systems are understood as organized ones, which means that they are constituted of a set of (...) mutually dependent functional structures acting as constraints, whose maintenance depends on their reciprocal interactions. Closure refers specifically to the mutual dependence among functional constraints in an organism. We show that, when compared to feedback loops, organizational closure can generate much richer descriptions of the processes and constraints at play in the metabolism and regulation of glycaemia, by making explicit the different hierarchical orders involved. We expect that the proposed theoretical framework will open the way to the construction of original mathematical models, which would provide a better understanding of endocrine regulation from an organicist perspective. (shrink)

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in (...) this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined. (shrink)

Starting from a philosophical perspective, which states that the living structures are actually a combination between matter and information, this article presents the results on an analysis of the bipolar information-matter structure of the human organism, distinguishing three fundamental circuits for its survival, which demonstrates and supports this statement, as a base for further development of the informational model of consciousness to a general informational model of the human organism. For this, it was examined the Informational System of the Human (...) Body and its components from the perspective of the physics/information/neurosciences concepts, showing specific functions of each of them, highlighting the correspondence of these centers with brain support areas and with their projections in consciousness, which are: Center of Acquisition and Storing of Information (CASI) reflected in consciousness as memory, Center of Decision and Command (CDC) (decision), Info-Emotional Center (IES) (emotions), Maintenance Informational System (MIS) (personal status), Genetic Transmission System (GTS) (associativity/genetic transmission) and Info Genetic Generator (IGG) related by the body development and inherited behaviors. The Info Connection (IC), detected in consciousness as trust and confidence can explain the Near-Death Experiences (NDEs) and associated phenomena. This connection is antientropic and informational, because from the multitude of uncertain possibilities is selected a certain one, helping/supporting the survival and life. The human body appears therefore as a bipolar structure, connected to two poles: information and matter. It is argued that the survival, which is the main objective of the organism, is complied in three main ways, by means of: (i) the reactive operation for adaptation by attitude; (ii) the info-genetic integration of information by epigenetic processes and genetic transmission of information for species survival, both circuits (i) and (ii) being associated to the information pole; (iii) maintenance of the material body (defined as informed matter) and its functions, associated to the matter pole of the organism. It results therefore that the informational system of the human body is supported by seven informational circuits formed by the neuro-connections between the specific zones of the brain corresponding to the informational subsystems, the cognitive centers, the sensors, transducers and execution (motor/mobile) elements. The fundamental informational circuits assuring the survival are the reactive circuit, expressible by attitude, the epigenetic/genetic circuit, absorbing and codifying information to be transmitted to the next generations, and the metabolic circuit, connected to matter (matter pole). The presented analysis allows to extend the informational modeling of consciousness to an Informational Model of Consciousness and Organism, fully describing the composition/functions of the organism in terms of information/matter and neurosciences concept. (shrink)

Biological order provided by α-helical secondary protein structures is an important resource exploitable by living organisms for increasing the efficiency of energy transport. In particular, self-trapping of amide I energy quanta by the induced phonon deformation of the hydrogen-bonded lattice of peptide groups is capable of generating either pinned or moving solitary waves following the Davydov quasiparticle/soliton model. The effect of applied in-phase Gaussian pulses of amide I energy, however, was found to be strongly dependent on the site of application. (...) Moving solitons were only launched when the amide I energy was applied at one of the α-helix ends, whereas pinned solitons were produced in the α-helix interior. In this paper, we describe a general mechanism that launches moving solitons in the interior of the α-helix through phase-modulated Gaussian pulses of amide I energy. We also compare the predicted soliton velocity based on effective soliton mass and the observed soliton velocity in computer simulations for different parameter values of the isotropy of the exciton-phonon interaction. The presented results demonstrate the capacity for explicit control of soliton velocity in protein α-helices, and further support the plausibility of gradual optimization of quantum dynamics for achieving specialized protein functions through natural selection. (shrink)

