Systems biology beyond networks: generating order from disorder through self-organization

The "Culture" of Organs: A Holistic Theory on the Origins of the Cancer Tissue Environment

For over a century, the somatic gene mutation theory of cancer has been a scientific orthodoxy. The recent failures of causal explanations using this theory and the lack of significant progress in addressing the cancer problem medically have led to a new competition of ideas about just what cancer is. This essay presents an alternative view of cancer as a developmental process gone wrong. More specifically, cancer is a breakdown in the autopoietic process of organ maintenance and the multicellular coordination of tissues. Breast cancer is viewed through a systems science perspective as an example of the importance of framing one's theoretical assumptions before making empirical judgments. Finally, a new understanding of the histoarchitecture of the interstitium is presented as a first principle of cancer: a process of cells coming from cells, invading the space between cells.

Informing the Cannabis Conjecture: From Life's Beginnings to Mitochondria, Membranes and the Electrome-A Review

Before the late 1980s, ideas around how the lipophilic phytocannabinoids might be working involved membranes and bioenergetics as these disciplines were "in vogue". However, as interest in genetics and pharmacology grew, interest in mitochondria (and membranes) waned. The discovery of the cognate receptor for tetrahydrocannabinol (THC) led to the classification of the endocannabinoid system (ECS) and the conjecture that phytocannabinoids might be "working" through this system. However, the how and the "why" they might be beneficial, especially for compounds like CBD, remains unclear. Given the centrality of membranes and mitochondria in complex organisms, and their evolutionary heritage from the beginnings of life, revisiting phytocannabinoid action in this light could be enlightening. For example, life can be described as a self-organising and replicating far from equilibrium dissipating system, which is defined by the movement of charge across a membrane. Hence the building evidence, at least in animals, that THC and CBD modulate mitochondrial function could be highly informative. In this paper, we offer a unique perspective to the question, why and how do compounds like CBD potentially work as medicines in so many different conditions? The answer, we suggest, is that they can modulate membrane fluidity in a number of ways and thus dissipation and engender homeostasis, particularly under stress. To understand this, we need to embrace origins of life theories, the role of mitochondria in plants and explanations of disease and ageing from an adaptive thermodynamic perspective, as well as quantum mechanics.

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.

Biologically grounded scientific methods: The challenges ahead for combating epidemics

The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.

In vitro models of the human heart

Cardiac congenital disabilities are the most common organ malformations, but we still do not understand how they arise in the human embryo. Moreover, although cardiovascular disease is the most common cause of death globally, the development of new therapies is lagging compared with other fields. One major bottleneck hindering progress is the lack of self-organizing human cardiac models that recapitulate key aspects of human heart development, physiology and disease. Current in vitro cardiac three-dimensional systems are either engineered constructs or spherical aggregates of cardiomyocytes and other cell types. Although tissue engineering enables the modeling of some electro-mechanical properties, it falls short of mimicking heart development, morphogenetic defects and many clinically relevant aspects of cardiomyopathies. Here, we review different approaches and recent efforts to overcome these challenges in the field using a new generation of self-organizing embryonic and cardiac organoids.

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.

Electromagnetic Fields, Genomic Instability and Cancer: A Systems Biological View

This review discusses the use of systems biology in understanding the biological effects of electromagnetic fields, with particular focus on induction of genomic instability and cancer. We introduce basic concepts of the dynamical systems theory such as the state space and attractors and the use of these concepts in understanding the behavior of complex biological systems. We then discuss genomic instability in the framework of the dynamical systems theory, and describe the hypothesis that environmentally induced genomic instability corresponds to abnormal attractor states; large enough environmental perturbations can force the biological system to leave normal evolutionarily optimized attractors (corresponding to normal cell phenotypes) and migrate to less stable variant attractors. We discuss experimental approaches that can be coupled with theoretical systems biology such as testable predictions, derived from the theory and experimental methods, that can be used for measuring the state of the complex biological system. We also review potentially informative studies and make recommendations for further studies.

Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data

Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy, pleiotropy, complexity, dynamics, ambiguity, circularity, personalized outcomes, and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features-such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.

