The OneNet Theory of Life

Yongqun "Oliver" He

The author first proposed the One Network (abbreviated as "OneNet") Theory of Life (for one organism) in a peer-reviewed article published in the journal Expert Reviews in Vaccines in June 2014 (He, 2014), and the author further extended the theory in another article in Current Pharmocology Reports in 2016 (He, 2016). The OneNet theory is targeted to explain and seek a deep and comprehensive understanding of various phenomena of the life of an organism (for example, a single cell bacterium like E. coli and a multi-cell organism like human).

Basically, the OneNet theory states that the (whole) life of an organism is a single complex and dynamic network (i.e., "OneNet'), and the OneNet has its own features. This document is generated to provide more details about this theory:

Table of Contents

1. Tenets of the OneNet theory
2. Derivation of the OneNet theory
3. Hypotheses derived from the OneNet theory
4. Representation and study of the OneNet interaction networks
5. Applications of the OneNet theory
6. Related References
7. OneNet theory News
8. Notes about this webpage

1. Tenets of the OneNet Theory of Life:

The OneNet theory states: The whole process of the life of an organism is a single complex and dynamic network (called “OneNet”).

The OneNet has five characteristics represented by five tenets:

• The OneNet blueprint is stored in the genotype of the organism. - OneNet Blueprint
• The OneNet starts at the moment when the first cell of the organism forms. - OneNet Start
• OneNet is inherently motivated for the organism to survive, develop, reproduce (when ready), and live a high multilevel quality of life in its physical and social environments. - OneNet Motivation
• The OneNet of temporal interactions between the genetic materials and their environments determine the dynamic phenotypes of the life. - OneNet Dynamics
• An organism with its expressed OneNet profile more adaptive to an environment is advantaged to survive, develop, replicate, and live a better life in the environment. - OneNet Outcome (previously called OneNet Effectiveness)

The tenets of OneNet Blueprint, Start, and Dynamics were defined and discussed in the original paper (He, 2014). Described in detail in a 2016 paper (He, 2016), the OneNet Effectiveness tenet was later added to provide a mechanism to measure the effectiveness (or outcome) of the complex OneNet process (also see its explanation below). OneNet Motivation is newly proposed, briefly discussed below, and detailed in a manuscript being prepared.

2. Derivation of the OneNet theory:

The development of the OneNet theory was inspired from many existing theories, including:

• The Evolutionary Synthesis Theory (Futuyma, 1997). This is basically the contemporary evolutionary theory that studies the distinction and relationship between genotype and phenotype. The genotype provides a blueprint for an organism's development and growth, and the phenotype demonstrates the physical manifestation of a genotype. The environment will also influence the phenotype outcomes.
• The Cell Theory. The Cell Theory states that all organisms include one or more cells, the cell is the most basic unit of structure, function, and organization in any organism, and all cells are arisen from pre-existing, living cells.
• The Immune Network Theory. The theory explains the mechanism of the adaptive immune system. This theory indicates that the immune system is a network with the components connected to each other through special V-V interactions where V represents variable V region in an immune antibody.
• The Immune Response Gene Network Theory (Poland et al, 2009). The theory was initially proposed by Dr. Gregory A. Poland from Mayo Clinic. This theory states that host response to a vaccine is induced by the interactions driven and between host genes.
• Darwin's Theory of Evolution: This theory states that populations evolve over the course of generations through a process of natural selection.
• Maslow's motivation theory: According to Maslow's hierarchy of needs theory, people are motivated by unsatisfied needs, including Physiological needs (hunger, thirst, sleep, etc.), Safety needs (physical and psychological security, shelter, health, etc.), Social needs (love, affection, friendship, etc.), Esteem needs (recognition, achievement, etc.), and Self-actualization needs (need for creativity, self-fulfillment, integrity, etc.).

While the Evolutionary Synthesis Theory and the Cell Theory introduce the general principles on how cells and organisms evolve, these theories do not focus on uncovering the step-by-step molecular interaction network mechanisms of the cellular and organismic processes in one single organism's life. The Immune Network Theory and the Immune Response Gene Network Theory provide more specific mechanistic explanation on how a host organism responds to different triggers using its immune system. However, these two theories do not consider other host systems (e.g., developmental system) as integrated parts of the whole organism and its dynamic life.

OneNet Motivation states that the dynamic OneNet interactions are not operated in random directions. Instead, an organism is always motivated to engage in behaviors that maximize its genetic fitnesses in an environmental and social context. This tenet links and integrates basic molecular and cellular mechanisms in an organism with cognitive motivations and psychology.