The essential biological processes that sustain life are catalyzed by protein nano-engines, which maintain living systems in far-from-equilibrium ordered states. To investigate energetic processes in proteins, we have analyzed the system of generalized Davydov equations that govern the quantum dynamics of multiple amide I exciton quanta propagating along the hydrogen-bonded peptide groups in α-helices. Computational simulations have confirmed the generation of moving Davydov solitons by applied pulses of amide I energy for protein α-helices of varying length. The stability and mobility (...) of these solitons depended on the uniformity of dipole-dipole coupling between amide I oscillators, and the isotropy of the exciton-phonon interaction. Davydov solitons were also able to quantum tunnel through massive barriers, or to quantum interfere at collision sites. The results presented here support a nontrivial role of quantum effects in biological systems that lies beyond the mechanistic support of covalent bonds as binding agents of macromolecular structures. Quantum tunneling and interference of Davydov solitons provide catalytically active macromolecular protein complexes with a physical mechanism allowing highly efficient transport, delivery, and utilization of free energy, besides the evolutionary mandate of biological order that supports the existence of such genuine quantum phenomena, and may indeed demarcate the quantum boundaries of life. (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)

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for countering (...) heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

<2020 Buddhist Book Award (2nd place), Korea> <2020 Sejong Book Award, Korea> The book discusses and provides solutions for the philosophical issues of Aristotelian essentialist biology, Darwin’s evolutionary theory, and contemporary molecular biology in light of Buddhist concepts of dependent arising and emptiness. CONTENT 1. Buddhist Teachings from the perspective of Bioscience 2. Bioscience from the Perspective of Buddhist Teachings 3. Enlightenment, Compassion and Bioscientific Phenomena 4. Enlightenment, Revolutionary Change of Worldview 5. The Buddhist Understanding of Development in Biology 1 (...) 6. The Buddhist Understanding of Development in Biology 2 7. The Buddhist Understanding of Development 1 8. The Buddhist Understanding of Development 2 9. The Buddhist Understanding of Aging 10. The Buddhist Understanding of Sickness 11. The Buddhist Understanding of Death 1 12. The Buddhist Understanding of Death 2 13. The Buddhist Understanding of Species 1 14. The Buddhist Understanding of Species 2 15. The Concept of Gene and its History 16. Genetic Dualism and Buddhist Criticism 17. The Buddhist Understanding of the Gene 1 18. The Buddhist Understanding of the Gene 2 19. Evolution is not Progress but Changes 20. Evolution is neither Deterministic nor Indeterministic. It is the Process of Interdependent Arising 21. Reconsidering Chance and Necessity 22. What Darwin Left Us 23. The Evolution of Buddhism 1 24. The Evolution of Buddhism 2 . (shrink)

Among those speaking in the name of materialism, whether speculative, dialectical, or "new," it is commonplace to dismiss with a single gesture a vast field of theoretical and philosophical endeavor, indicated as the last 50 or 250 years of theory and philosophy. Self-styled "speculative" writers who would surpass all philosophy since Kant, and various New Materialists who sequester decades of thought under the heading of "constructivism," manufacture the avant-garde status of their own work by claiming to delineate a simple break (...) with the past. We would be at a loss, and perhaps at risk of missing the point, if we sought to uncover the common essence underlying this "new" work, either by looking for an agreement... (shrink)

Neutral Theory is controversial in ecology. Ecologists and philosophers have diagnosed the source of the controversy as: its false assumption that individuals in different species within the same trophic level are ecologically equivalent, its conflict with Competition Theory and the adaptation of species, its role as a null hypothesis, and as a Lakatosian research programme. In this paper, I show why we should instead understand the conflict at the level of research programs which involve more than theory. The Neutralist and (...) Competitionist research programs borrow and construct theories, models, and experiments for various aims and given their home ecological systems. I present a holistic and pragmatic view of the controversy that foregrounds the interrelation between many kinds of practices and decisions in ecological research. (shrink)