Modeling Cell Reactions to Ionizing Radiation: From a Lesion to a Cancer

This article focuses on the analytic modeling of responses of cells in the body to ionizing radiation. The related mechanisms are consecutively taken into account and discussed. A model of the dose- and time-dependent adaptive response is considered for 2 exposure categories: acute and protracted. In case of the latter exposure, we demonstrate that the response plateaus are expected under the modelling assumptions made. The expected total number of cancer cells as a function of time turns out to be perfectly described by the Gompertz function. The transition from a collection of cancer cells into a tumor is discussed at length. Special emphasis is put on the fact that characterizing the growth of a tumor (ie, the increasing mass and volume), the use of differential equations cannot properly capture the key dynamics-formation of the tumor must exhibit properties of the phase transition, including self-organization and even self-organized criticality. As an example, a manageable percolation-type phase transition approach is used to address this problem. Nevertheless, general theory of tumor emergence is difficult to work out mathematically because experimental observations are limited to the relatively large tumors. Hence, determination of the conditions around the critical point is uncertain.

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions

In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

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.

Cancer as an ecomolecular disease and a neoplastic consortium

Current anticancer paradigms largely target driver mutations considered integral for cancer cell survival and tumor progression. Although initially successful, many of these strategies are unable to overcome the tremendous heterogeneity that characterizes advanced tumors, resulting in the emergence of resistant disease. Cancer is a rapidly evolving, multifactorial disease that accumulates numerous genetic and epigenetic alterations. This results in wide phenotypic and molecular heterogeneity within the tumor, the complexity of which is further amplified through specific interactions between cancer cells and the tumor microenvironment. In this context, cancer may be perceived as an "ecomolecular" disease that involves cooperation between several neoplastic clones and their interactions with immune cells, stromal fibroblasts, and other cell types present in the microenvironment. This collaboration is mediated by a variety of secreted factors. Cancer is therefore analogous to complex ecosystems such as microbial consortia. In the present article, we comment on the current paradigms and perspectives guiding the development of cancer diagnostics and therapeutics and the potential application of systems biology to untangle the complexity of neoplasia. In our opinion, conceptualization of neoplasia as an ecomolecular disease is warranted. Advances in knowledge pertinent to the complexity and dynamics of interactions within the cancer ecosystem are likely to improve understanding of tumor etiology, pathogenesis, and progression. This knowledge is anticipated to facilitate the design of new and more effective therapeutic approaches that target the tumor ecosystem in its entirety.

Origin of Cancer: An Information, Energy, and Matter Disease

Cells are open, highly ordered systems that are far away from equilibrium. For this reason, the first function of any cell is to prevent the permanent threat of disintegration that is described by thermodynamic laws and to preserve highly ordered cell characteristics such as structures, the cell cycle, or metabolism. In this context, three basic categories play a central role: energy, information, and matter. Each of these three categories is equally important to the cell and they are reciprocally dependent. We therefore suggest that energy loss (e.g., through impaired mitochondria) or disturbance of information (e.g., through mutations or aneuploidy) or changes in the composition or distribution of matter (e.g., through micro-environmental changes or toxic agents) can irreversibly disturb molecular mechanisms, leading to increased local entropy of cellular functions and structures. In terms of physics, changes to these normally highly ordered reaction probabilities lead to a state that is irreversibly biologically imbalanced, but that is thermodynamically more stable. This primary change—independent of the initiator—now provokes and drives a complex interplay between the availability of energy, the composition, and distribution of matter and increasing information disturbance that is dependent upon reactions that try to overcome or stabilize this intracellular, irreversible disorder described by entropy. Because a return to the original ordered state is not possible for thermodynamic reasons, the cells either die or else they persist in a metastable state. In the latter case, they enter into a self-driven adaptive and evolutionary process that generates a progression of disordered cells and that results in a broad spectrum of progeny with different characteristics. Possibly, 1 day, one of these cells will show an autonomous and aggressive behavior—it will be a cancer cell.

Properties of Life: Toward a Coherent Understanding of the Organism

The question of specific properties of life compared to nonliving things accompanied biology throughout its history. At times this question generated major controversies with largely diverging opinions. Basically, mechanistic thinkers, who tried to understand organismic functions in terms of nonliving machines, were opposed by those who tried to describe specific properties or even special forces being active within living entities. As this question included the human body, these controversies always have been of special relevance to our self-image and also touched practical issues of medicine. During the second half of the twentieth century, it seemed to be resolved that organisms are explainable basically as physicochemical machines. Especially from the perspective of molecular biology, it seemed to be clear that organisms need to be explained solely by the chemical functions of their component parts, although some resistance to this view never ceased. This research program has been working quite successfully, so that science today knows a lot about the physiological and chemical processes within organisms. However, again new doubts arise questioning whether the mere continuation of this analytical approach will finally generate a fundamental understanding of living entities. At the beginning of the twenty-first century the quest for a new synthesis actually comes from analytical empiricists themselves. The hypothesis of the present paper is that empirical research has been developed far enough today, that it reveals by itself the materials and the prerequisites to understand more of the specific properties of life. Without recourse to mysterious forces, it is possible to generate answers to this age-old question, just using recent, empirically generated knowledge. This view does not contradict the results of reductionistic research, but rather grants them meaning within the context of organismic systems and also may increase their practical usefulness. Although several of these properties have been discussed before, different authors usually concentrated on a single one or some of them. The paper describes ten specific properties of living entities as they can be deduced from contemporary science. The aim is to demonstrate that the results of empirical research show both the necessity as well as the possibility of the development of a new conception of life to build a coherent understanding of organismic functions.