OneNet Outcome states that an organism with its expressed OneNet profile more adaptive to an environment is advantaged to survive, develop, replicate, and live a better life in the environment. The terms effectiveness, efficacy, and efficiency mean different things in different settings. In medicine, effectiveness relates to how well a treatment works in practice, while efficacy measures how well it works in a well-controlled clinical trials or laboratory studies. In physics, an effective theory is a framework intended to explain an observed effect without claiming underlying unobserved processes. The OneNet theory adopts the definition of "effectiveness" provided in the uncertainty representation and reasoning evaluation framework (URREF). In the URREF ontology, the “effectiveness” relates to a system’s capability to produce an effect, and effectiveness includes: (i) “efficiency”: doing a thing in the most economical way, (ii) “efficacy”: getting a thing done (i.e., meeting a target or desire); and (iii) “correctness”: doing a "right" things, i.e., setting a right target to achieve an overall goal (the effect). Similarly, OneNet effectiveness represents the capability of an organism to produce an effect, and it may include different levels, e.g., efficiency, efficacy, and correctness.

The author views this theory novel because this theory is first proposed in this coherent and integrative way to explain life with one single statement about the OneNet and different tenets to explain different aspects of the OneNet. This theory appears simple; however, simplicity is a good thing. On the other hand, this simple theory can be used to explain numerous life questions. For example, what is the definition of the environment introduced in the theory? The environment of genetic materials here is quite different from the typical sense of environment. The environment here is defined as the environment to the genetic materials, meaning anything outside the genetic materials, no matter it is inside or outside the cell that holds the genetic materials. If the environment factor is outside the cell, it may influence the genetic material expression through a signal pathway(s). The environment outside an organism will also indirectly influence the gene expression through specific pathways. Derived from the OneNet theory, we can also generate many basic but foundamental hypotheses as examplified below.

3. Hypotheses derived from the OneNet theory:

The OneNet theory can be used to derive hypotheses such as the following two as laid out in the (He, 2016):

OneNet Blueprint-Manifestations Hypothesis: "One organism (e.g., human or E. coli) uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions."

This hypothesis appears against the obvious experimental observations that human uses different mechanisms against different vaccine/drug administrations. However, there is no conflict since there are two different types of mechanisms: one is the "OneNet Blueprint" design of mechanisms, the other are "OneNet Manifestations", i.e., the manifestations (or expressions) of the OneNet blueprint given specific conditions.

The author views the "OneNet Blueprint" and "OneNet Manifestation" are two important concepts (or entities) in the OneNet theory. The “OneNet Blueprint-Manifestations” hypothesis provides a framework that integrates various observed, disintegrated, and often appearingly conflicting mechanisms under the integrative blueprint-manifestation system.

Hypothesis 2 - Multi-cell Blueprint Sharing and Manifestations Collaboration: All cells with the same genotype in a multi-cell organism share the same OneNet blueprint mechanism although the blueprint manifestations in different cells may differ given different conditions, and the OneNet blueprint includes mechanistic design for how different human cells interact with each other to form the physical human body and how these different cells collaborate in response to various environmental factors.

The first hypothesis is about the whole organism, no matter it is a single-cell or multi-cell organism, and it is specifically about the basic relation between OneNet blueprint and manifestations of the whole organism. The second hypothesis is for multi-cell organisms (e.g., human), specifically, for how different cells inside a multi-cell organism differentiate and collaborate to support the physical body establishment and collaborative response to various triggers such as vaccinations, drug administrations, infections, and stresses.

One pitfall is the probable genetic variations in the life of an organism. Although it does not occur ofen, it does occur and may occur more frequently given specific conditions (e.g., radiation). Such genetic variation is indeed a part of the OneNet manifestation. We will need to closely monitor such genetic variation and identify how such variations may affect the life process afterwards.

Based on the above two hypotheses, our long-term goal is to fully identify the comprehensive OneNet blueprint mechanism for an organism and use it to predict specific OneNet manifestation profiles given various conditions. It is also noted that individuals under the same species (e.g., human) are different. This issue is also discussed in the paper (He, 2016).

4. Representation and study of the OneNet interaction networks:

As the Periodic Table of Chemical Elements has been used to represent different chemical elements and their properties and relations, I proposed to use ontology-based strategy to logically represent different components and stages of the "OneNet" of individual organism's life (He, 2014). In a sense, the periodic table of chemical elements is a good ontology. However, the OneNet of an organism's life is much more complicated, and it cannot be represented using a simple table.

A more complex ontology representation is required to represent such OneNet. Hundreds of biological ontologies have been developed. To ensure data integration, there is a requirement of well structured ontology representation systems. The Basic Formal Ontology (BFO) is an upper level ontology that has been used by over 100 different domain specific ontologies, including many under the OBO Foundry library. By using ontologies, esp. those in the OBO foundry library and those aligned with BFO, we may be able to generate a huge ontology framework to fully represent various aspects of the "OneNet" of individual organism's life.

