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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

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

This section will report the significant turn in genetics and biology since the 2001 Human Genome Project to reassemble and reconceive the many biomolecule pieces and components into an integral whole. With their initial occurrence and identity in place, the presence of equally real interrelations in between, and the information they carry and convey, can be admitted and quantified. By this inclusive scope, the dynamic web of life can shed a mechanistic, machinery cast for a phenomenal emergent vitality.

Into the 2010s the network revolution has joined and deepened the systems approach by which further to reunite animate phenomena. Life’s nested wholes within wholes thus become braced and graced by the same multiplex topologies and dynamics as found everywhere else from quantome to neurome and epitome.

Into the 2020s we have added evolutionary developmental biology references as these phases come together again, along with an emphasis on network anatomy and physiology everywhere, homology recurrences and a growing sense of inherency as life's forms and functions seem to arise on cue from deep physical sources.

2020: Since the 2001 human genome sequence, a broad movement has sought to quantify and involve the equally real regulatory and vivifying connectivities between the biomolecular nucleotides. Recent endeavors factor in network topologies and involve deep learning methods. As the 130 sample references convey, an international effort aided by online advances in technique and computation is well along with an integral understanding of 3D and 4D organismic epi/genomes and metabolomes.

Bogdan, Paul, et al. Biological Networks Across Scales. Integrative & Comparative Biology. 61/6, 2022.
Caetano-Anolles, Kelsey, et al. A Minimal Framework for Describing Living Systems: A MultiDimensional View of Life Scales. Integrative & Comparative Biology. 61/6, 2021
DiFrisco, James and Johannes Jaeger. Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective. BioEssays. 42/6, 2020.
Gazestani, Vahid and Nathan Lewis. From Genotype to Phenotype: Augmenting Deep Learning with Networks and Systems Biology. Current Opinion in Systems Biology. 15/68, 2019.
Gilpin, William, et al. Machine Learning Meets Systems Biology Current Opinion in Systems Biology. July 30, 2020.

Holford, Mande and Benjamin Normark. Integrating the Life Sciences to Jumpstart the Next Decade of Discovery. Integrative & Comparative Biology. 61/6, 2021.
Ingalls, Brian. Mathematical Modeling in Systems Biology Cambridge: MIT Press, 2020.
Kutschera, Ulrich. Systems Biology of Eukaryotic Superorganisms and the Holobiont Concept. Theory in Biosciences. Online June, 2018
Ogura, Takehiko and Wolfgang Busch. Genotypes, Networks, Phenotypes: Moving Toward Plant Systems Genetics. Annual Review of Cell and Developmental Biology. 32/103, 2016.
Stephanou, Angelique, et al. Systems Biology, Systems Medicine, Systems Pharmacology. Acta Biotheretica. 66/4, 2018.


8th International Biocuration Conference. http://biocuration2015.big.ac.cn/. With a banner From Big Data to Big Discovery, this meeting of the International Society for Biocuration was held in Beijing in April 2015. A 200 page Abstracts book is available from the site, two of which are below. Since 2008 this bioinformatic endeavor has become a significant contributor to genome sequencing projects from individuals to present and past species. An initial note was The Future of Biocuration in Nature (455/48, 2008). The ISB website, www.biocurator.org, is an entry to more info, publications, and events. A new Oxford journal, DATABASE Journal of Biological Databases and Curation, is a good article source, and for Proceedings from prior conferences.

Biocuration involves the translation and integration of information relevant to biology into a database or resource that enables integration of the scientific literature as well as large data sets. Accurate and comprehensive representation of biological knowledge, as well as easy access to this data for working scientists and a basis for computational analysis, are primary goals of biocuration.

Genome annotation The process of identifying the locations of genes and all of the coding regions in a genome and determining what those genes do. An annotation (irrespective of the context) is a note added by way of explanation or commentary. Once a genome is sequenced, it needs to be annotated to make sense of it.

The Role of Biocurators: To extract knowledge from published papers; To connect information from different sources in a coherent and comprehensible way; To inspect and correct automatically predicted gene structures and protein sequences to provide high-quality proteomes; To develop and manage structured controlled vocabularies that are crucial for data relations and the logical retrieval of large data sets; To integrate knowledge bases to represent complex systems such as metabolic pathways and protein-interaction networks. (Nature, 48)

In this presentation I will discuss the challenges that we have faced in developing community annotation in the area of protein and RNA families. We have used Wikipedia as the source of our annotations and as the interface for our communityto use. I will present results showing the success of this approach as well as some of the more challenging aspects. Finally I will discuss the social engineering of how we have tried to motivate scientists to edit Wikipedia. (Biocuration in the Community, Alex Bateman, European Bioinformatics Institute)