The Significance of an Enhanced Concept of the Organism for Medicine

Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

Emergent properties of a computational model of tumour growth

While there have been enormous advances in our understanding of the genetic drivers and molecular pathways involved in cancer in recent decades, there also remain key areas of dispute with respect to fundamental theories of cancer. The accumulation of vast new datasets from genomics and other fields, in addition to detailed descriptions of molecular pathways, cloud the issues and lead to ever greater complexity. One strategy in dealing with such complexity is to develop models to replicate salient features of the system and therefore to generate hypotheses which reflect on the real system. A simple tumour growth model is outlined which displays emergent behaviours that correspond to a number of clinically relevant phenomena including tumour growth, intra-tumour heterogeneity, growth arrest and accelerated repopulation following cytotoxic insult. Analysis of model data suggests that the processes of cell competition and apoptosis are key drivers of these emergent behaviours. Questions are raised as to the role of cell competition and cell death in physical cancer growth and the relevance that these have to cancer research in general is discussed.

A systemic evolutionary approach to cancer: Hepatocarcinogenesis as a paradigm

The systemic evolutionary theory of cancer pathogenesis posits that cancer is generated by the de-emergence of the eukaryotic cell system and by the re-emergence of its archaea (genetic material and cytoplasm) and prokaryotic (mitochondria) subsystems with an uncoordinated behavior. This decreased coordination can be caused by a change in the organization of the eukaryote environment (mainly chronic inflammation), damage to mitochondrial DNA and/or to its membrane composition by many agents (e.g. viruses, chemicals, hydrogenated fatty acids in foods) or damage to nuclear DNA that controls mitochondrial energy production or metabolic pathways, including glycolysis. Here, we postulate that the two subsystems (the evolutionarily inherited archaea and the prokaryote) in a eukaryotic differentiated cell are well integrated, and produce the amount of clean energy that is constantly required to maintain the differentiated status. Conversely, when protracted injuries impair cell or tissue organization, the amount of energy necessary to maintain cell differentiation can be restricted, and this may cause gradual de-differentiation of the eukaryotic cell over time. In cirrhotic liver, for example, this process can be favored by reduced oxygen availability to the organ due to an altered vasculature and the fibrotic barrier caused by the disease. Thus, hepatocarcinogenesis is an ideal example to support our hypothesis. When cancer arises, the pre-eukaryote subsystems become predominant, as shown by the metabolic alterations of cancer cells (anaerobic glycolysis and glutamine utilization), and by their capacity for proliferation and invasion, resembling the primitive symbiotic components of the eukaryotic cell.

Deep sea as a source of novel-anticancer drugs: update on discovery and preclinical/clinical evaluation in a systems medicine perspective

The deep-sea habitat is a source of very potent marine-derived agents that may inhibit the growth of human cancer cells "in vitro" and "in vivo". Salinosporamide-A, Marizomib, by Salinispora species is a proteasome inhibitor with promising anticancer activity (Phase I/II trials). Different deep-sea-derived drugs are under preclinical evaluation. Cancer is a complex disease that may be represented by network medicine. A simple consequence is the change of the concept of target entity from a single protein to a whole molecular pathway and or cellular network. Deep-sea-derived drugs fit well to this new concept.

Deep sea as a source of novel-anticancer drugs: update on discovery and preclinical/clinical evaluation in a systems medicine perspective

The deep-sea habitat is a source of very potent marine-derived agents that may inhibit the growth of human cancer cells "in vitro" and "in vivo". Salinosporamide-A, Marizomib, by Salinispora species is a proteasome inhibitor with promising anticancer activity (Phase I/II trials). Different deep-sea-derived drugs are under preclinical evaluation. Cancer is a complex disease that may be represented by network medicine. A simple consequence is the change of the concept of target entity from a single protein to a whole molecular pathway and or cellular network. Deep-sea-derived drugs fit well to this new concept.

