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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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V. Life's Evolutionary Development Organizes Itself: A 2020s Genesis Synthesis

B. Systems Biology Integrates: Genomes, Networks, Symbiosis, Deep Homology

Halley, Julianne, et al. Self-Organizing Circuitry and Emergent Computation in Mouse Embryonic Stem Cells. Stem Cell Research8/2. 2012. As the Abstract details, Wellcome Trust Centre, University of Cambridge, Monash University, Monash Institute of Pharmaceutical Sciences, and Institute for Systems Biology, Seattle, researchers, including David Winkler and Sui Huang, look toward a wider theoretical survey and basis for medical guidance. As many endeavors nowadays, they seek “an underlying philosophy that resonates strongly with the physical sciences… in terms of general principles or laws that do not depend on specific details.” In regard, the nonlinear sciences of complex dynamic systems are turned to provide better explanations needed to understand and benefit from vital stem cell biology. For another example, see “Growing Self-Organizing Mini-Guts from a Single Intestinal Stem Cell” by Toshiro Sato and Hans Clevers in the June 7, 2013 issue of Science.

Pluripotency is a cellular state of multiple options. Here, we highlight the potential for self-organization to contribute to stem cell fate computation. A new way of considering regulatory circuitry is presented that describes the expression of each transcription factor (TF) as a branching process that propagates through time, interacting and competing with others. In a single cell, the interactions between multiple branching processes generate a collective process called ‘critical-like self-organization’. We explain how this phenomenon provides a valid description of whole genome regulatory circuit dynamics. The hypothesis of exploratory stem cell decision-making proposes that critical-like self-organization (also called rapid self-organized criticality) provides the backbone for cell fate computation in regulative embryos and pluripotent stem cells.

The emergent and highly dynamic circuitry is affected by various sources of selection pressure, such as the expression of TFs with disproportionate influence over other genes, and extrinsic biological and physical stimuli that differentially modulate particular gene expression cascades. Extrinsic conditions continuously trigger waves of transcription that ripple throughout regulatory networks on multiple spatiotemporal scales, providing the context within which circuitry self-organizes. In this framework, a distinction between instructive and selective mechanisms of fate determination is misleading because it is the 'interference pattern', rather than any single instructing or selecting factor, that is ultimately responsible for computing and directing cell fate. (Abstract excerpts)

Henney, Adriano and Giulio Superti-Furga. A Network Solution. Nature. 455/730, 2008. A report by scientists engaged in medicinal advances notes that although the systems biology turn is of obvious value, because the approach has been malleable and ill defined, its full benefit has lagged. To aid such discussion, a blog site has been set up: http://network.nature.com/groups/systbiohumanhealth/forum/topics.

Hernandez-Lemus, Enrique, et al. Advances in Systems Biology. Computational Biology and Chemistry. 59/B, 2015. The three authors – EH-L does Computational Genomics at the National Institute of Genomic Medicine and Center for Complexity Sciences, National Autonomous University of Mexico, Wentian Li is at the Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, Long Island, USA, and Pablo Meyer is in the Translational Systems Biology and Nanobiotechnology Group, IBM Watson Research Center, New York – well reflect a worldwide collaborative effort to understand and avail genomic phenomena via an array of mid 2010s methods and capabilities. Typical papers are Principles for the Organization of Gene-Sets and Transcriptional Master Regulator Analysis in Breast Cancer Genetic Networks.

This special issue of deals with the most recent advances in systems biology and is focused on modern techniques and approaches for modeling, analysis and integration of cell signaling and transcriptional networks, metabolic pathways, phenotypic and physiological data and other issues related to an integrated, quantitative and systemic view of biological systems. Statistical and probabilistic approaches, reverse engineering methods, multi-scale modeling, data mining, pattern recognition and machine learning techniques applied to the study of diseased phenotypes and human biology, eukaryotic cells functioning as well as studies in metabolism and microbiology, are some of the themes included herein.

Hoehndorf, Robert, et al. Integrating Systems Biology Models and Biomedical Ontologies. BMC Systems Biology. 5/124, 2011. In this BioMed Central (BMC) journal, seven authors from British, Canadian, and American universities and institutes join the historic, intensive shift of research studies from isolate individuals and paper publishing to a total online occasion spanning databases, protocols, collaborations, and on to instant, peer-reviewed postings. We cite this article to illustrate a prime aspect in this post-sequence era, that of achieving translateable, practical, software languages. Actually, a search of such sources as Journal of Cheminformatics, (see Systems Chemistry) Bioinformatics, PLoS Computational Biology, PLoS One, Journal of the Royal Society Interface, and so on, along with the many “-Omics” journals, reveals a Babel-like proliferation of markup languages in search of a common vernacular.

Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. (124) We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms. (124)

Hogeweg, Paulien. The Roots of Bioinformatics in Theoretical Biology. PLoS Computational Biology. 7/3, 2011. As the attributed originator of the term “bioinformatics” in the 1970s, the author here present a retrospect years later of its acceptance, a fertile approach still far from running its course. Cells and organisms are not collections or bag of disparate pieces, but rightly “complex dynamical systems” that merit such an appropriate modeling.

It seemed to us (PH and Ben Hesper) that one of the defining properties of life was information processing in its various forms, e.g., information accumulation during evolution, information transmission from DNA to intra- and intercellular processes, and the interpretation of such information at multiple levels. (1) We felt that the re-introduction of biologically inspired computational ideas back into biology was needed in order to begin to understand biological systems as information processing systems. In particular, a focus on local interaction leading to emergent phenomena at multiple scales seemed to be missing in most biological models. (2)

Holford, Mande and Benjamin Normark. Integrating the Life Sciences to Jumpstart the Next Decade of Discovery. Integrative & Comparative Biology. 61/6, 2021. Hunter College and UM Amherst biologists introduce this special edition of 29 papers which relate to the new National Science Foundation Big Ideas initiative: Understanding the Rules of Life. As we note, this 2020s span (along with its trauma and tragedy) seems to be a singular moment when many scientific fields from quantum and evolutionary to societal and cosmic have reached an epic phase of convergent synthesis. A strong, steady theme can then be seen to course through these entries. Living systems, in both their Earthly development and organismic function, are found to be distinguished by nested networks which join all their cellular, modular, communal scalar domains. With this overall frame in place, researchers can now go on to discern a common pattern and process which recurs in kind at every spatial and temporal instance.

Other typical entries are The Axes of Life: A Roadmap for Understanding Dynamic Multiscale Systems; Charting a New Frontier Integrating Mathematical Modeling in Complex Biological Systems from Molecules to Ecosystems; From Flatland to Jupiter: Searching for Rules of Interaction Across Biological Scales; Complex Temporal Biology: Towards a Unified Multi-Scale Approach to Predict the Flow of Information; and Deep Learning for Reintegrating Biology. In regard, a Grand Challenge is scoped out that does allude to a vivifying self-similar lawfulness which exists on its independent own. But such an imperative revolutionary admission by our EarthWise scientists remains in abeyance. (As a note, a working “Discovery Decade” title had been in place for our Chap. IX 2022 Summary Edition before I came across this project.)

NSF’S Big Ideas: Understanding the Rules of Life Life on our planet is arranged in levels of organization ranging from the molecular scale through to the biosphere. There exists a remarkable amount of complexity in the interactions within and between these levels of organization and across scales of time and space. The NSF Rules of Life Big Idea seeks to enable discoveries to better understand such interactions and identify causal, predictive relationships across these scales.

Hood, Leroy. A Systems Approach to Medicine. Zewail, Ahmed, ed. Physical Biology: From Atoms to Medicine. London: Imperial College Press, 2008. A pioneer researcher and advocate, long at Caltech and now founding director of the Seattle-based Institute for Systems Biology, plans to engage going forward life’s intricate organization with advanced instrumentation and sequencing capabilities. This project will be guided by the perception that biology is most of all “informational” in kind. For the second quote, the first two aspects are the digital genome, and its ubiquitous interactive networks.

Biology will be a dominant science in the 21st century – just as chemistry was in the 19th century and physics was in the 20th century – and for a fascinating reason. The dominant challenge for all the scientific and engineering disciplines in the 21st century will be complexity. (337) Third, biological information is encoded by a multiscale information hierarchy: DNA, RNA, proteins, interactions, biological networks, cells, tissues and organs, individuals, and finally, ecologies. (342)

Huang, Sui. The Molecular and Mathematical Basis of Waddington's Epigenetic Landscape: A Framework for Post-Darwinian Biology? BioEssays. 34/2, 2012. An editorial by Andrew Moore highlights Huang’s article as a harbinger of a “New Evolutionary Synthesis,” as founded on gene regulatory networks. The biologist and physician author is at the Institute of Systems Biology, Seattle, previously University of Calgary’s Institute of Biocomplexity and collaborator with Stuart Kauffman, and earlier at Harvard Medical School. As the paper avers, in this post-sequence, systems biology turn, the day of single gene to trait is surpassed by an equally real presence and activity of ubiquitous networks. To this can be added many epigenetic inputs, seen as a 21st century verification of Conrad Waddington’s 1960s landscape model. That is to conclude, much more is actually going on than random mutation and winnowing selection. A century and a half after The Origin of Species, it is possible to factor in a whole new dimension and input of nature’s “complex dynamic systems” as they serve life’s episodic emergence. An important, timely contribution as unavoidable evidence builds toward the critical consilience of a genesis evolutionary synthesis.