Contemporary philosophy of science has seen a growing trend towards a focus on scientific practice over the epistemic outputs that such practices produce. This practice-oriented approach has yielded a clearer understanding of how reductive research strategies play a central role in contemporary scientific inquiry. In parallel, a growing body of work has sought to explore the role of non-reductive, or systems-level, research strategies. As a result, the relationship between reductive and non-reductive scientific practices is becoming of increased importance. In this (...) paper, I provide a framework within which research strategies can be compared. I argue that no strategy is reductive or non-reductive simpliciter, rather strategies are more, or are less, reductive than one another according to a frame of reference. That frame of reference is provided by a continuum of possible ways in which the target system might be conceptualised. I illustrate the utility of the framework by deploying it to analyse a recent debate in cancer research. When set within the framework, a prominent reductive strategy—the somatic mutation theory—and a prominent non-reductive strategy—the tissue organisational field theory—do not stand opposed to one another. Rather, they serve as boundary markers to chart the territory of approaches to carcinogenesis within which most strategies in the field fall. (shrink)

The fracture in the emerging discipline of biosemiotics when the code biologist Marcello Barbieri claimed that Peircian biosemiotics is not genuine science raises anew the question: What is science? When it comes to radically new approaches in science, there is no simple answer to this question, because if successful, these new approaches change what is understood to be science. This is what Galileo, Darwin and Einstein did to science, and with quantum theory, opposing interpretations are not merely about what theory (...) is right, but what is real science. Peirce's work, as he acknowledged, is really a continuation of efforts of Schelling to challenge the heritage of Newtonian science for the very good reason that the deep assumptions of Newtonian science had made sentient life, human consciousness and free will unintelligible, the condition for there being science. Pointing out the need for such a revolution in science has not succeeded as a defence of Peircian biosemiotics, however. In this paper, I will defend the scientific credentials of Peircian biosemiotics by relating it to the theoretical biology of the bio-mathematician, Robert Rosen. Rosen's relational biology, focusing on anticipatory systems and giving a place to final causes, should also be seen as a rigorous development of the Schellingian project to conceive nature in such a way that the emergence of sentient life, mind and science are intelligible. Rosen has made a very strong case for the characterization of his ideas as a real advance not only in science, but in how science should be understood, and I will argue that it is possible to provide a strong defence of Peircian biosemiotics as science through Rosen's defence of relational biology. In the process, I will show how biosemiotics can and should become a crucial component of anticipatory systems theory. (shrink)

There is considerable correspondence between theories and models used in biology and the social sciences. One type of model that is in use in both biology and the social sciences is the fitness landscape model. The properties of the fitness landscape model have been applied rather freely in the social domain. This is partly due to the versatility of the model, but it is also due to the difficulties of transferring a model to another domain. We will demonstrate that in (...) order to transfer the biological fitness landscape model to the social science it needs to be substantially modified. We argue that the syntactic structure of the model can remain unaltered, whilst the semantic dimension requires considerable modification in order to fit the specific phenomena in the social sciences. We will first discuss the origin as well as the basic properties of the model. Subsequently, we will demonstrate the considerations and modifications pertaining to such a transfer by showing how and why we altered the model to analyse collective decision-making processes. We will demonstrate that the properties of the target domain allow for a transfer of the syntactic structure but don’t tolerate the semantic transfer. (shrink)

We provide a new perspective on the relation between the space of description of an object and the appearance of novelties. One of the aims of this perspective is to facilitate the interaction between mathematics and historical sciences. The definition of novelties is paradoxical: if one can define in advance the possibles, then they are not genuinely new. By analyzing the situation in set theory, we show that defining generic (i.e., shared) and specific (i.e., individual) properties of elements of a (...) set are radically different notions. As a result, generic and specific definitions of possibilities cannot be conflated. We argue that genuinely stating possibilities requires that their meaning has to be made explicit. For example, in physics, properties playing theoretical roles are generic; then, generic reasoning is sufficient to define possibilities. By contrast, in music, we argue that specific properties matter, and generic definitions become insufficient. Then, the notion of new possibilities becomes relevant and irreducible. In biology, among other examples, the generic definition of the space of DNA sequences is insufficient to state phenotypic possibilities even if we assume complete genetic determinism. The generic properties of this space are relevant for sequencing or DNA duplication, but they are inadequate to understand phenotypes. We develop a strong concept of biological novelties which justifies the notion of new possibilities and is more robust than the notion of changing description spaces. These biological novelties are not generic outcomes from an initial situation. They are specific and this specificity is associated with biological functions, that is to say, with a specific causal structure. Thus, we think that in contrast with physics, the concept of new possibilities is necessary for biology. (shrink)