[Cancer and the elusive cancer genes]

For almost a century, the somatic mutation theory (SMT) has been the prevalent theory to explain carcinogenesis. The SMT posits that the accumulation of mutations in the genome of a single normal cell is responsible for the transformation of such cell into a neoplasm. Implicitly, this theory claims that the default state of cells in metazoan is quiescence and that cancer is a cell-based, genetic and molecular disease. From lessons learned while performing our own research on control of cell proliferation and while adopting an organicist perspective, in 1999, we proposed a competing theory, the tissue organization field theory (TOFT). In contraposition to the SMT, (1) the TOFT posits that cancer is a tissue-based disease whereby carcinogens (directly) and mutations in the germ-line (indirectly) may alter normal interactions between the stroma and their adjacent epithelium. And (2) the TOFT explicitly acknowledges that the default state of all cells is proliferation and motility, a premise that is relevant to and compatible with evolutionary theory. Theoretical arguments and experimental evidence are presented to compare the merits of the original SMT and its variants and those of the TOFT in organizing principles, construct objectivity, and ultimately explain carcinogenesis.

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.

Competing views on cancer

Despite intense research efforts that have provided enormous insight, cancer continues to be a poorly understood disease. There has been much debate over whether the cancerous state can be said to originate in a single cell or whether it is a reflection of aberrant behaviour on the part of a 'society of cells'. This article presents, in the form of a debate conducted among the authors, three views of how the problem might be addressed. We do not claim that the views exhaust all possibilities. These views are (a) the tissue organization field theory (TOFT) that is based on a breakdown of tissue organization involving many cells from different embryological layers, (b) the cancer stem cell (CSC) hypothesis that focuses on genetic and epigenetic changes that take place within single cells, and (c) the proposition that rewiring of the cell's protein interaction networks mediated by intrinsically disordered proteins (IDPs) drives the tumorigenic process. The views are based on different philosophical approaches. In detail, they differ on some points and agree on others. It is left to the reader to decide whether one approach to understanding cancer appears more promising than the other.

Recent advances in modeling languages for pathway maps and computable biological networks

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs.

Brain basis of self: self-organization and lessons from dreaming

Through dreaming, a different facet of the self is created as a result of a self-organizing process in the brain. Self-organization in biological systems often happens as an answer to an environmental change for which the existing system cannot cope; self-organization creates a system that can cope in the newly changed environment. In dreaming, self-organization serves the function of organizing disparate memories into a dream since the dreamer herself is not able to control how individual memories become weaved into a dream. The self-organized dream provides, thereby, a wide repertoire of experiences; this expanded repertoire of experience results in an expansion of the self beyond that obtainable when awake. Since expression of the self is associated with activity in specific areas of the brain, the article also discusses the brain basis of the self by reviewing studies of brain injured patients, discussing brain imaging studies in normal brain functioning when focused, when daydreaming and when asleep and dreaming.

An automated training paradigm reveals long-term memory in planarians and its persistence through head regeneration

Planarian flatworms are a popular system for research into the molecular mechanisms that enable these complex organisms to regenerate their entire body, including the brain. Classical data suggest that they may also be capable of long-term memory. Thus, the planarian system may offer the unique opportunity to study brain regeneration and memory in the same animal. To establish a system for the investigation of the dynamics of memory in a regenerating brain, we developed a computerized training and testing paradigm that avoided the many issues that confounded previous, manual attempts to train planarians. We then used this new system to train flatworms in an environmental familiarization protocol. We show that worms exhibit environmental familiarization, and that this memory persists for at least 14 days - long enough for the brain to regenerate. We further show that trained, decapitated planarians exhibit evidence of memory retrieval in a savings paradigm after regenerating a new head. Our work establishes a foundation for objective, high-throughput assays in this molecularly tractable model system that will shed light on the fundamental interface between body patterning and stored memories. We propose planarians as key emerging model species for mechanistic investigations of the encoding of specific memories in biological tissues. Moreover, this system is lik ely to have important implications for the biomedicine of stem-cell-derived treatments of degenerative brain disorders in human adults.

A self-organization framework for symmetry breaking in the mammalian embryo

The mechanisms underlying the appearance of asymmetry between cells in the early embryo and consequently the specification of distinct cell lineages during mammalian development remain elusive. Recent experimental advances have revealed unexpected dynamics of and new complexity in this process. These findings can be integrated in a new unified framework that regards the early mammalian embryo as a self-organizing system.

