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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An EarthWinian Genesis Synthesis

B. Systems Biology Unites: EvoDevo, Genomes, Cells, Networks, Symbiosis, Homology, Inherency

Qureshi, Irfan and Mark Mehler. Emerging Roles of Non-Coding RNAs in Brain Evolution, Development, Plasticity and Disease. Nature Reviews Neuroscience. 13/8, 2012. Albert Einstein College of Medicine physicians and neuroscientists provide a good example of the welling 21st century AND phase of integrative genomics, as it opens to realizations of multifaceted nucleotide network interrelations.

Novel classes of small and long non-coding RNAs (ncRNAs) are being characterized at a rapid pace, driven by recent paradigm shifts in our understanding of genomic architecture, regulation and transcriptional output, as well as by innovations in sequencing technologies and computational and systems biology. These ncRNAs can interact with DNA, RNA and protein molecules; engage in diverse structural, functional and regulatory activities; and have roles in nuclear organization and transcriptional, post-transcriptional and epigenetic processes. This expanding inventory of ncRNAs is implicated in mediating a broad spectrum of processes including brain evolution, development, synaptic plasticity and disease pathogenesis. (Abstract)

Radde, Nicole and Marc-Thorsten Hutt. The Physics behind Systems Biology. EPJ Nonlinear Biomedical Physics. Online August, 2016. As an instance of the present cross-fertilization of life sciences into physical realms, which become animated by way of natural complexities, University of Stuttgart and Jacobs University, Bremen, theorists proceed to give this integrative turn a fundamental basis. A significant reason is the growing profusion of network studies everywhere since 2000. After the two infinities of atom and cosmos, this third phase and turn of realizing an actual organic genesis uniVerse is much underway.

Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology.

Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.

Rigoutsos, Isidore and Gregory Stephanopoulos, eds. Systems Biology. Oxford: Oxford University Press, 2007. A two volume set covering Genomics and Systems Biology (Vol. 1 Published) and Networks, Models, and Applications (Vol. 2 Forthcoming).

Systems biology is an integrated approach that brings together and leverages theoretical, experimental, and computational approaches in order to establish connections among important molecules or groups of molecules in order to aid the eventual mechanistic explanation of cellular processes and systems. (Vol. 1, xiii)

Written for a wide audience, each volume presents a timely compendium of essential information that is necessary for a comprehensive study of the subject. The chapters in the two volumes reflect the hierarchical nature of systems biology. Chapter authors-world-recognized experts in their fields-provide authoritative discussions on a wide range of topics along this hierarchy. Volume I explores issues pertaining to genomics that range from prebiotic chemistry to noncoding RNAs. Volume II covers an equally wide spectrum, from mass spectrometry to embryonic stem cells. (Oxford website)

Rulands, Steffen and Benjamin Simons. Emergence and Universality in the Regulation of Stem Cell Fate. Current Opinion in Systems Biology. 5/57, 2017. MPI Physics of Complex Systems and Cambridge University biophysicists profess a new level of integrity between biological dynamics and statistical physics, by which stem cell studies and their palliative avail can be advanced. A novel synthesis is laid out by way of phase transitions, renormalization theories, non-equilibrium thermodynamics, and more, so to define universal classes. This new journal contains similar examples such as Andrea Cavagna, et al (9/49, 2018) and Lea Geontoro (1/80, 2017), see each herein.

The mechanisms that control cell fate behaviour during development, and their dysregulation in disease, remain the subject of interest and debate. Advances in single-cell genomics have shifted emphasis towards the elucidation of molecular regulatory programmes and transcriptional cell states. However, quantitative statistical approaches based on cell lineage tracing data have provided fresh insight into stem and progenitor cell behaviour, questioning the role of cell fate stochasticity, transcriptional heterogeneity and state priming. These investigations, which draw upon conceptual insights from statistical physics and mathematics, provide a novel, generic and rigorous framework to resolve and classify stem cell self-renewal strategies. Here, using epithelial maintenance as an example, we consider the foundation, conceptual basis, utility and limitations of such quantitative approaches in cell biology. (Abstract)

