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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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V. Systems Evolution: A 21st Century Genesis Synthesis

A. Systems Network Biology and Genetics

This section will report the significant turn in genetics and biology since the 2001 Human Genome Project to reassemble the many prior biomolecule pieces and components into an integral whole. With their initial identity in place, the presence equally real interrelations in between, and the information they carry and convey, can be admitted. By this inclusive paradigm, the dynamic web of life can shed a mechanistic, machinery cast for a phenomenal emergent vitality. Circa 2015 going forward, the pervasive network revolution joins and deepens the systems approach by which further to reunite animate phenomena. Regnant 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.

Circa 2015 going forward, the network revolution has now fully reached this systems approach to reunite biological phenomena. The interactive holistic relations necessary to quantify a full articulation of life’s wholes within wholes are now bolstered by the same multiplex topologies and dynamics as being found everywhere else from neurome to quantome. See for example Network Science of Biological Systems at Different Scales by Marko, Gosak, et al (2017) and contributions throughout topical sections which document this turn and expansion of the Systems Biology movement.

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. London: Chapman and Hall, 2006. Cell biologist Alon is associated with the Physics of Complex Systems group at the Weizmann Institute of Science, Israel. See also Alon’s note in Universal Principles.

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)

Asgari, Ehsaneddin and Mohammad Mofrad. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics. PLoS One. November, 2015. A publication from M. Mofrad’s UC Berkeley Molecular Cell Biomechanics laboratory which is guided by a premise that this anatomy and physiology may be best studied if nature and life is seen to be suffused with a linguistic quality. Herein the synthesis is broached by way of deep neural learning, textual analysis, and bioinformatics. The second quote is from Asgari’s Life Language Processing web page. Search the Iranian-American authors, and other lab members, for additional papers.

Nature uses certain languages to describe biological sequences such as DNA, RNA, and proteins. Much like humans adopt languages to communicate, biological organisms use sophisticated languages to convey information within and between cells. Inspired by this conceptual analogy, we adopt existing methods in natural language processing (NLP) to gain a deeper understanding of the “language of life” with the ultimate goal to discover functions encoded within biological sequences. (2) Distributed representation has proved one of the most successful approaches in machine learning. The main idea in this approach is encoding and storing information about an item within a system through establishing its interactions with other members. Distributed representation was originally inspired by the structure of human memory, where the items are stored in a “content-addressable” fashion. Content-based storing allows for efficiently recalling items from partial descriptions. Since the content-addressable items and their properties are stored within a close proximity, such a system provides a viable infrastructure to generalize features attributed to an item. (2)

Nature uses certain languages to describe biological sequences such as DNA, RNA, and proteins. Much like humans adopt languages to communicate, biological organisms use sophisticated languages to convey information within and between cells. Inspired by this conceptual analogy, we adopt existing methods in natural language processing (NLP) to gain a deeper understanding of the "language of life'' with the ultimate goal to discover functions encoded within biological sequences. The language of life is still far from well-understood and could benefit from the modern techniques of computational linguistics and machine learning. In Life Language Processing (LLP) project we are interested in statistical linguistic modeling of life sequences. (www.llp.berkeley.edu)

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