The role of genetic relatedness in social evolution has recently come under critical attention. These arguments are here critically analyzed, both theoretically and empirically. It is argued that when the conceptual structure of the theory of natural selection is carefully taken into account, genetic relatedness can be seen to play an indispensable role in the evolution of both facultative and advanced eusociality. Although reviewing the empirical evidence concerning the evolution of eusociality reveals that relatedness does not play a role in (...) the initial appearance of helper phenotypes, this follows simply from the fact that natural selection – of which relatedness is a necessary component – does not play a causal role in the origin of any traits. Further, separating two logically distinct elements of causal explanation – necessity and sufficiency – explains why the debate lingers on: although relatedness plays a necessary role in the evolution of helping and advanced eusociality, relatedness alone is not sufficient for their appearance. Therefore, if the relatedness variable in a given data set is held at a uniformly high value, then it indeed may turn out that other factors occupy a more prominent role. However, this does not change the fact that high relatedness functions as a necessary background condition for the evolution of advanced eusociality. (shrink)

The aim of this article is to show how a tumor can modify energy substrates fluxes in the brain to support its own growth. To address this question we use a modeling approach to explain brain nutrient kinetics. In particular we set up a system of 17 equations for oxygen, lactate, glucose concentrations and cells number in the brain. We prove the existence and uniqueness of nonnegative solutions and give bounds on the solutions. We also provide numerical simulations.

Cancer is a paradigmatic case of a complex causal process; causes of cancer operate at a variety of temporal and spatial scales, and the respects in which these causes act and interact are diverse. There are, for instance, temporal order effects, organizational effects, structural effects, and dynamic relationships between causes operating at different temporal and spatial scales. Because of this complexity, models of cancer initiation and progression often involve deliberate choices to focus on one time scale, one causal pathway, or (...) one aspect of cancer’s dynamics. As in most of biology, modeling cancer involves simplification and idealization. At the same time, theoretical perspectives inform the construction of these models. Such perspectives might involve viewing cancer ‘as’ a genetic disease, metabolic disease, stem cell disease, an infectious disease, or a disease of tissue disorganization. This paper will argue that purportedly competing theoretical views of cancer are not at odds, but can be viewed as mutually informative. Models are most often developed in the service of asking very specific questions, and this requires limiting our view of the phenomena to a specific temporal or spatial scale, a particular cause, or a particular outcome, dynamic, or pattern. Thus, while some models may seem at odds, often they are simply concerned with different questions, or, they are complementary and mutually informative. I hope to bring this case to bear on debates among philosophers of science over perspectivism and realism, as well pluralism about the aims and scope of scientific theory. (shrink)

The aim of this study is to provide a feedback control, called the Chemotherapy Protocol Law, with the purpose to keep the density of tumor cells that are treated by chemotherapy below a “tolerance level” L_c, while retaining the density of normal cells above a “healthy level” N_c. The mathematical model is a controlled dynamical system involving three nonlinear differential equations, based on a Gompertzian law of cell growth. By evoking viability and set-valued theories, we derive sufficient conditions for the (...) existence of a Chemotherapy Protocol Law. Thereafter, on a suitable viability domain, we build a multifunction whose selections are the required Chemotherapy Protocol Laws. Finally, we propose a design of selection that generates a Chemotherapy Protocol Law. (shrink)

Scientific models are used to predict and understand the target phenomena in the reality. The kind of epistemic relationship between the model and the reality is always regarded by most of the philosophers as a representational one. I argue that, complementary to this representational role, some of the scientific models have a constructive role to play in altering and reconstructing the reality in a physical way. I hold that the idealized model assumptions and elements bestow the constructive force of a (...) model on the reality. By recognizing the physical constructive force of some scientific models, the merit of these models could be judged by how successful they are in the reality construction, rather than by the traditional criterion of model-world representation. (shrink)