Saetzler, Kurt, et al. Systems Biology beyond Networks: Generating Order from Disorder through Self-Organization. Seminars in Cancer Biology. 21/3, 2012. In a special issue on Systems Biology and Cancer, with coauthors Carlos Sonnenschein and Ana Soto, (also issue coeditors), University of Ulster, N. Ireland, and Tufts University School of Medicine researchers draw upon an “organicism and emergentism” tradition going back to Ludwig von Bertalanffy and Paul Weiss to advance a 21st century systems turn as the necessary, vital way to perceive, and to heal and offset cancer cell malfeasance. See also in this issue “Outline of a Concept for Organismic Systems Biology” by Bernd Rosslenbroich, and “On the Intrinsic Inevitability of Cancer” by Sui Huang, who tells how to mollify this.

Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. (Abstract)

Salazar-Ciudad, Isaac, and Hugo Cano-Fernandez. Evo-Devo Beyond Development: Generalizing Evo-Devo to All Levels of Phenotypic Evolution. BioEssays. March, 2023. Into this year, Universitat Autònoma de Barcelona system biologists propose an expanded unifed cross-synthesis between life’s embryonic, physiological and historic phases. In regard, their contribution is further suggestive of an embryonic gestation (all the way to our latest transition in pediawise individuality.

A foundational idea of evo-devo is that morphological variation is not isotropic in all directions. Instead, some directions are more likely than others from DNA variations which depend on development. We argue that this evo-devo perspective should apply not only to morphology but to evolution across its phenotypic phases. To do this, two types of arguments need be combined: generative about which phenotypic mode arises in each instance and selective issues about which passes to the offspring. (Excerpt)

Sauro, Herbert and Kyung Hyuk Kim. It’s an Analog World. Nature. 497/572, 2013. A news report on a technical article “Synthetic Analog Computation in Living Cells” in the same issue by Ramiz Daniel, et al, a MIT systems biology group, that is said to achieve an effective recognition and reciprocal utilization of both archetypal digital byte and relational integration analog modes.

The reality is that cells use a hybrid approach to information processing. In some cases they use digital yes-or-no decisions, but in many cases cellular signals are analog, with levels of gradation. (Sauro, 572) We propose that an efficient and accurate computational approach to synthetic biological networks will ultimately integrate both analog and digital processing. This mixed signal approach can utilize analog or collective analog computation for front-end processing in concert with decision-making digital circuits,or it may use graded feedback for simultaneous analog and digital computation, as in neuron networks in the brain. (Daniel, 623)

Schwenk, Kurt, et al. Grand Challenges in Organismal Biology. Integrative and Comparative Biology. 49/1, 2009. Mostly unnoticed, a grand revolution is underway in the biological and evolutionary sciences to perceive living systems as far more than passive, accidents. Rather personal organisms at every sequential, emergent stage are distinguished by a universal modular, self-organization.

A renaissance in organismal biology has been sparked by recent conceptual, theoretical, methodological, and computational advances in the life sciences, along with an unprecedented interdisciplinary integration with Mathematics, Engineering, and the physical sciences. Despite a decades-long trend toward reductionist approaches to biological problems, it is increasingly recognized that whole organisms play a central role in organizing and interpreting information from across the biological spectrum. (7)

Organisms operate across spatial scales from molecules to ecosystems and temporal scales from nanoseconds to eons. They are dynamic, multi-dimensional, hierarchical, and nonlinear networks characterized by positive and negative feedback, ill-defined boundaries, and high levels of stochasticity. Further, they operate within dynamic environments that display similar complexity. (10)

