Biophysics and systems biology

Mathematical Modeling in the Study of Organisms and Their Parts

Mathematical modeling is a very powerful tool to understand natural phenomena. Such a tool carries its own assumptions and should always be used critically. In this chapter we highlight the key ingredients and steps of modeling and focus on their biological interpretation. Particularly, we discuss the role of theoretical principles in writing models. We also highlight the meaning and interpretation of equations. The main aim of this chapter is to facilitate the interaction between biologists and mathematical modelers. We focus on the case of cell proliferation and motility in the context of multicellular organisms.

Fractal Analysis in Clinical Neurosciences: An Overview

Over the last years, fractals have entered into the realms of clinical neurosciences. The whole brain and its components (i.e., neurons and astrocytes) have been studied as fractal objects, and even more relevant, the fractal-based quantification of the geometrical complexity of histopathological and neuroradiological images as well as neurophysiopathological time series has suggested the existence of a gradient in the pattern representation of neurological diseases. Computational fractal-based parameters have been suggested as potential diagnostic and prognostic biomarkers in different brain diseases, including brain tumors, neurodegeneration, epilepsy, demyelinating diseases, cerebrovascular malformations, and psychiatric disorders as well. This chapter and the entire third section of this book are focused on practical applications of computational fractal-based analysis into the clinical neurosciences, namely, neurology and neuropsychiatry, neuroradiology and neurosurgery, neuropathology, neuro-oncology and neurorehabilitation, neuro-ophthalmology, and cognitive neurosciences, with special emphasis on the translation of the fractal dimension and other fractal parameters as clinical biomarkers useful from bench to bedside.

Phenomics as an approach to Comparative Developmental Physiology

The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.

Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot-and-mouth disease dissemination

Introduction

Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed.

Methods

To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination?

Results

Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties-including data circularity-were demonstrated. The emergent patterns helped identify 11 counties as 'disseminators' or 'facilitators' (F) and 264 counties as 'barriers' (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur.

Discussion

Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.

Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool

A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as "tumor reversion". However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.

Biology, Buddhism, and AI: Care as the Driver of Intelligence

Intelligence is a central feature of human beings' primary and interpersonal experience. Understanding how intelligence originated and scaled during evolution is a key challenge for modern biology. Some of the most important approaches to understanding intelligence are the ongoing efforts to build new intelligences in computer science (AI) and bioengineering. However, progress has been stymied by a lack of multidisciplinary consensus on what is central about intelligence regardless of the details of its material composition or origin (evolved vs. engineered). We show that Buddhist concepts offer a unique perspective and facilitate a consilience of biology, cognitive science, and computer science toward understanding intelligence in truly diverse embodiments. In coming decades, chimeric and bioengineering technologies will produce a wide variety of novel beings that look nothing like familiar natural life forms; how shall we gauge their moral responsibility and our own moral obligations toward them, without the familiar touchstones of standard evolved forms as comparison? Such decisions cannot be based on what the agent is made of or how much design vs. natural evolution was involved in their origin. We propose that the scope of our potential relationship with, and so also our moral duty toward, any being can be considered in the light of Care-a robust, practical, and dynamic lynchpin that formalizes the concepts of goal-directedness, stress, and the scaling of intelligence; it provides a rubric that, unlike other current concepts, is likely to not only survive but thrive in the coming advances of AI and bioengineering. We review relevant concepts in basal cognition and Buddhist thought, focusing on the size of an agent's goal space (its cognitive light cone) as an invariant that tightly links intelligence and compassion. Implications range across interpersonal psychology, regenerative medicine, and machine learning. The Bodhisattva's vow ("for the sake of all sentient life, I shall achieve awakening") is a practical design principle for advancing intelligence in our novel creations and in ourselves.

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

Single-Cell Analysis for Whole-Organism Datasets

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type-specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution.

Emergence of informative higher scales in biological systems: a computational toolkit for optimal prediction and control

The biological sciences span many spatial and temporal scales in attempts to understand the function and evolution of complex systems-level processes, such as embryogenesis. It is generally assumed that the most effective description of these processes is in terms of molecular interactions. However, recent developments in information theory and causal analysis now allow for the quantitative resolution of this question. In some cases, macro-scale models can minimize noise and increase the amount of information an experimenter or modeler has about "what does what." This result has numerous implications for evolution, pattern regulation, and biomedical strategies. Here, we provide an introduction to these quantitative techniques, and use them to show how informative macro-scales are common across biology. Our goal is to give biologists the tools to identify the maximally-informative scale at which to model, experiment on, predict, control, and understand complex biological systems.

Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications

Phenotypic delay-the time delay between genetic mutation and expression of the corresponding phenotype-is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.