Optimality models are widely used in different parts of biology. Two important questions that have been asked about such models are: are they explanatory and, if so, what type of explanations do they offer? My concern in this paper is with the approach of Rice (2012, 2015) and Irvine (2015), who claim that these models provide non-causal explanations. I argue that there are serious problems with this approach and with the accounts of explanation it is intended to justify. The idea (...) behind this undertaking is to draw attention to an important issue associated with the recent pluralist stance on explanation: the rampant proliferation of theories of explanation. This proliferation supports a pluralist perspective on explanation, and pluralism encourages such a proliferation. But, if we are not careful about how we arrive at and how we justify new accounts of explanation — i.e., if we do not try to avoid the sort of problems discussed in this paper — we may end up trivializing the concept of explanation. (shrink)

In this paper, we develop a 3D-individual-based model to understand effect of various small-scale mechanisms in phytoplankton cells, on the cellular aggregation process. These mechanisms are: spatial interactions between cells due to their chemosensory abilities, a molecular diffusion and a demographical process. The latter is considered as a branching process with a density-dependent death rate to take into account the local competition on resources. We implement the IBM and simulate various scenarios under real parameter values for phytoplankton cells. To quantify (...) the effects of the different processes quoted above on the spatial and temporal distribution of phytoplankton, we used two spatial statistics: the Clark–Evans index and the group belonging percentage. Our simulation study highlights the role of the branching process with a weak-to-medium competition in reinforcing the aggregating structure that forms from attraction mechanisms, and shows by contrast that aggregations cannot form when competition is high. (shrink)

Within the last decade, several approaches using quaternion numbers to handle and model multiband images in a holistic manner were introduced. The quaternion Fourier transform can be efficiently used to model texture in multidimensional data such as color images. For practical application, multispectral satellite data appear as a primary source for measuring past trends and monitoring changes in forest carbon stocks. In this work, we propose a texture-color descriptor based on the quaternion Fourier transform to extract relevant information from multiband (...) satellite images. We propose a new multiband image texture model extraction, called FOTO++, in order to address biomass estimation issues. The first stage consists in removing noise from the multispectral data while preserving the edges of canopies. Afterward, color texture descriptors are extracted thanks to a discrete form of the quaternion Fourier transform, and finally the support vector regression method is used to deduce biomass estimation from texture indices. Our texture features are modeled using a vector composed with the radial spectrum coming from the amplitude of the quaternion Fourier transform. We conduct several experiments in order to study the sensitivity of our model to acquisition parameters. We also assess its performance both on synthetic images and on real multispectral images of Cameroonian forest. The results show that our model is more robust to acquisition parameters than the classical Fourier Texture Ordination model. Our scheme is also more accurate for aboveground biomass estimation. We stress that a similar methodology could be implemented using quaternion wavelets. These results highlight the potential of the quaternion-based approach to study multispectral satellite images. (shrink)

The aim of the book is to explore common concerns regarding methodological individualism in different fields of the life sciences broadly construed. It will address conceptual problems regarding individuals and their relation and dependence on the collectivities they are part of and consider innovative new viewpoints, grounded in specific scientific projects that question the present descriptions and understanding and raise challenges. A wide variety of recent, influential contributions in the life sciences utilize notions of collectivity, sociality, rich interactions and emergent (...) phenomena, as essential explanatory tools to handle numerous persistent scientific questions in the life sciences. -/- Examples range from gene expression and regulation, the dynamics of bacterial populations, the ecological developmental biology of symbiosis, levels of selection, to communication, motion and patterns of behavior in diverse life forms, the role of social institutions and cultural dynamics in human evolution, and the psychological basis for social norms. The book is being structured so that fruitful and rewarding disciplinary interactions take place among researchers and scholars who have been concerned with overcoming methodological individualism. -/- The books includes a section of four papers on holobionts and the hologenome theory and a special memorial article on the work of the late Prof. Eshel Ben-Jacob authored by Herbert Levine. (shrink)