Shapiro, James. Rethinking the (Im)Possible in Evolution. http://shapiro.bsd.uchicago.edu/Shapiro.2013.Rethinking_the_%28Im%29Possible_in_Evolution.html. A paper by the University of Chicago geneticist presented March 2012 at an Oxford Workshop on the Conceptual Foundations of Systems Biology, to appear in Progress in Biophysics and Molecular Biology in 2013. With his usual incisiveness, Shapiro (search) surveys the history of evolutionary theory whence mechanism trumped vitalism because the tacit machine model has ruled since the 17th century. Any teleological goal-orientation has henceforth been prohibited. Yet a plethora of evidence from systems genomics and an informational turn lately makes a quite different case. The 1950s Modern Synthesis of random mutations of pointillist genes is passé, need be scraped. Much more goes on via biochemical, nucleotide, and cellular spontaneities and responses. The salient shift is from a one-way “read-only (ROM) memory” to a Read-Write memory system, able to edit itself due to epigenetic environmental influences. As a result, a non-accidental, natural purposefulness ought to be allowed and recognized. On his website can also be found Blog entries, mainly on Huffington Post, where attacks by a selection-only old guard (Jerry Coyne) are countered. Once more, another capsule of the imminent epochal revolution from sterile nothingness to a quickening, gravid something, as life conquers death.

DNA change is a non-random process in the sense that it results from well-defined biochemical operations, each leaving a characteristic signature in DNA structure. Collectively, these are called “natural genetic engineering” operators. Cells synthesize, recombine, cut and splice, and otherwise modify their genomes in well-defined reactions. (2) Cells execute purposeful DNA restructuring events during normal life-cycles in a non-random but also non-deterministic fashion. These goal-oriented natural genetic engineering processes occur in many organisms, including ourselves. (3)

Shubin, Neil. Gene Regulatory Networks and Network Models in Development and Evolution. Proceedings of the National Academy of Sciences. 114/Vol. 23, 2017. An introduction to this September 2015 Sackler Colloquium organized by Shubin in honor and memory of Eric Davidson (1937-2015), the CalTech biologist (search both) who since the early 2000s studied and advocated the genomic and evolutionary importance of active nucleotide connectivities. Among the papers are Causes and Evolutionary Consequences of Primordial Germ-cell Specification in Metazoans, Gene Regulation During Drosophila Eggshell Patterning, Applying Gene Regulatory Network Logic to the Evolution of Social Behavior (search Baran) and Assessing Regulatory Information in Developmental Gene Regulatory Networks by Eric Davidson and Isabelle Peter (abstract next). This “conceptual revolution” is now in full force as many more entries in the new section attest.

Gene regulatory networks (GRNs) provide a transformation function between the static genomic sequence and the spatial specifications operating development. We address regulatory information at different levels of network organization from single node to subcircuit to large-scale GRNs and how design features such as architecture, hierarchical organization, and cis-regulatory logic contribute to developmental functions. Using subcircuits from the sea urchin endomesoderm GRN, we evaluate by Boolean modeling and in silico perturbations the import of circuit features. Thus, we begin to see how regulatory information encoded at individual nodes is integrated at all levels of network organization to control developmental process. (IP & ED Abstract excerpt)

Shubin, Neil, et al. Deep Homology and the Origins of Evolutionary Novelty. Nature. 457/818, 2009. Shubin, University of Chicago, along with Cliff Tabin, Harvard Medical School, and Sean B. Carroll, University of Wisconsin, follow on their 1997 Nature paper Fossils, Genes, and the Evolution of Animal Limbs (388/639) which introduced this expansive view of life’s conservation of skeletal Bauplan forms from its very beginnings. Studies since of animal features such as eye kind, tetrapod limbs, fish fins, beetle horns, and so on imply a “parallel evolution” of independent causations, which are now traceable to a common generative gentics. See also The Origin of the Tetrapod Limb by Igor Schneider and Neil Shubin in Trends in Genetics (29/7, 2013) whence its tripartite segments recur from fish fins to primates, and Tecumseh Fitch’s (search) The Biology and Evolution of Language: Deep Homology and the Evolution of Innovation (2009).