The N-space Episenome unifies cellular information space-time within cognition-based evolution

Self-referential cellular homeostasis is maintained by the measured assessment of both internal status and external conditions based within an integrated cellular information field. This cellular field attachment to biologic information space-time coordinates environmental inputs by connecting the cellular senome, as the sum of the sensory experiences of the cell, with its genome and epigenome. In multicellular organisms, individual cellular information fields aggregate into a collective information architectural matrix, termed a N-space Episenome, that enables mutualized organism-wide information management. It is hypothesized that biological organization represents a dual heritable system constituted by both its biological materiality and a conjoining N-space Episenome. It is further proposed that morphogenesis derives from reciprocations between these inter-related facets to yield coordinated multicellular growth and development. The N-space Episenome is conceived as a whole cell informational projection that is heritable, transferable via cell division and essential for the synchronous integration of the diverse self-referential cells that constitute holobionts.

Natural History Collections as Inspiration for Technology

Living organisms are the ultimate survivalists, having evolved phenotypes with unprecedented adaptability, ingenuity, resourcefulness, and versatility compared to human technology. To harness these properties, functional descriptions and design principles from all sources of biodiversity information must be collated - including the hundreds of thousands of possible survival features manifest in natural history museum collections, which represent 12% of total global biodiversity. This requires a consortium of expert biologists from a range of disciplines to convert the observations, data, and hypotheses into the language of engineering. We hope to unite multidisciplinary biologists and natural history museum scientists to maximize the coverage of observations, descriptions, and hypotheses relating to adaptation and function across biodiversity, to make it technologically useful. This is to be achieved by developments in meta- taxonomic classification, phylogenetics, systematics, biological materials research, structure and morphological characterizations, and ecological data gathering from the collections - the aim being to identify and catalogue features essential for good biomimetic design.

Biological evolution as defense of 'self'

Although the origin of self-referential consciousness is unknown, it can be argued that the instantiation of self-reference was the commencement of the living state as phenomenal experientiality. As self-referential cognition is demonstrated by all living organisms, life can be equated with the sustenance of cellular homeostasis in the continuous defense of 'self'. It is proposed that the epicenter of 'self' is perpetually embodied within the basic cellular form in which it was instantiated. Cognition-Based Evolution argues that all of biological and evolutionary development represents the perpetual autopoietic defense of self-referential basal cellular states of homeostatic preference. The means by which these states are attained and maintained is through self-referential measurement of information and its communication. The multicellular forms, either as biofilms or holobionts, represent the cellular attempt to achieve maximum states of informational distinction and energy efficiency through individual and collective means. In this frame, consciousness, self-consciousness and intelligence can be identified as forms of collective cellular phenotype directed towards the defense of fundamental cellular self-reference.

Four domains: The fundamental unicell and Post-Darwinian Cognition-Based Evolution

Contemporary research supports the viewpoint that self-referential cognition is the proper definition of life. From that initiating platform, a cohesive alternative evolutionary narrative distinct from standard Neodarwinism can be presented. Cognition-Based Evolution contends that biological variation is a product of a self-reinforcing information cycle that derives from self-referential attachment to biological information space-time with its attendant ambiguities. That information cycle is embodied through obligatory linkages among energy, biological information, and communication. Successive reiterations of the information cycle enact the informational architectures of the basic unicellular forms. From that base, inter-domain and cell-cell communications enable genetic and cellular variations through self-referential natural informational engineering and cellular niche construction. Holobionts are the exclusive endpoints of that self-referential cellular engineering as obligatory multicellular combinations of the essential Four Domains: Prokaryota, Archaea, Eukaryota and the Virome. Therefore, it is advocated that these Four Domains represent the perpetual object of the living circumstance rather than the visible macroorganic forms. In consequence, biology and its evolutionary development can be appraised as the continual defense of instantiated cellular self-reference. As the survival of cells is as dependent upon limitations and boundaries as upon any freedom of action, it is proposed that selection represents only one of many forms of cellular constraint that sustain self-referential integrity.

Physical constraints in cell fate specification. A case in point: Microgravity and phenotypes differentiation

Data obtained by studying mammalian cells in absence of gravity strongly support the notion that cell fate specification cannot be understood according to the current molecular model. A paradigmatic case in point is provided by studying cell populations growing in absence of gravity. When the physical constraint (gravity) is 'experimentally removed', cells spontaneously allocate into two morphologically different phenotypes. Such phenomenon is likely enacted by the intrinsic stochasticity, which, in turn, is successively 'canalized' by a specific gene regulatory network. Both phenotypes are thermodynamically and functionally 'compatibles' with the new, modified environment. However, when the two cell subsets are reseeded into the 1g gravity field the two phenotypes collapse into one. Gravity constraints the system in adopting only one phenotype, not by selecting a pre-existing configuration, but more precisely shaping it de-novo through the modification of the cytoskeleton three-dimensional structure. Overall, those findings highlight how macro-scale features are irreducible to lower-scale explanations. The identification of macroscale control parameters - as those depending on the field (gravity, electromagnetic fields) or emerging from the cooperativity among the field's components (tissue stiffness, cell-to-cell connectivity) - are mandatory for assessing boundary conditions for models at lower scales, thus providing a concrete instantiation of top-down effects.