Overall, the big data has four hallmarks: Volume: the quantity of data; Velocity: the time in which Big Data can be processed; Variety: the type of data that Big Data can comprise; and Veracity: the degree in which a researcher trusts the used information. Biological big data, in general, has the similar properties. But, not alike the data gathered by Google, WeChat, and Ali Baba, etc, even the big biological data is highly heterogeneous, inside the data, there exist intrinsic structures determined by various biological principles and experiment designs. (Challenges and Practices of Big Data in Life Science, Yixue Li, Shanghai Center of Bioinformation Technology)

Conceptual Foundations of Systems Biology. www.balliol.ox.ac.uk/BII/projects/biology. A web introduction for this seminar at Balliol College, University of Oxford, during Trinity Term 2011. Conveners are Denis Noble, Eric Werner, Tom Melham, and Jonathan Bard. We note as a capsule of the on-going scientific shift from things, once all found, to fathom equally real, informative interconnections between them.

Biology is at a crossroads. We have realized that it is not genes but networks that create change and generate function – networks so rich and complex that understanding them requires mathematical and computer science methods, not only molecular biology and bioinformatics. The early promise of the genomic era has not been realized. Even the central dogma has come into question. Systems biology is now an integral part of biology proper – modelling and simulation are standard practice. But its fundamental concepts and methods are far from settled. This seminar will engage in an interdisciplinary dialogue to discover, elaborate, and clarify the fundamental concepts underlying modern biology – its biological presuppositions, its formal models, and its mathematics. Clarifying the theoretical foundations of a field can have profound implications for its science, its practice and its predictions.

Institute for Systems Biology. www.systemsbiology.org. This Seattle, WA facility, founded in 2000 by Leroy Hood and colleagues, has become a world leader in biological and medical research. The 21st century turn in the life sciences from constituent reduction alone, albeit a necessary phase, to additionally recognize an intrinsic complex, dynamic organization for genome and organism, is epitomized by its many laboratory programs. At this website, where the quotes are from, can be found much information about the systems biology approach and leading edge faculty group projects

As scientists have developed the tools and technologies which allow them to delve deeper into the foundations of biological activity — genes and proteins — they have learned that these components almost never work alone. They interact with each other and with other molecules in highly structured but incredibly complex ways, similar to the complex interactions among the countless computers on the Internet. Systems biology seeks to understand these complex interactions, as these are the keys to understanding life.

In summary, systems are comprised of parts which interact. The interaction of these parts gives rise to new properties and functions which are key to the system. We call these new properties and functions "emergent properties". Because emergent properties are the result of interactions between the parts, they can not be attributed to any single parts of the system. This makes systems irreducible. A system is unlikely to be fully understood by taking it apart and studying each part on its own. To understand systems, and to be able to fully understand a system's emergent properties, systems need be studied as a whole. This recognition that complex systems, especially life, are truly understood from knowledge of the interactions of their component parts is fundamental to systems biology.

Laboratory of Living Matter. http://lab.rockefeller.edu/leibler/. An endeavor hosted at New York City’s Rockefeller University (science for the benefit of humanity) and directed by Stanislas Leibler. Click on Publications for pithy papers.

Analysis of Biological Networks: In recent years molecular biology has moved away from the study of individual components towards the study of many interacting components. The "systemic" approach seeks an appropriate, and if possible, quantitative description of cells and organisms. Both the theoretical and experimental methods necessary for such studies still need to be developed. We are far from understanding even the simplest collective behavior of biomolecules, cells or organisms.

Aderem, Alan. Systems Biology. Cell. 121/511, 2005. A Commentary on the “iterative” reassembly of the evolutionary nest of life as huge databases of biological data are digitally generated and placed online.

There are three basic concepts that are crucial to understanding complex biological systems: they are emergence, robustness, and modularity. (511) To practice systems biology, one must capture and integrate global sets of biological data from as many hierarchical levels of information as possible. These could include DNA sequences, RNA and protein measurements, protein-protein and protein-DNA interactions, biomodules, signaling and gene regulatory networks, cells, organs, individuals, populations, and ecologies. (511)

Alberghina, Lilia and Hans Westerhoff, eds. Systems Biology: Definitions and Perspectives. Berlin: Springer, 2005. A synopsis of the active adjustment from part to relation, from isolated genetic molecule to relevant, fluid environment, in the study of life, which is seen to involve self-organization, networks and hierarchies.

Almass, Eivind. Biological Impacts and Context of Network Theory. Journal of Experimental Biology. 210/9, 2007. In this special issue on Post-Genomic and Systems Approaches to Comparative and Integrative Physiology, a survey of how proteins and cells are interactively graced and can be modeled by the newly discovered class of nested scale-free modular networks.

Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. The recent availability of large-scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory networks, signal transduction networks, protein interaction networks and metabolic networks will dramatically enhance our understanding of cellular function and dynamics. (1548)

Alon, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. London: Chapman and Hall, 2019. Cell biologist Uri Alon is a cell biologist in the Physics of Complex Systems group at the Weizmann Institute of Science, Israel. The work is a second edition from 2007 which was a leading textbook for the fiels. See also Alon’s entry in Universal Principles.