Homology, as classically defined, refers to a historical continuity in which morphological features in related species are similar in pattern or form because they evolved from a corresponding structure in a common ancestor. Deep homology also implies a historical continuity, but in this case the continuity may not be so evident in particular morphologies; it lies in the complex regulatory circuitry inherited from a common ancestor. (818)

Do new anatomical structures arise de novo, or do they evolve from pre-existing structures? Advances in developmental genetics, palaeontology and evolutionary developmental biology have recently shed light on the origins of some of the structures that most intrigued Charles Darwin, including animal eyes, tetrapod limbs and giant beetle horns. In each case, structures arose by the modification of pre-existing genetic regulatory circuits established in early metazoans. The deep homology of generative processes and cell-type specification mechanisms in animal development has provided the foundation for the independent evolution of a great variety of structures. (Abstract)

It is not possible to identify what is new in evolution without understanding the old. This is a reflection of the way evolution works, with some movelties being traceable as modifications of primitive conditions and others having origins that are much less obvious. As a result, the problems of novelty and homology have been deeply intertwined for the past century and a half. One of the most important, and entirely unanticipated insights of the past 15 years was the recognition of an ancient similarity of patterning mechanisms in diverse organisms, often among structures not thought to be homologous on morphological or phylogenetic grounds. (818)

Darwin closed The Descent of Man with the line “Man still bears in his bodily frame the indelible stamp of his lowly origin.” These words have never rung more true. Researchers are now starting to appreciate the presence of far more indelible stamps of humanity’s lowly metazoan origins that Darwin could ever have imagined. The detection of deep homologies offers more than new glimpses of evolutionary history, however. Such nomologies provide a profound insight into the evolutionary process. Studies of deep homology are showing that new structures need not arise from scratch, genetically speaking, but can evolve by deploying regulatory circuits that were first established in early animals.

Soyer, Orkun, ed. Evolutionary Systems Biology. Berlin: Springer, 2012. A collection edited by the University of Exeter biologist to provide a better notice throughout life’s long course of how so much interactive, dynamically generative phenomena is going on. The publisher waxes “This book tries to decipher evolutionary design principles and the origins of systems level properties in biology, such as modularity, robustness, and network connectivity.” Typical chapters are Paulien Hogeweg’s “Toward a Theory of Multilevel Evolution: Long-Term Information Integration Shapes the Mutational Lindscape and Enhances Evolvability,” and “Building Synthetic systems to Learn Nature’s Design Principles” by Eric Davidson, et al. In "On the Search for Design Principles in Biological Principles in Biological Systems" Juan Poyatos, Logic of Genomic Systems Laboratory, Spanish National Biotechnology Centre, agrees that the 1960s project of General Systems Theory is at last confirmed for there are indeed independent, universal laws or program that repeat in kind across life's emergent scale. So might we now consider another integral school designated as “Systems Evolution?” For a follow-up synopsis, see “Evolutionary Systems Biology: What It Is and Why It Matters” by Soyer and Maureen O’Malley in BioEssays (Online May, 2013).

Most of evolutionary theory has abstracted away from how information is coded in the genome and how this information is transformed into traits on which selection takes place. While in the earliest stages of biological evolution, in the RNA world, the mapping from the genotype into function was largely predefined by the physical–chemical properties of the evolving entities (RNA replicators, e.g. from sequence to folded structure and catalytic sites), in present-day organisms, the mapping itself is the result of evolution. I will review results of several in silico evolutionary studies which examine the consequences of evolving the genetic coding, and the ways this information is transformed, while adapting to prevailing environments. Such multilevel evolution leads to long-term information integration. Through genome, network, and dynamical structuring, the occurrence and/or effect of random mutations becomes nonrandom, and facilitates rapid adaptation. This is what does happen in the in silico experiments. Is it also what did happen in biological evolution? I will discuss some data that suggest that it did. (Hogeweg Abstract)

Most of evolutionary theory has abstracted away from how information is coded in the genome and how this information is transformed into traits on which selection takes place. While in the earliest stages of biological evolution, in the RNA world, the mapping from the genotype into function was largely predefined by the physical–chemical properties of the evolving entities (RNA replicators, e.g. from sequence to folded structure and catalytic sites), in present-day organisms, the mapping itself is the result of evolution. I will review results of several in silico evolutionary studies which examine the consequences of evolving the genetic coding, and the ways this information is transformed, while adapting to prevailing environments. Such multilevel evolution leads to long-term information integration. Through genome, network, and dynamical structuring, the occurrence and/or effect of random mutations becomes nonrandom, and facilitates rapid adaptation. This is what does happen in the in silico experiments. Is it also what did happen in biological evolution? I will discuss some data that suggest that it did. (Hogeweg Abstract)

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