Synthetic biology offers a powerful method to design and construct biological devices for human purposes. Two prominent design methodologies are currently used. Rational design adapts the design methodology of traditional engineering sciences, such as mechanical engineering. Directed evolution, in contrast, models its design principles after natural evolution, as it attempts to design and improve systems by guiding them to evolve in a certain direction. Previous work has argued that the primary difference between these two is the way they treat variation: (...) rational design attempts to suppress it, whilst direct evolution utilizes variation. I argue that this contrast is too simplistic, as it fails to distinguish different types of variation and different phases of design in synthetic biology. I outline three types of variation and show how they influence the construction of synthetic biological systems during the design process. Viewing the two design approaches with these more fine-grained distinctions provides a better understanding of the methodological differences and respective benefits of rational design and directed evolution, and clarifies the constraints and choices that the different design approaches must deal with. (shrink)

ABSTRACT There is a new argument form within theoretical biology. This form takes as input competing explanatory models; it yields as output the conclusion that one of these models is more plausible than the others. The driving force for this argument form is an analysis showing that one model exhibits more parametric robustness than its competitors. This article examines these inferences to the more robust explanation, analysing them as variants of inference to the best explanation. The article defines parametric robustness (...) and distinguishes it from more familiar kinds of robustness. The article also argues that parametric robustness is an explanatory virtue not subsumed by more familiar explanatory virtues, and that the plausibility verdicts in the conclusions of inferences to the more robust explanations are best interpreted as guidance for research activity, rather than claims about likely truth. 1Introducing Inference to the More Robust Explanation 2Inference to the More Robust Explanation in the Study of Apoptosis 2.1Regulating apoptosis 2.2Competing models and evidential indecision 2.3Measuring robustness 2.4Robustness as a guide to plausibility 2.5Varieties of robustness 3Inference to the More Robust Explanation as Inference to the Best Explanation 3.1The structure of inference to the best explanation 3.2Parametric robustness as an explanatory virtue rather than an explanandum 3.3Relation of parametric robustness to other explanatory virtues 4Epistemological Significance of Inference to the More Robust Explanation 4.1Plausibility in practice 4.2Plausibility in principle 5Conclusion. (shrink)

A mathematical model for Vibrio Cholerae (V. Cholerae) in a closed environment is considered, with the aim of investigating the impact of climatic factors which exerts a direct influence on the bacterial metabolism and on the bacterial reservoir capacity. We first propose a V. Cholerae mathematical model in a closed environment. A sensitivity analysis using the eFast method was performed to show the most important parameters of the model. After, we extend this V. cholerae model by taking account climatic factors (...) that influence the bacterial reservoir capacity. We present the theoretical analysis of the model. More precisely, we compute equilibria and study their stabilities. The stability of equilibria was investigated using the theory of periodic cooperative systems with a concave nonlinearity. Theoretical results are supported by numerical simulations which further suggest the necessity to implement sanitation campaigns of aquatic environments by using suitable products against the bacteria during the periods of growth of aquatic reservoirs. (shrink)

The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods to predict (...) the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence. -/- . (shrink)

Simple regression (regression analysis with a single explanatory variable), and multiple regression (regression models with multiple explanatory variables), typically correspond to very different biological questions. The former use regression lines to describe univariate associations. The latter describe the partial, or direct, effects of multiple variables, conditioned on one another. We suspect that the superficial similarity of simple and multiple regression leads to confusion in their interpretation. A clear understanding of these methods is essential, as they underlie a large range of (...) procedures in common use in biology. Beyond simple and multiple regression in their most basic forms, understanding the key principles of these procedures is critical to understanding, and properly applying, many methods, such as mixed models, generalised models, and causal inference using graphs (including path analysis and its extensions). A simple, but careful, look at the distinction between these two analyses is valuable in its own right, and can also be used to clarify widely-held misconceptions about collinearity (correlations among explanatory variables). There is no general sense in which collinearity is a problem. We suspect that the perception of collinearity as a hindrance to analysis stems from misconceptions about interpretation of multiple regression models, and so we pursue discussions about these misconceptions in this light. In particular, collinearity causes multiple regression coefficients to be less precisely estimated than corresponding simple regression coefficients. This should not be interpreted as a problem, as it is perfectly natural that direct effects should be harder to characterise than univariate associations. Purported solutions to the perceived problems of collinearity are detrimental to most biological analyses. (shrink)