A Primer on Mathematical Modeling in the Study of Organisms and Their Parts

Mathematical modeling is a very powerful tool for understanding natural phenomena. Such a tool carries its own assumptions and should always be used critically. In this chapter, we highlight the key ingredients and steps of modeling and focus on their biological interpretation. In particular, we discuss the role of theoretical principles in writing models. We also highlight the meaning and interpretation of equations. The main aim of this chapter is to facilitate the interaction between biologists and mathematical modelers. We focus on the case of cell proliferation and motility in the context of multicellular organisms.

Biological information systems: Evolution as cognition-based information management

An alternative biological synthesis is presented that conceptualizes evolutionary biology as an epiphenomenon of integrated self-referential information management. Since all biological information has inherent ambiguity, the systematic assessment of information is required by living organisms to maintain self-identity and homeostatic equipoise in confrontation with environmental challenges. Through their self-referential attachment to information space, cells are the cornerstone of biological action. That individualized assessment of information space permits self-referential, self-organizing niche construction. That deployment of information and its subsequent selection enacted the dominant stable unicellular informational architectures whose biological expressions are the prokaryotic, archaeal, and eukaryotic unicellular forms. Multicellularity represents the collective appraisal of equivocal environmental information through a shared information space. This concerted action can be viewed as systematized information management to improve information quality for the maintenance of preferred homeostatic boundaries among the varied participants. When reiterated in successive scales, this same collaborative exchange of information yields macroscopic organisms as obligatory multicellular holobionts. Cognition-Based Evolution (CBE) upholds that assessment of information precedes biological action, and the deployment of information through integrative self-referential niche construction and natural cellular engineering antecedes selection. Therefore, evolutionary biology can be framed as a complex reciprocating interactome that consists of the assessment, communication, deployment and management of information by self-referential organisms at multiple scales in continuous confrontation with environmental stresses.

Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?

The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system's behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics.

Cellular intelligence: Microphenomenology and the realities of being

Traditions of Eastern thought conceptualised life in a holistic sense, emphasising the processes of maintaining health and conquering sickness as manifestations of an essentially spiritual principle that was of overriding importance in the conduct of living. Western science, which drove the overriding and partial eclipse of Eastern traditions, became founded on a reductionist quest for ultimate realities which, in the modern scientific world, has embraced the notion that every living process can be successfully modelled by a digital computer system. It is argued here that the essential processes of cognition, response and decision-making inherent in living cells transcend conventional modelling, and microscopic studies of organisms like the shell-building amoebae and the rhodophyte alga Antithamnion reveal a level of cellular intelligence that is unrecognized by science and is not amenable to computer analysis.

Why an extended evolutionary synthesis is necessary

Since the last major theoretical integration in evolutionary biology—the modern synthesis (MS) of the 1940s—the biosciences have made significant advances. The rise of molecular biology and evolutionary developmental biology, the recognition of ecological development, niche construction and multiple inheritance systems, the ‘-omics’ revolution and the science of systems biology, among other developments, have provided a wealth of new knowledge about the factors responsible for evolutionary change. Some of these results are in agreement with the standard theory and others reveal different properties of the evolutionary process. A renewed and extended theoretical synthesis, advocated by several authors in this issue, aims to unite pertinent concepts that emerge from the novel fields with elements of the standard theory. The resulting theoretical framework differs from the latter in its core logic and predictive capacities. Whereas the MS theory and its various amendments concentrate on genetic and adaptive variation in populations, the extended framework emphasizes the role of constructive processes, ecological interactions and systems dynamics in the evolution of organismal complexity as well as its social and cultural conditions. Single-level and unilinear causation is replaced by multilevel and reciprocal causation. Among other consequences, the extended framework overcomes many of the limitations of traditional gene-centric explanation and entails a revised understanding of the role of natural selection in the evolutionary process. All these features stimulate research into new areas of evolutionary biology.

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

Evolution has conserved "economic" systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions-including the use of arrows that connect pairs of consecutive observations-non-reductionist (spatial-temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

An engineering paradigm in the biomedical sciences: Knowledge as epistemic tool

In order to deal with the complexity of biological systems and attempts to generate applicable results, current biomedical sciences are adopting concepts and methods from the engineering sciences. Philosophers of science have interpreted this as the emergence of an engineering paradigm, in particular in systems biology and synthetic biology. This article aims at the articulation of the supposed engineering paradigm by contrast with the physics paradigm that supported the rise of biochemistry and molecular biology. This articulation starts from Kuhn's notion of a disciplinary matrix, which indicates what constitutes a paradigm. It is argued that the core of the physics paradigm is its metaphysical and ontological presuppositions, whereas the core of the engineering paradigm is the epistemic aim of producing useful knowledge for solving problems external to the scientific practice. Therefore, the two paradigms involve distinct notions of knowledge. Whereas the physics paradigm entails a representational notion of knowledge, the engineering paradigm involves the notion of 'knowledge as epistemic tool'.

