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

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

Ceska, Milan and David Safranek, eds. Computational Methods in Systems Biology. International: Springer, 2018. The Proceedings of the 16th International Conference on this title subject (CMSB) held in Brno, Czech Republic, in September. Some entries are Deep Abstractions of Chemical Reactions Networks, Synthesis for Vesicle Traffic Systems, and Experimental Biological Protocols with formal Semantics. May one then wonder, what grand phenomena, of we are as yet unawares, is our worldwise sapiensphere coming upon, trying to learn, provide a cosmic self-describe? What is going on, what language is natural genesis written in? Who are we phenomenal human beings to be able to do this, for what purpose? Does a universal procreation want us to intentionally take from here?

The 15 full and 7 short papers presented together with 5 invited talks were selected from 46 submissions. Topics of interest include formalisms for modeling biological processes; models and their biological applications; frameworks for model verification, validation, analysis, and simulation of biological systems; high-performance computational systems biology; parameter and model inference from experimental data; automated parameter and model synthesis; model integration and biological databases; multi-scale modeling and analysis methods; design, analysis, and verification methods for synthetic biology; methods for biomolecular computing and engineered molecular devices.

Chuang, Han-Yu, et al. A Decade of Systems Biology. Annual Review of Cell and Developmental Biology. 26/23.1, 2010. With coauthors Matan Hofree and Trey Ideker, University of California, San Diego scientists provide a chapter survey that reviews the integral bioinformatic approaches and advances that life’s dynamic connectivities, here dubbed the “translational sciences.”

Civelek, Mete and Aidons Lusis. Systems Genetics Approaches to Understand Complex Traits. Nature Reviews Genetics. 15/1, 2014. UCLA geneticists and microbiologists provide a succinct entry to this paradigm shift as separate nucleotides and biomolecules now become interconnected and networked within broadly conceived genomes.

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease. (Abstract)

Systems genetics shares with systems biology a holistic, global perspective. The typical strategy in systems biology is to perturb a system, monitor the responses, integrate the date and formulate mathematical models that describe the system. Systems genetics is a particular type of systems biology, in which genetic variation within a populations is used to perturb the system. Ultimately, the goal of systems genetics is to understand the broad molecular underpinnings, such as genetic architecture and intermediate physiological phenotypes, of complex traits, including diseases. (35)

Clarke, Declan, et al. Novel Insights through the Integration of Structural and Functional Genomics Data with Protein Networks. Journal of Structural Biology. 179/3, 2012. With coauthors Nitin Bhardwaj and Mark Gerstein, Yale University systems geneticists, whose laboratory is an ENCODE Project research site, offer another take on the native presence, beyond discrete nucleotide or codons, of interconnective networks composed of elemental “node” and cross-linking “edge” phases. These dual complements, (DNA/AND), then constitute and distinguish a whole genome. The article details a further finesse of network topologies, whereof nodes themselves are often nested nets within encompassing scale-free (epi)genomic hierarchies.

In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein–protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or “nodes”) themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network’s nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. (Abstract, 320)

Biological networks are conventionally represented as maps of nodes and edges, wherein nodes represent biological entities (such as a gene, protein, or mRNA) and edges represent interactions between these entities (such as regulation or physical association). (320)

Cohen, Irun. Tending Adam’s Garden: Evolving the Cognitive Immune Self. San Diego: Academic Press, 2000. An engaging, erudite entry by the Weizmann Institute of Science immunologist to the present reconception of the immune system as an ecology of nonlinear networks that dynamically organize themselves. By so doing, immune responses become cognitive and decisive in kind, similar in activity to self-organizing brains. Just as our neural capacity creates itself out of on-going experience, so somatic immune reactions arise from its vicarious environment. So then as for Adam, our own self makes up its unique individuality. The quote is from the topical headings for a ‘Self-Organization’ section. (For Cohen’s recent work see Explaining a Complex Living System: Dynamics, Multi-scaling and Emergence, with David Harel in Journal of the Royal Society Interface 4/175, 2007.)

Cognitive systems organize themselves as they evolve; what is self-organization and learning? Biologic evolution is the self-organization of species. Individual and cultural self-organization is somatic. Complexity is progressive. (82)

Cornish-Bowden, Athel. Putting the Systems Back into Systems Biology. Perspectives in Biology and Medicine. 49/4, 2006. Although this phrase is much bandied about today in biological science, in actuality, as researchers admit, the reductive project goes on as usual. To truly conceive and carry out imperative systemic studies of life, we need to recall the earlier contributions of the philosophical biologists Ludwig von Bertalanffy and Robert Rosen.