Theoretical aspects of Systems Biology

The natural world consists of hierarchical levels of complexity that range from subatomic particles and molecules to ecosystems and beyond. This implies that, in order to explain the features and behavior of a whole system, a theory might be required that would operate at the corresponding hierarchical level, i.e. where self-organization processes take place. In the past, biological research has focused on questions that could be answered by a reductionist program of genetics. The organism (and its development) was considered an epiphenomenona of its genes. However, a profound rethinking of the biological paradigm is now underway and it is likely that such a process will lead to a conceptual revolution emerging from the ashes of reductionism. This revolution implies the search for general principles on which a cogent theory of biology might rely. Because much of the logic of living systems is located at higher levels, it is imperative to focus on them. Indeed, both evolution and physiology work on these levels. Thus, by no means Systems Biology could be considered a 'simple' 'gradual' extension of Molecular Biology.

Paradoxes in Carcinogenesis: There Is Light at the End of That Tunnel!

The exchange of opinions motivated by Dr. Baker's article "Paradoxes in carcinogenesis should spur new avenues of research: An historical perspective" illustrates the reasons why the field of cancer research is stuck in a dead end. This paralysis presents a rich opportunity for philosophers, historians and sociologists of science to decipher the whys of this impasse. On the strictly biological front, we suggest to reinstate in cancer research the time proven practice so productive in the physical sciences of discarding wrong hypotheses and theories. We share the suggestion by Dr. Baker to stop trying to unify the two main theories of carcinogenesis, i.e., the Somatic Mutation Theory (SMT) and the Tissue Organization Field Theory (TOFT) because they are incompatible. Dr. Baker suggests breaching the impasse by investing in paradox-driven research. We discuss the barriers to the implementation of this novel strategy, and the significant impact that this strategy will have on knowledge at large and its application for the prevention and cure of cancer.

Systems biology, bioinformatics, and biomarkers in neuropsychiatry

Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.

From bottom-up approaches to levels of organization and extended critical transitions

Biological thinking is structured by the notion of level of organization. We will show that this notion acquires a precise meaning in critical phenomena: they disrupt, by the appearance of infinite quantities, the mathematical (possibly equational) determination at a given level, when moving at an “higher” one. As a result, their analysis cannot be called genuinely bottom-up, even though it remains upward in a restricted sense. At the same time, criticality and related phenomena are very common in biology. Because of this, we claim that bottom-up approaches are not sufficient, in principle, to capture biological phenomena. In the second part of this paper, following (Bailly, 1991b), we discuss a strong criterium of level transition. The core idea of the criterium is to start from the breaking of the symmetries and determination at a “first” level in order to “move” at the others. If biological phenomena have multiple, sustained levels of organization in this sense, then they should be interpreted as extended critical transitions.

Is systems biology a promising approach to resolve controversies in cancer research?

At the beginning of the 21st century cancer research has reached an impasse similar to that experienced in developmental biology in the first decades of the 20th century when conflicting results and interpretations co-existed for a long time until these differences were resolved and contradictions were eliminated. In cancer research, instead of this healthy "weeding-out" process, there have been attempts to reach a premature synthesis, while no hypothesis is being rejected. Systems Biology could help cancer research to overcome this stalemate by resolving contradictions and identifying spurious data. First, in silico experiments should allow cancer researchers to be bold and a priori reject sets of data and hypotheses in order to gain a deeper understanding of how each dataset and each hypothesis contributes to the overall picture. In turn, this process should generate novel hypotheses and rules, which could be explored using these in silico approaches. These activities are significantly less costly and much faster than "wet-experiments". Consequently, Systems Biology could be advantageously used both as a heuristic tool to guide "wet-experiments" and to refine hypotheses and test predictions.

Chromatin remodeling system, cancer stem-like attractors, and cellular reprogramming

The cancer cell attractors theory provides a next-generation understanding of carcinogenesis and natural explanation of punctuated clonal expansions of tumor progression. The impressive notion of atavism of cancer is now updated but more evidence is awaited. Besides, the mechanisms that the ectopic expression of some germline genes result in somatic tumors such as melanoma and brain tumors are emerging but are not well understood. Cancer could be triggered by cells undergoing abnormal cell attractor transitions, and may be reversible with "cyto-education". From mammals to model organisms like Caenorhabditis elegans and Drosophila melanogaster, the versatile Mi-2β/nucleosome remodeling and histone deacetylation complexes along with their functionally related chromatin remodeling complexes (CRCs), i.e., the dREAM/Myb-MuvB complex and Polycomb group complex are likely master regulators of cell attractors. The trajectory that benign cells switch to cancerous could be the reverse of navigation of embryonic cells converging from a series of intermediate transcriptional states to a final adult state, which is supported by gene expression dynamics inspector assays and some cross-species genetic evidence. The involvement of CRCs in locking cancer attractors may help find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cancer cell function in the same way as the normal cells.