The Nature of Self-Regulatory Fatigue and "Ego Depletion": Lessons From Physical Fatigue

Self-regulation requires overriding a dominant response and leads to temporary self-regulatory fatigue. Existing theories of the nature and causes of self-regulatory fatigue highlight physiological substrates such as glucose, or psychological processes such as motivation, but these explanations are incomplete on their own. Historically, theories of physical fatigue demonstrate a similar pattern of useful but incomplete explanations, as recent views of physical fatigue emphasize the roles of both physiological and psychological factors. In addition to accounting for multiple inputs, these newer views also explain how fatigue can occur even in the presence of sufficient resources. Examining these newer theories of physical fatigue can serve as a foundation on which to build a more comprehensive understanding of self-regulatory fatigue that integrates possible neurobiological underpinnings of physical and self-regulatory fatigue, and suggests the possible function of self-regulatory fatigue.

Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations

Background

Diagnostic errors can occur, in infectious diseases, when anti-microbial immune responses involve several temporal scales. When responses span from nanosecond to week and larger temporal scales, any pre-selected temporal scale is likely to miss some (faster or slower) responses. Hoping to prevent diagnostic errors, a pilot study was conducted to evaluate a four-dimensional (4D) method that captures the complexity and dynamics of infectious diseases.

Methods

Leukocyte-microbial-temporal data were explored in canine and human (bacterial and/or viral) infections, with: (i) a non-structured approach, which measures leukocytes or microbes in isolation; and (ii) a structured method that assesses numerous combinations of interacting variables. Four alternatives of the structured method were tested: (i) a noise-reduction oriented version, which generates a single (one data point-wide) line of observations; (ii) a version that measures complex, three-dimensional (3D) data interactions; (iii) a non-numerical version that displays temporal data directionality (arrows that connect pairs of consecutive observations); and (iv) a full 4D (single line-, complexity-, directionality-based) version.

Results

In all studies, the non-structured approach revealed non-interpretable (ambiguous) data: observations numerically similar expressed different biological conditions, such as recovery and lack of recovery from infections. Ambiguity was also found when the data were structured as single lines. In contrast, two or more data subsets were distinguished and ambiguity was avoided when the data were structured as complex, 3D, single lines and, in addition, temporal data directionality was determined. The 4D method detected, even within one day, changes in immune profiles that occurred after antibiotics were prescribed.

Conclusions

Infectious disease data may be ambiguous. Four-dimensional methods may prevent ambiguity, providing earlier, in vivo, dynamic, complex, and personalized information that facilitates both diagnostics and selection or evaluation of anti-microbial therapies.

Visualizing the indefinable: three-dimensional complexity of 'infectious diseases'

BACKGROUND

The words 'infection' and 'inflammation' lack specific definitions. Here, such words are not defined. Instead, the ability to visualize host-microbial interactions was explored.

METHODS

Leukocyte differential counts and four bacterial species (Staphylococcus aureus, Streptococcus dysgalactiae, Staphylococcus chromogenes, and Escherichia coli) were determined or isolated in a cross-sectional and randomized study conducted with 611 bovine milk samples. Two paradigms were evaluated: (i) the classic one, which measures non-structured (count or percent) data; and (ii) a method that, using complex data structures, detects and differentiates three-dimensional (3D) interactions among lymphocytes (L), macrophages (M), and neutrophils (N).

RESULTS

Classic analyses failed to differentiate bacterial-positive (B+) from -negative (B-) observations: B- and B+ data overlapped, even when statistical significance was achieved. In contrast, the alternative approach showed distinct patterns, such as perpendicular data inflections, which discriminated microbial-negative/mononuclear cell-predominating (MCP) from microbial-positive/phagocyte-predominating (PP) subsets. Two PP subcategories were distinguished, as well as PP/culture-negative (false-negative) and MCP/culture-positive (false-positive) observations. In 3D space, MCP and PP subsets were perpendicular to one another, displaying ≥ 91% specificity or sensitivity. Findings supported five inferences: (i) disease is not always ruled out by negative bacterial tests; (ii) low total cell counts can coexist with high phagocyte percents; (iii) neither positive bacterial isolation nor high cell counts always coincide with PP profiles; (iv) statistical significance is not synonymous with discrimination; and (v) hidden relationships cannot be detected when simple (non-structured) data formats are used and statistical analyses are performed before data subsets are identified, but can be uncovered when complexity is investigated.

CONCLUSIONS

Pattern recognition-based assessments can detect host-microbial interactions usually unobserved. Such cutoff-free, confidence interval-free, gold standard-free approaches provide interpretable information on complex entities, such as 'infection' and 'inflammation', even without definitions. To investigate disease dynamics, combinations of observational and experimental longitudinal studies, on human and non-human infections, are recommended.

Getting personal: can systems medicine integrate scientific and humanistic conceptions of the patient?