Cornish-Bowden, Athel, et al. Beyond Reductionism: Metabolic Circularity as a Guiding Vision for a Real Biology of Systems. Proteomics. 7/6, 2007. A typical article from a special issue on Systems Biology which the long quote well conveys.

The definition of life has excited little interest among molecular biologists during the past half-century, and the enormous development in biology during that time has been largely based on an analytical approach in which all biological entities are studied in terms of their components, the process being extended to greater and greater detail without limit. The benefits of this reductionism are so obvious that they need no discussion, but there have been costs as well, and future advances, for example, for creating artificial life or for taking biotechnology beyond the level of tinkering, will need more serious attention to be given to the question of what makes a living organism living. According to Robert Rosen's theory of metabolism-replacement systems, the central idea missing from molecular biology is that of metabolic circularity, most evident from the obvious but commonly ignored fact that proteins are not given from outside but are products of metabolism, and thus metabolites. Among other consequences, this implies that the usual distinction between proteome and metabolome is conceptually artificial - however useful it may be in practice - as the proteome is part of the metabolome. (839)

Coruzzi, Gloria and Rodrigo Gutierrez, eds. Plant Systems Biology. Oxford: Oxford University Press, 2009. Volume 35 of Annual Plant Reviews that dutifully covers the whole range of this budding approach from generic complexity principles by Reka Albert and Sarah Assmann, to their active presence from fauna genomes to the range of epigenomes, proteomes, metabolomes, and so on. Another integrative paper “Perspectives on Ecological and Evolutionary Systems Biology” by Christina Richards, et al continues into bioregional realms.

Danchin, Etienne, et al. Beyond DNA: Integrating Inclusive Inheritance into an Extended Theory of Evolution. Nature Reviews Genetics. 12/7, 2011. From our retrospect, genetic studies in the later 20th century first focused on identifying and sequencing molecular nucleotides. With this in place, a systems approach is now finding equally significant network relations between these genomic components. But as a result, the agency of “hereditary” is being widely extended from epigenetic to cultural reaches. With coauthors Frances Champagne, Anne Charmantier, Alex Mesoudi, Benoit Pujol, and Simon Blanchet, a new generation of researchers from France, England, and the U.S. scope out and synthesize this expansion, as the Abstract attests. With 162 references, the paper, available on Mesoudi’s website, provides a good summary of this epochal 21st century revision and expansion. See also Danchin's 2013 paper "Avatars of Information: Towards an Inclusive Evolutionary Synthesis" in Trends in Ecology and Evolution (Online March).

Many biologists are calling for an ‘extended evolutionary synthesis’ that would ‘modernize the modern synthesis’ of evolution. Biological information is typically considered as being transmitted across generations by the DNA sequence alone, but accumulating evidence indicates that both genetic and non-genetic inheritance, and the interactions between them, have important effects on evolutionary outcomes. We review the evidence for such effects of epigenetic, ecological and cultural inheritance and parental effects, and outline methods that quantify the relative contributions of genetic and non-genetic heritability to the transmission of phenotypic variation across generations. These issues have implications for diverse areas, from the question of missing heritability in human complex-trait genetics to the basis of major evolutionary transitions. (Abstract, 475)

Daneker, Mitchell, et al. Systems Biology: Analysis and Parameter Identification via Informed Neural Networks. arXiv:2202.01723. We cite this entry by University of Pennsylvania, and Brown University “biomolecular engineers” as an example of how this integral approach is also meriting from these cerebral AI learning methods.

The dynamics of systems biological processes are usually modeled by ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from noisy and sparse measurements. Here, we make avail of systems-biology informed neural networks for parameter estimation by incorporating the ODEs content into them. To complete the workflow of system identification, we describe a structural and practical analysis to identify and study salient features. We use an ultridian endocrine model for glucose-insulin interaction to demonstrate these methods and their implementation. (Excerpt)

Davidson, Eric. Evolutionary Bioscience as Regulatory Systems Biology. Developmental Biology. 357/1, 2011. The senior Caltech cell biologist makes a summary statement, that, per the second quote, can well define this 21st century integral revision. Still underway, its import is to set aside the 1950s modern synthesis of gene mutations and population drift for a novel evolution due more to the newly found prevalence of layered networks that compose genomes. It is changes in these varying systemic interconnections between nucleotides that actually influence life’s long development.