Busseola fusca is a maize and sorghum pest that can cause significant damage to both crops. Given that maize is one of the main cereals grown in the worldwide, this pest is a major challenge for maize production and therefore for the economies of several countries . In this paper , based on the life cycle of B. fusca, we propose a mathematical model to study the population dynamics of this insect pest . A sensitivity analysis using the eFast method (...) was performed to show the most important parameters of the model. We present the theoretical analysis of the model. More precisely, we derive a threshold parameter 0 , called basic offspring number and show that the trivial equilibrium is globally asymptotically stable whenever 0≤1 , while if 0>1 , the non trivial equilibrium is globally asymptotically stable. The theoretical results are supported by numerical simulations. (shrink)

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...) is a distinction between aspects of the same model. In molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analysing the discovery and characterization of restriction–modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in the cognitive disposition of the modeller towards the model: a modeller will look at a model as a ‘model of’ if interested in its explanatory force, or as a ‘model for’ if interested in the material manipulations it can possibly afford. (shrink)

Holobionts, consisting of a host and diverse microbial symbionts, function as distinct biological entities anatomically, metabolically, immunologically, and developmentally. Symbionts can be transmitted from parent to offspring by a variety of vertical and horizontal methods. Holobionts can be considered levels of selection in evolution because they are well-defined interactors, replicators/reproducers, and manifestors of adaptation. An initial mathematical model is presented to help understand how holobionts evolve. The model offered combines the processes of horizontal symbiont transfer, within-host symbiont proliferation, vertical symbiont (...) transmission, and holobiont selection. The model offers equations for the population dynamics and evolution of holobionts whose hologenomes differ in gene copy number, not in allelic or loci identity. The model may readily be extended to include variation among holobionts in the gene identities of both symbionts and host. (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)

This paper deals with designing a harvesting control strategy for a predator–prey dynamical system, with parametric uncertainties and exogenous disturbances. A feedback control law for the harvesting rate of the predator is formulated such that the population dynamics is asymptotically stabilized at a positive operating point, while maintaining a positive, steady state harvesting rate. The hierarchical block strict feedback structure of the dynamics is exploited in designing a backstepping control law, based on Lyapunov theory. In order to account for unknown (...) parameters, an adaptive control strategy has been proposed in which the control law depends on an adaptive variable which tracks the unknown parameter. Further, a switching component has been incorporated to robustify the control performance against bounded disturbances. Proofs have been provided to show that the proposed adaptive control strategy ensures asymptotic stability of the dynamics at a desired operating point, as well as exact parameter learning in the disturbance-free case and learning with bounded error in the disturbance prone case. The dynamics, with uncertainty in the death rate of the predator, subjected to a bounded disturbance has been simulated with the proposed control strategy. (shrink)

This computational study generates a hypothesis for the coagulation protein whose initial concentration greatly influences the course of coagulation. Many clinical malignancies of blood coagulation arise due to abnormal initial concentrations of coagulation factors. Sensitivity analysis of mechanistic models of blood coagulation is a convenient method to assess the effect of such abnormalities. Accordingly, the study presents sensitivity analysis, with respect to initial concentrations, of a recently developed mechanistic model of blood coagulation. Both the model and parameters to which model (...) sensitivity is being analyzed provide newer insights into blood coagulation: the model incorporates distinct equations for plasma-phase and platelet membrane-bound species, and sensitivity to initial concentrations is a new dimension in sensitivity analysis. The results show that model predictions are most uncertain with respect to changes in initial concentration of factor VIII, and this hypothesis is supported by results from other models developed independently. (shrink)