RATIONALE, AIMS AND OBJECTIVES

The practicing doctor, and most obviously the primary care clinician who encounters the full complexity of patients, faces several fundamental but intrinsically related theoretical and practical challenges - strongly actualized by so-called medically unexplained symptoms (MUS) and multi-morbidity. Systems medicine, which is the emerging application of systems biology to medicine and a merger of molecular biomedicine, systems theory and mathematical modelling, has recently been proposed as a primary care-centered strategy for medicine that promises to meet these challenges. Significantly, it has been proposed to do so in a way that at first glance may seem compatible with humanistic medicine. More specifically, it is promoted as an integrative, holistic, personalized and patient-centered approach. In this article, we ask whether and to what extent systems medicine can provide a comprehensive conceptual account of and approach to the patient and the root causes of health problems that can be reconciled with the concept of the patient as a person, which is an essential theoretical element in humanistic medicine.

METHODS

We answer this question through a comparative analysis of the theories of primary care doctor Eric Cassell and systems biologist Denis Noble.

RESULTS AND CONCLUSIONS

We argue that, although systems biological concepts, notably Noble's theory of biological relativity and downward causation, are highly relevant for understanding human beings and health problems, they are nevertheless insufficient in fully bridging the gap to humanistic medicine. Systems biologists are currently unable to conceptualize living wholes, and seem unable to account for meaning, value and symbolic interaction, which are central concepts in humanistic medicine, as constraints on human health. Accordingly, systems medicine as currently envisioned cannot be said to be integrative, holistic, personalized or patient-centered in a humanistic medical sense.

Unraveling liver complexity from molecular to organ level: challenges and perspectives

Biological responses are determined by information processing at multiple and highly interconnected scales. Within a tissue the individual cells respond to extracellular stimuli by regulating intracellular signaling pathways that in turn determine cell fate decisions and influence the behavior of neighboring cells. As a consequence the cellular responses critically impact tissue composition and architecture. Understanding the regulation of these mechanisms at different scales is key to unravel the emergent properties of biological systems. In this perspective, a multidisciplinary approach combining experimental data with mathematical modeling is introduced. We report the approach applied within the Virtual Liver Network to analyze processes that regulate liver functions from single cell responses to the organ level using a number of examples. By facilitating interdisciplinary collaborations, the Virtual Liver Network studies liver regeneration and inflammatory processes as well as liver metabolic functions at multiple scales, and thus provides a suitable example to identify challenges and point out potential future application of multi-scale systems biology.

Tumor and the microenvironment: a chance to reframe the paradigm of carcinogenesis?

The somatic mutation theory of carcinogenesis has eventually accumulated an impressive body of shortfalls and paradoxes, as admittedly claimed by its own supporters given that the cell-based approach can hardly explain the emergence of tissue-based processes, like cancer. However, experimental data and alternatives theories developed during the last decades may actually provide a new framework on which cancer research should be reframed. Such issue may be fulfilled embracing new theoretical perspectives, taking the cells-microenvironment interplay as the privileged level of observation and assuming radically different premises as well as new methodological frameworks. Within that perspective, the tumor microenvironment cannot be merely considered akin to new “factor” to be added to an already long list of “signaling factors”; microenvironment represents the physical-biochemical support of the morphogenetic field which drives epithelial cells towards differentiation and phenotype transformation, according to rules understandable only by means of a systems biology approach. That endeavour entails three fundamental aspects: general biological premises, the level of observation (i.e., the systems to which we are looking for), and the principles of biological organization that would help in integrating and understanding experimental data.

FOUNDATIONS FOR MODELING THE DYNAMICS OF GENE REGULATORY NETWORKS: A MULTILEVEL-PERSPECTIVE REVIEW

A promising alternative for unraveling the principles under which the dynamic interactions among genes lead to cellular phenotypes relies on mathematical and computational models at different levels of abstraction, from the molecular level of protein-DNA interactions to the system level of functional relationships among genes. This review article presents, under a bottom–up perspective, a hierarchy of approaches to modeling gene regulatory network dynamics, from microscopic descriptions at the single-molecule level in the spatial context of an individual cell to macroscopic models providing phenomenological descriptions at the population-average level. The reviewed modeling approaches include Molecular Dynamics, Particle-Based Brownian Dynamics, the Master Equation approach, Ordinary Differential Equations, and the Boolean logic abstraction. Each of these frameworks is motivated by a particular biological context and the nature of the insight being pursued. The setting of gene network dynamic models from such frameworks involves assumptions and mathematical artifacts often ignored by the non-specialist. This article aims at providing an entry point for biologists new to the field and computer scientists not acquainted with some recent biophysically-inspired models of gene regulation. The connections promoting intuition between different abstraction levels and the role that approximations play in the modeling process are highlighted throughout the paper.

Systems approaches in understanding evolution and evolvability

Systems and network-based approaches are becoming increasingly popular in cellular biology. One contribution of such approaches has been to shed some light on the evolutionary origins of core organisational principles in biological systems, such as modularity, robustness, and evolvability. Models of interactions between genes (epistasis) have also provided insight into how sexual reproduction may have evolved. Additionally, recent work on viewing evolution as a form of learning from the environment has indicated certain bounds on the complexity of the genetic circuits that can evolve within feasible quantities of time and resources. Here we review the key studies and results in these areas, and discuss possible connections between them. In particular, we speculate on the link between the two notions of 'evolvability': the evolvability of a system in terms of how agile it is in responding to novel goals or environments, and the evolvability of certain kinds of gene network functionality in terms of its computational complexity. Drawing on some recent work on the complexity of graph-theoretic problems on modular networks, we suggest that modularity as an organising principle may have its raison d'etre in its ability to enhance evolvability, in both its senses.