At present several entirely different explanatory approaches compete to illuminate the mechanisms by which animal body plans have evolved. Their respective relevance is briefly considered here in the light of modern knowledge of genomes and the regulatory processes by which development is controlled. Just as development is a system property of the regulatory genome, causal explanation of evolutionary change in developmental process must be considered at a system level. Here I enumerate some mechanistic consequences that follow from the conclusion that evolution of the body plan has occurred by alteration of the structure of developmental gene regulatory networks. The hierarchy and multiple additional design features of these networks act to produce Boolean regulatory state specification functions at upstream phases of development of the body plan. These are created by the logic outputs of network subcircuits, and in modern animals these outputs are impervious to continuous adaptive variation unlike genes operating more peripherally in the network. (Abstract)

The first (standard view) is the classic neo-Darwinian concept that evolution of animal morphology occurs by means of small continuous changes in primary protein sequence which in general require homozygosity to effect phenotype. The second paradigm holds that evolution at all levels can be illuminated by detailed analysis of cis-regulatory changes in genes that are direct targets of sequence level selection, in that they control variation of immediate adaptive significance. Both approaches often focus on changes at single gene loci, and both are framed within the concepts of population genetics. An entirely different way of thinking is that the evolution of animal body plans is a system level property of the developmental gene regulatory networks (dGRNs) which control ontogeny of the body plan. (35)

Del Moral, Raquel, et al. From Genomics to Scientomics: Expanding the Bioinformation Paradigm. Information. 2/4, 2011. With coauthors Monica Gonzalez, Jorge Navarro, and Pedro Marijuan of the Bioinformation and Systems Biology Group, Instituto Aragonés de Ciencias de la Salud, Zaragoza, Spain, an article from a special issue of Selected Papers from FIS (Frontiers of Information Science) 2010 Beijing. The online journal, conference, and this paper are revolutionary signs realization, after science fills in all the parts, and their complex connections, that what is actually going on is nature’s persistent, creative cross-communication. As the authors note, not only has this view take hold in molecular genetics, but similarly across neuronal studies, and on to cultural discourse, as a distinctive attribute which is informational in kind. In regard, the “–omics” connotation can be carried into every realm, whence worldwide scientific collaboration becomes akin to genomic activity. This newly perceived quality appears in the same, repetitive fashion from proteins to people, which begs the presence and discovery of the innate, parent to child, genetic code of a genesis universe.

Contemporary biological research (particularly in systems biology and the “omic” disciplines) is factually answering some of the poignant questions associated with the information concept and the limitations of information theory. Here, rather than emphasizing and persisting on a focalized discussion about the i-concept, an ampler conception of “informational entities” will be advocated. The way living cells self-produce, interact with their environment, and collectively organize multi-cell systems becomes a paradigmatic case of what such informational entities consist of. Starting with the fundamentals of molecular recognition, and continuing with the basic cellular processes and subsystems, a new interpretation of the global organization of the living cell must be assayed, so that the equivalents of meaning, value, and intelligence will be approached along an emerging “bioinformational” perspective.

The great advantage fuelling the expansion of the bioinformation paradigm is that, today, cellular information processes may be defined almost to completion at the molecular scale (at least in the case of prokaryotic cells). Concretely, the crucial evolutionary phenomenon of protein-domain recombination—knowledge recombination—will be analyzed here as a showcase of, and even as a model for, the interdisciplinary and multidisciplinary mixing of the sciences so prevalent in contemporary societies. Scientomics will be proposed as a new research endeavor to assist advancement. Informationally, the “society of enzymes” appears as a forerunner of the “society of neurons”, and even of the “society of individuals”. (651, Abstract)

The creation of knowledge is always an informational process, ultimately derived from knowledge recombination processes in the cerebral “workspace” of individuals. Reliable knowledge mediates action/perception cycles of individuals and prolongs them, supra-individually, making possible a more cogent and integrated closure at the social scale. The scientific method itself appears from this perspective as the conditions to be met for a coherent decomposition of problems by communities of problem-solvers whose workings are separated in time and space. Standards, measurements, mathematical operations, formalizations, and so on, become ways and means to extract mental operations out of the individual’s nervous system and directly interconnect perceptions and actions at a vast institutional-social scale. We really see a “collective nervous system”, a “social workspace” in action. (663)

It can be argued that the growth of informational complexity of cells, nervous systems, and societies along their respective evolutionary, ontogenetic, and historical trajectories has been based on the cumulative consequences of knowledge recombination phenomena. Very limited “agents” are capable of developing a collective processing that goes far beyond the computing bounds of each single agential entity; and their collective processing includes the dynamics of knowledge recombination in all the different information realms. It is amazing that among the procedures to create novelty in those repositories, a central strategy becomes the swapping of knowledge “chunks” from one construct to another, so that new cognitive constructs emerge and new adaptive capabilities are deployed. We have seen how this occurs evolutionarily in cellular protein-domain recombination, and how mental constructs seem to be handled in a similar way. From a highly macroscopic point of view, the different disciplines would also partake in this common strategy of knowledge recombination. (667-668)

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