The Neo-Darwinian concept of natural selection is plausible when one assumes a straightforward causation of phenotype by genotype. However, such simple 1:1 mapping must now give place to the modern concepts of gene regulatory networks and gene expression noise. Both can, in the absence of genetic mutations, jointly generate a diversity of inheritable randomly occupied phenotypic states that could also serve as a substrate for natural selection. This form of epigenetic dynamics challenges Neo-Darwinism. It needs to incorporate the non-linear, stochastic dynamics of gene networks. (149)

Natural constraints in organismal design, emanating from the inescapable laws of chemistry, physics and even mathematics, as well as from history, present prefabricated modules of high complexity for natural selection to choose from. But the complexity itself is not the work of natural selection. The fractal, optimally space-filling structure of branching tissues, such as the lung, or the appealing stripe and spot patters of animal coats are the most lucid examples of the creative force of self-organization that can be reduced to the laws of physics. (150)

Many of the conceptual difficulties of Neo-Darwinian theory can be traced to its failure to embrace the dynamic consequences of gene regulatory interactions in their entirety – a much neglected source of self-organizing constraints, variability and randomness with persistent effects. The quasi-potential landscape with attractors – a mathematical entity that has a molecular basis and is not a mere metaphor – must be considered as a key intermediate layer in the genotype-to-phenotype correspondence that underlies neo-Darwinian theory. Gene network dynamics readily accounts for Waddington’s genetic assimilation, the related Baldwin effect, Neo-Lamarckism and other epigenetic phenomena. (156)

Ideker, Trey. Network Genomics. Bringmann, P., et al, eds. Systems Biology. Berlin: Springer, 2007. Now that many genetic components have been sequenced and mapped, contends the Principal Investigator at the Laboratory for Integrative Network Biology, University of California at San Diego, it is possible to recognize the endemic modular networks that arise from their dynamic interaction. Other typical articles herein are Systems Biology: New Paradigms for Cell Biology and Drug Design by Hans Westerhoff, and A Plea for More Theory in Molecular Biology by O. Wolkenhauer.

Ideker, Trey, et al. A New Approach to Decoding Life: Systems Biology. Annual Review of Genomics and Human Genetics. 2/343, 2001. After sequencing of the human genome, by which biology is seen as an informational science, the next research phase is to appreciate the complementary network properties of gene expression. In this view is constructed a multilevel informational hierarchy of complex processes from DNA and protein interactions to organisms and ecologies.

Ingalls, Brian. Mathematical Modeling in Systems Biology. Cambridge: MIT Press, 2020. A University of Waterloo, Ontario biomathematician provides a latest tutorial for all manner of nonlinear complex, network, dynamical phenomena at effect in this integrative field.

This book offers an introduction to mathematical concepts and methods needed for the construction and interpretation of models in molecular systems biology. The first four chapters cover the basics of mathematical modeling in molecular systems biology. The last four chapters address specific biological domains, treating modeling of metabolic networks, of signal transduction pathways, of gene regulatory networks, and of electrophysiology and neuronal action potentials.

Jaeger, Johannes, et al. The Comet Cometh: Evolving Developmental Systems. Biological Theory. 10/1, 2015. In a special EDS issue, Jaeger, Universitat Pompeu Fabra, Barcelona, Manfred Laubichler, Arizona State University, along with the late Werner Callebaut, write a lengthy mediation on this expansive view which seeks to aptly apply dynamical theories to evolutionary phenomena. As the Abstract details, such an integral explanation is being filled in as disciplines and approaches come together and cross-inform over temporal and spatial domains.

In a recent opinion piece, Denis Duboule has claimed that the increasing shift towards systems biology is driving evolutionary and developmental biology apart, and that a true reunification of these two disciplines within the framework of evolutionary developmental biology (EvoDevo) may easily take another 100 years. He identifies methodological, epistemological, and social differences as causes for this supposed separation. Our article provides a contrasting view. We argue that Duboule’s prediction is based on a one-sided understanding of systems biology as a science that is only interested in functional, not evolutionary, aspects of biological processes. Instead, we propose a research program for an evolutionary systems biology, which is based on local exploration of the configuration space in evolving developmental systems.

We call this approach—which is based on reverse engineering, simulation, and mathematical analysis—the natural history of configuration space. We discuss a number of illustrative examples that demonstrate the past success of local exploration, as opposed to global mapping, in different biological contexts. We argue that this pragmatic mode of inquiry can be extended and applied to the mathematical analysis of the developmental repertoire and evolutionary potential of evolving developmental mechanisms and that evolutionary systems biology so conceived provides a pragmatic epistemological framework for the EvoDevo synthesis. (Abstract)

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