Systems Biology and Ratio-Based, Real-Time Disease Surveillance

Most infectious disease surveillance methods are not well fit for early detection. To address such limitation, here we evaluated a ratio- and Systems Biology-based method that does not require prior knowledge on the identity of an infective agent. Using a reference group of birds experimentally infected with West Nile virus (WNV) and a problem group of unknown health status (except that they were WNV-negative and displayed inflammation), both groups were followed over 22 days and tested with a system that analyses blood leucocyte ratios. To test the ability of the method to discriminate small data sets, both the reference group (n = 5) and the problem group (n = 4) were small. The questions of interest were as follows: (i) whether individuals presenting inflammation (disease-positive or D+) can be distinguished from non-inflamed (disease-negative or D-) birds, (ii) whether two or more D+ stages can be detected and (iii) whether sample size influences detection. Within the problem group, the ratio-based method distinguished the following: (i) three (one D- and two D+) data classes; (ii) two (early and late) inflammatory stages; (iii) fast versus regular or slow responders; and (iv) individuals that recovered from those that remained inflamed. Because ratios differed in larger magnitudes (up to 48 times larger) than percentages, it is suggested that data patterns are likely to be recognized when disease surveillance methods are designed to measure inflammation and utilize ratios.

Systems biology - the broader perspective

Systems biology has two general aims: a narrow one, which is to discover how complex networks of proteins work, and a broader one, which is to integrate the molecular and network data with the generation and function of organism phenotypes. Doing all this involves complex methodologies, but underpinning the subject are more general conceptual problems about upwards and downwards causality, complexity and information storage, and their solutions provide the constraints within which these methodologies can be used. This essay considers these general aspects and the particular role of protein networks; their functional outputs are often the processes driving phenotypic change and physiological function—networks are, in a sense, the units of systems biology much as proteins are for molecular biology. It goes on to argue that the natural language for systems-biological descriptions of biological phenomena is the mathematical graph (a set of connected facts of the general form <state 1> [process] <state 2> (e.g., <membrane-bound delta> [activates] <notch pathway>). Such graphs not only integrate events at different levels but emphasize the distributed nature of control as well as displaying a great deal of data. The implications and successes of these ideas for physiology, pharmacology, development and evolution are briefly considered. The paper concludes with some challenges for the future.

Randomness and multilevel interactions in biology

The dynamic instability of living systems and the "superposition" of different forms of randomness are viewed, in this paper, as components of the contingently changing, or even increasing, organization of life through ontogenesis or evolution. To this purpose, we first survey how classical and quantum physics define randomness differently. We then discuss why this requires, in our view, an enriched understanding of the effects of their concurrent presence in biological systems' dynamics. Biological randomness is then presented not only as an essential component of the heterogeneous determination and intrinsic unpredictability proper to life phenomena, due to the nesting of, and interaction between many levels of organization, but also as a key component of its structural stability. We will note as well that increasing organization, while increasing "order", induces growing disorder, not only by energy dispersal effects, but also by increasing variability and differentiation. Finally, we discuss the cooperation between diverse components in biological networks; this cooperation implies the presence of constraints due to the particular nature of bio-entanglement and bio-resonance, two notions to be reviewed and defined in the paper.

Feedback-based, system-level properties of vertebrate-microbial interactions

Background

Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered.

Methods

To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions.

Results

In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections.

Conclusions

More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

Compartmentalization and metabolic channeling for multienzymatic biosynthesis: practical strategies and modeling approaches

: The construction of efficient enzyme complexes for multienzymatic biosynthesis is of increasing interest in order to achieve maximum yield and to minimize the interference due to shortcomings that are typical for straightforward one-pot multienzyme catalysis. These include product or intermediate feedback inhibition, degeneration, and diffusive losses of reaction intermediates, consumption of co-factors, and others. The main mechanisms in nature to tackle these effects in transient or stable protein associations are the formation of metabolic channeling and microcompartments, processes that are desirable also for multienzymatic biosynthesis in vitro. This chapter provides an overview over two main aspects. First, numerous recent strategies for establishing compartmentalized multienzyme associations and constructed synthetic enzyme complexes are reviewed. Second, the computational methods at hand to investigate and optimize such associations systematically, especially with focus on large multienzyme complexes and metabolic channeling, are discussed. Perspectives on future studies of multienzymatic biosynthesis concerning compartmentalization and metabolic channeling are presented.

Defining systems biology: a brief overview of the term and field

Here we provide a broad overview of the definition of the term "systems biology" as well as pinpoint specific events in biological research and beyond that are consistently cited to have contributed and led to the current science of in silico systems biology. Since there have been many reviews and historical accounts describing the term, it would be impossible to include all single references. However, we do attempt to provide a consensus vision of how the field has evolved and consequently the terminology that followed it. We also highlight the development and general acceptance, and use, of standards for model representations as being crucial to the continued success of the in silico systems biology field.