Written for students and researchers, the second edition of this best-selling textbook continues to offer a clear presentation of design principles that govern the structure and behavior of biological systems. It highlights simple, recurring circuit elements that make up the regulation of cells and tissues. Rigorously classroom-tested, this edition includes new chapters on exciting advances made in the last decade.

Alvarez-Buylla, Elena, et al. Systems Biology Approaches to Development beyond Bioinformatics. BioScience. 66/5, 2016. We cite this contribution by Universidad Nacional Autonoma de Mexico (UNAM) researchers (bio notes below) as a good example of the growing realizations of both an independently existing, universally effective self-organizing complex network phenomena and their procreative influence on flora and fauna organisms and environments. We excerpt the author’s biographic interests, which help evoke this major change in evolutionary thinking and synthesis.

Biological systems are complex, stochastic, and nonlinear; therefore, understanding how genes map to phenotypes remains a challenge. A complex-systems mechanistic approach, emphasizing relations over associations, is required for understanding the emergence of cell differentiation and morphogenesis during development. An increasing number of contemporary studies that integrate biological data into dynamic, nonlinear, and stochastic models are providing novel explanations for development. Unfortunately, the adaptation of the biological research tradition to such quantitative and interdisciplinary approaches is not straightforward. In an attempt to contribute to this necessary transition, drawing mainly on our own studies as examples, we present here a nontechnical overview article of how such models are helping unravel the emergence of cell differentiation, pattern formation, and morphogenesis. (Abstract)

Elena Alvarez-Buylla is a developmental and evolutionary biologist who has combined experimental and theoretical approaches to understanding development. She has used plants as experimental systems to validate nonlinear and stochastic models to uncover the necessary and sufficient restrictions that emerge from the dynamic interactions of genetic and nongenetic components during cell differentiation and their transitions during normal and altered morphogenesis. She also devotes important time and efforts to the preservation of biocultural diversity in Mexico, the promotion of agroecology and the defense of food sovereignty, and a scientific practice with a strong ethical and socioenvironmental commitment.

Jose Davila-Velderrain is interested in the study of cellular behavior and evolution as the inevitable manifestation of the intrinsic nonlinear and stochastic properties of natural systems and in uncovering patterns supporting this perspective through the analysis of high-throughput molecular data. He also attempts to understand and predict human collective behavior and cultural dynamics through the lens of complex-systems science.

Juan Carlos Martínez García is a specialist in mathematical control theory focused on systems-biology approaches to study the complex regulatory dynamics in cell biomolecular circuits. He is also interested in the study of the parallelism between designed systems and natural ones as far as functional performance and robustness are concerned. He also collaborates in various projects involving the interaction between art and science.

Alvarez-Ponce, David, et al. Gene Similarity Networks Provide Tools for Understanding Eukaryote Origins and Evolution. Proceedings of the National Academy of Sciences. 110/6624, 2013. Biologists Alvarez-Ponce and James McInerney, National University of Ireland, Maynooth, with Philippe Lopez and Eric Bapteste, Universite Pierre et Marie Curie, Paris, contend that it is the pervasive, nested node and link topologies being found to actually distinguish genomes that are transcribed and repeated, than just discrete, disparate nucleotide molecules.

Arkin, Adam and David Schaffer. Network News: Innovations in 21st Century Systems Biology. Cell. 144/844, 2011. In this Review of Systems Biology issue, University of California, Berkeley, bioengineers survey the past 10 years and before to laud its new promise to reveal the dense complexities of “predictive genome-scale regulatory and metabolic models of organisms.” The next decade should get us closer, in a mechanistic way, to resolving the question “what is life?” But per the quote, “thinking machines” is surely the wrong answer.

Systems biology aims to understand how individual elements of the cell interact to generate behaviors that allow survival in changeable environments and collective cellular organization into structured communities. Ultimately, these cellular networks to form large-scale ecologies and thinking machines, such as humans. (844)

Arthur, Wallace. The Emerging Conceptual Framework of Evolutionary Developmental Biology. Nature. 415/757, 2002. In the latter 19th century evolution and embryology were unified as a subject but went separate ways as quantitative studies in each field diverged. Around 1980 a reconvergence began with the discovery of the homeobox gene complex and of epigenetic influences. Arthur provides a lucid review of how developmental and phylogenetic findings now reinforce each other. What results is a reciprocity of discrete gene and field or topological features along with a “broadly recapitulatory” parallel between individual ontogenetic maturation and the long course of evolution.

Looked at in one way, development is programmed by genes. But this is too limited a view. There is a complementary process, the epigenetic programme, through which genes are controlled by developmental agents of diverse kinds, including transcription factors and secreted morphogens. (759)

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