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V. Life's Evolutionary Development Organizes Itself: A 2020s Genesis Synthesis

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

Pang, Tin Yau and Sergei Maslov. Universal Distribution of Component Frequencies in Biological and Technological Systems. Proceedings of the National Academy of Sciences. 110/15, 2013. Brookhaven National Laboratory biologists contribute to the growing experimental and theoretical recognition that life’s evolution, each creaturely instance, and our civil society repeats and iterates the same dynamic fractal self-organization at every degree, time and place. See also in regard by the authors “Toolbox Model of Evolution of Metabolic Pathways on Networks of Arbitrary Topology” in PLoS Computational Biology, along with Jacopo Grilli, et al, “Joint Scaling Laws in Functional and Evolutionary Categories in Prokaryotic Genomes” in Nucleic Acids Research (40/2, 2011) and Marco Lagomarsino, et al “Universal Features in the Genome-Level Evolution of Protein Domains” in Genome Biology (10/R12, 2009). Maslov leads a “KBase” team based at BNL, Cold Spring Harbor Laboratory, and Yale University trying to “integrate everything we can learn about plants, microbes, and metagenomics from the genetic and molecular to the organism and systems level,” see second quote. And where do all these whole repetitive propensities come from, who are we to learn this, what great discovery might they bode?

Bacterial genomes and large-scale computer software projects both consist of a large number of components (genes or software packages) connected via a network of mutual dependencies. Components can be easily added or removed from individual systems, and their use frequencies vary over many orders of magnitude. We study this frequency distribution in genomes of ~500 bacterial species and in over 2 million Linux computers and find that in both cases it is described by the same scale-free power-law distribution with an additional peak near the tail of the distribution corresponding to nearly universal components. We argue that the existence of a power law distribution of frequencies of components is a general property of any modular system with a multilayered dependency network. (Abstract, this paper)

In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger “universal” network formed by the union of all species-specific networks. (Abstract, PLoS article)

The Department of Energy Systems Biology Knowledgebase (KBase) is an emerging software and data environment designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses on a scalable computing infrastructure, and model interactions in microbes, plants, and their communities. KBase provides an open, extensible framework for secure sharing of data, tools, and scientific conclusions in predictive and systems biology. (www.kbase.us)

Perrimon, Norbert and Naama Barkai. The Era of Systems Developmental Biology. Current Opinion in Genetics & Development. 21/6, 2011. An introduction to a special issue of the Genetics of System Biology by the Harvard Medical School researchers. A typical article might be “Scaling of Morphogen Gradients” by Danny Ben-Zvi, et al. In this issue and journal “development” often pertains to embryological stages, as the “systems” vista once more revitalizes and reunites this archetypal phase.

Developmental biology, fueled by advances in genomics, proteomics, imaging, and applications of physics and mathematical modeling, is yet undergoing another renaissance – entering the era of ‘Systems Developmental Biology’. The goal of ‘Systems Developmental Biology’ is to go beyond our current understanding of what a single gene, or a few connected parts, do in a biological context. The challenge is to become more systematic, unbiased and quantitative in the analysis of developmental questions. Thus, we now want to identify all the parts and pathways involved and quantify some of the key parameters to build mathematical and computational models that describe and predict the behavior of the systems. (681)

Peter, Isabelle and Eric Davidson. Genomic Control Process: Development and Evolution. Cambridge, MA: Academic Press, 2015. A CalTech biology professor and the geneticist (1937-2015, search) who was the founding theorist of gene regulatory networks provide a consummate volume to date of this major expansion of active genetic phenomena.

Chapter 1 explains different levels of control affecting developmental gene expression in animal cells, and an overview of the physical nature of the regulatory genome. The book goes on to provide in depth understandings of GRNs, how they generate the regulatory conditions, cis-regulatory functions operating at the network nodes, and the dynamics of transcriptional activity in development. The next Chapters apply network theory to embryonic development of all major kinds; development of adult body parts and organs; and to cell fate specification. Chapter 6 examines the conceptual richness that has derived from various approaches to predictive, quantitative models of GRNs and GRN circuits. In The final section the notes applications to bilaterian evolution, including the underlying explanation of hierarchical animal phylogeny, and more. (Publisher excerpt)

Priami, Corrado. Algorthmic Systems Biology. Communications of the ACM. May, 2009. The University of Trento computer scientist and Director of its Microsoft funded Centre for Computational and Systems Biology extols the shift from a prior object emphasis to lately engage life’s dynamical phenomena. But a mechanistic metaphor prevails such that a philosophical disconnect remains.

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)

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)

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