Systems biology and cardiac arrhythmias

During the past few years, the development of effective, empirical technologies for treatment of cardiac arrhythmias has exceeded the pace at which detailed knowledge of the underlying biology has accumulated. As a result, although some clinical arrhythmias can be cured with techniques such as catheter ablation, drug treatment and prediction of the risk of sudden death remain fairly primitive. The identification of key candidate genes for monogenic arrhythmia syndromes shows that to bring basic biology to the clinic is a powerful approach. Increasingly sophisticated experimental models and methods of measurement, including stem cell-based models of human cardiac arrhythmias, are being deployed to study how perturbations in several biologic pathways can result in an arrhythmia-prone heart. The biology of arrhythmia is largely quantifiable, which allows for systematic analysis that could transform treatment strategies that are often still empirical into management based on molecular evidence.

Humans, fruit flies, and automatons

My response is divided into four sections: (1) is devoted to a potpourri of commentaries that are essentially in agreement with the substance of my target article (with one exception); in (2) I address, in response to one of the commentaries, several issues relating to the use of candidate gene association studies in behavior genetics (in particular those proposing a specific G×E interaction); in (3) I provide a detailed response to several defenses of the twin study methodology; and in (4) I conclude with several reflections on that methodology and the conception of human nature it has fostered.

Behavior genetics and postgenomics

The science of genetics is undergoing a paradigm shift. Recent discoveries, including the activity of retrotransposons, the extent of copy number variations, somatic and chromosomal mosaicism, and the nature of the epigenome as a regulator of DNA expressivity, are challenging a series of dogmas concerning the nature of the genome and the relationship between genotype and phenotype. According to three widely held dogmas, DNA is the unchanging template of heredity, is identical in all the cells and tissues of the body, and is the sole agent of inheritance. Rather than being an unchanging template, DNA appears subject to a good deal of environmentally induced change. Instead of identical DNA in all the cells of the body, somatic mosaicism appears to be the normal human condition. And DNA can no longer be considered the sole agent of inheritance. We now know that the epigenome, which regulates gene expressivity, can be inherited via the germline. These developments are particularly significant for behavior genetics for at least three reasons: First, epigenetic regulation, DNA variability, and somatic mosaicism appear to be particularly prevalent in the human brain and probably are involved in much of human behavior; second, they have important implications for the validity of heritability and gene association studies, the methodologies that largely define the discipline of behavior genetics; and third, they appear to play a critical role in development during the perinatal period and, in particular, in enabling phenotypic plasticity in offspring. I examine one of the central claims to emerge from the use of heritability studies in the behavioral sciences, the principle of minimal shared maternal effects, in light of the growing awareness that the maternal perinatal environment is a critical venue for the exercise of adaptive phenotypic plasticity. This consideration has important implications for both developmental and evolutionary biology.

Driving developmental and evolutionary change: A systems biology view

Embryonic development is underpinned by ∼50 core processes that drive morphogenesis, growth, patterning and differentiation, and each is the functional output of a complex molecular network. Processes are thus the natural and parsimonious link between genotype and phenotype and the obvious focus for any discussion of biological change. Here, the implications of this approach are explored. One is that many features of developmental change can be modeled as mathematical graphs, or sets of connected triplets of the general form <noun><verb><noun>. In these, the verbs (edges) are the outputs of the processes that drive change and the nouns (nodes) are the time-dependent states of biological entities (from molecules to tissues). Such graphs help unpick the multi-level complexity of developmental phenomena and may help suggest new experiments. Another comes from analyzing the effect of mutation that lead to tinkering with the dynamic properties of these processes and to congenital abnormalities; if these changes are both inherited and advantageous, they become evolutionary modifications. In this context, protein networks often represents what classical evolutionary genetics sees as genes, and the realization that traits reflect the output processes of complex networks, particularly for growth, patterning and pigmentation, rather than anything simpler clarifies some problems that the evolutionary synthesis of the 1950s has found hard to solve. In the wider context, most processes are used many times in development and cooperate to produce tissue modules (bones, branching duct systems, muscles etc.). Their underlying generative networks can thus be thought of as genomic modules or subroutines.

A biological relativity view of the relationships between genomes and phenotypes

This article explores the relativistic principle that there is no privileged scale of causality in biology to clarify the relationships between genomes and phenotypes. The idea that genetic causes are primary views the genome as a program. Initially, that view was vindicated by the discovery of mutations and knockouts that have large and specific effects on the phenotype. But we now know that these form the minority of cases. Many changes at the genome level are buffered by robust networks of interactions in cells, tissues and organs. The 'differential' view of genetics therefore fails because it is too restrictive. An 'integral' view, using reverse engineering from systems biological models to quantify contributions to function, can solve this problem. The article concludes by showing that far from breaking the supervenience principle, downward causation requires that it should be obeyed.

What mechanisms/processes underlie radiation-induced genomic instability?

Radiation-induced genomic instability is a modification of the cell genome found in the progeny of irradiated somatic and germ cells but that is not confined on the initial radiation-induced damage and may occur de novo many generations after irradiation. Genomic instability in the germ line does not follow Mendelian segregation and may have unpredictable outcomes in every succeeding generation. This phenomenon, for which there is extensive experimental data and some evidence in human populations exposed to ionising radiation, is not taken into account in health risk assessments. It poses an unknown morbidity/mortality burden. Based on experimental data derived over the last 20 years (up to January 2012) six mechanistic explanations for the phenomenon have been proposed in the peer-reviewed literature. This article compares these hypotheses with the empirical data to test their fitness to explain the phenomenon. As a conclusion, the most convincing explanation of radiation-induced genomic instability attributes it to an irreversible regulatory change in the dynamic interaction network of the cellular gene products, as a response to non-specific molecular damage, thus entailing the rejection of the machine metaphor for the cell in favour of one appropriate to a complex dissipative dynamic system, such as a whirlpool. It is concluded that in order to evaluate the likely morbidity/mortality associated with radiation-induced genomic instability, it will be necessary to study the damage to processes by radiation rather than damage to molecules.

Future medicine shaped by an interdisciplinary new biology

The projected effects of the new biology on future medicine are described. The new biology is essentially the result of shifts in the way biological research has progressed over the past few years, mainly through the involvement of physical scientists and engineers in biological thinking and research with the establishment of new teams and task forces to address the new challenges in biology. Their contributions go well beyond the historical contributions of mathematics, physical sciences, and engineering to medical practice that were largely equipment oriented. Over the next generation, the entire fabric of the biosciences will change as research barriers between disciplines diminish and eventually cease to exist. The resulting effects are starting to be noticed in front-line medicine and the prospects for the future are immense and potentially society changing. The most likely disciplines to have early effects are outlined and form the main thrust of this paper, with speculation about other disciplines and emphasis that although physics-based and engineering-based biology will change future medicine, the physical sciences and engineering will also be changed by these developments. Essentially, physics is being redefined by the need to accommodate these new views of what constitutes biological systems and how they function.

Morphogenetic fields in embryogenesis, regeneration, and cancer: non-local control of complex patterning

Establishment of shape during embryonic development, and the maintenance of shape against injury or tumorigenesis, requires constant coordination of cell behaviors toward the patterning needs of the host organism. Molecular cell biology and genetics have made great strides in understanding the mechanisms that regulate cell function. However, generalized rational control of shape is still largely beyond our current capabilities. Significant instructive signals function at long range to provide positional information and other cues to regulate organism-wide systems properties like anatomical polarity and size control. Is complex morphogenesis best understood as the emergent property of local cell interactions, or as the outcome of a computational process that is guided by a physically encoded map or template of the final goal state? Here I review recent data and molecular mechanisms relevant to morphogenetic fields: large-scale systems of physical properties that have been proposed to store patterning information during embryogenesis, regenerative repair, and cancer suppression that ultimately controls anatomy. Placing special emphasis on the role of endogenous bioelectric signals as an important component of the morphogenetic field, I speculate on novel approaches for the computational modeling and control of these fields with applications to synthetic biology, regenerative medicine, and evolutionary developmental biology.

A theory of biological relativity: no privileged level of causation

Must higher level biological processes always be derivable from lower level data and mechanisms, as assumed by the idea that an organism is completely defined by its genome? Or are higher level properties necessarily also causes of lower level behaviour, involving actions and interactions both ways? This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes. These insights are then generalized. A priori, there is no privileged level of causation. The relations between this form of 'biological relativity' and forms of relativity in physics are discussed. Biological relativity can be seen as an extension of the relativity principle by avoiding the assumption that there is a privileged scale at which biological functions are determined.

Basic statistical recipes for the emergence of biochemical discernment

An essential step towards understanding life would be to identify the very basic mechanisms responsible for the discerning behaviour of living biochemical systems, absent from randomly reacting chemical soups. One intuitively feels that this question goes beyond the particular nature of the biological molecules and should relate to general physical principles. The pre-eminent physicist Ludwig Boltzmann early envisioned life as a struggle for entropy, in concordance with the subsequent principle of self-organization out of equilibrium. Re-examination of elementary steady state biochemical systems from a statistical perspective supports this view and shows that sigmoidal responses arising from microstates elimination, are sufficient to explain innermost characteristics of life, including its capacity to convert random molecular interactions into accurate biological reactions. A primary operating strategy to achieve this goal is the introduction of time-irreversible transitions in molecular state conversion cycles by injection of free energy, which confers decisional capacity to single macromolecules. Selected examples from various fields of molecular biology such as enzymology and gene expression, are provided to show that these non-equilibrium steady state mechanisms remain important in contemporary biochemical systems. But in addition, information archiving allowed the emergence of the time-reversible counterparts of these mechanisms, mediated by evolutionary pre-organized macromolecular complexes capable of generating discernment in a non-dissipative manner.