Based on the ontology strategy as described above, the author proposed the generation of an Interaction Network Ontology (INO) (Hur et al, 2015), a species-neural ontology that represents OneNet-related terms that are shared by different species. Species-specific ontologies can then be generated as extensions of INO. Particularly, we initiated the development of a Human Interaction Network Ontology (HINO) (He and Xiang, 2013). While these two ontologies are still immature, they will be continuously developed to achieve the goals. Such ontologies should be developed as open source, and anyone can join their development and use them freely. Such ontologies can be utilized in different applications.

In 2018, the author developed a Life Ontology (LifO), which can be more directly used to represent the life, different life tenets, and how various life elements are interacted in a dynamic and complex network pattern.

Mathematical and statistical approaches can also be used to study the OneNet interaction networks of an organism's life. This is an ongoing research, and more information will be provided later. The author also welcomes collaborations in these areas.

It is noted that we just started to understand various molecular interaction pathways. There is still a long way to go before we can fully understand every interaction and pathway of the OneNet networks of various organisms starting from the moment when a cell is formed (note: it is fertilized for human).

5. Applications of the OneNet theory:

The OneNet theory was indeed developed through the study of vaccine immune mechanisms with a goal to achieve positive intended vaccine efficacy and prevent negative unintended vaccine adverse events (See the references listed below). However, the theory should be applicable for studying other aspects of an organism's life.

The OneNet theory provides a framework for systematically studying what happens once a life has formed. OneNet integrates different development and growth stages of the whole life of an organism. The OneNet theory allows the deep explanation and analysis of various genotype-phenotype linkages such as the association between vaccination and vaccine adverse events. Such a theory can be used to explain and study the life of a single cell bacterium like E. coli or a multi-cell organism like human. See more details in the article (He, 2014).

The author anticipates that the OneNet theory will be used in many different applications. More hard work will be needed.

6. Related References:

OneNet Introduction:

• He Y. Ontology-based vaccine and drug adverse event representation and theory-guided systematic causal network analysis towards integrative pharmacovigilance research. Current Pharmacology Reports. 2016 Jun;2(3):113-128. PMID: 27458549. PMCID: PMC4957564 [Available on 2017-06-01]. DOI: 10.1007/s40495-016-0055-0.
• He Y. Ontology-supported research on vaccine efficacy, safety, and integrative biological networks. Expert Reviews in Vaccines. 2014 Jul;13(7):825-41. doi: 10.1586/14760584.2014.923762. Epub 2014 Jun 7. PMID: 24909153. PMCID: PMC4815432.
• He Y, Ong E, Xie J. Integrative representations and analyses of vaccine-induced intended protective immunity and unintended adverse events using ontology-based and theory-guided approaches. Global Vaccines and Immunology. 2016. 1 (2): 37-39. DOI: 10.15761/GVI.1000110. [PDF] PMID: 27868103. PMCID: PMC5111632.

References for OneNet theory derivation:

• Futuyma DJ: Evolutionary Biology, 3rd ed: Sinauer Associates; 1997. [Amazon.com link]
• The Cell Theory: http://en.wikipedia.org/wiki/Cell_theory.
• The Immune Network Theory: http://en.wikipedia.org/wiki/Immune_network_theory.
• Poland GA, Ovsyannikova IG, Jacobson RM: Application of pharmacogenomics to vaccines. Pharmacogenomics 2009, 10(5):837-852. PMID: 19450131.
• Darwin's Theory on the Evolution of Species: Darwin's book (6th edition) in Wikisource, Wiki: On the Origin of Species.
• Maslow, A.H. A theory of human motivation. Psychological Review 50, 370-396 (1943).

Towards ontological OneNet represenations:

• He Y. LifO: Life Ontology. https://github.com/lifeontology/lifo. Visualize it on NCBO BioPortal.
• Hur J, Özgür A, Xiang Z, He Y. Development and application of an Interaction Network Ontology for literature mining of vaccine-associated gene-gene interactions. Journal of Biomedical Semantics. 2015, 6:2. doi:10.1186/2041-1480-6-2.
• He Y, Xiang Z. HINO: a BFO-aligned ontology representing human molecular interactions and pathways. arXiv. 2013. arXiv:1311.3355.

6. OneNet theory News:

• 4/21/2021: Changed the name "OneNet Effectiveness" to "OneNet Outcome".
• 3/11/2018: Posted the first version of the Life Ontology (LifO) to GitHub, and submitted it to NCBO BioPortal.
• 3/2/2017: Oliver presented a talk titled "Ontology-based Biomedical Data Standardization, Integration, and Statistical Analysis" at the Statistics Seminar, Department of Mathematics in the University of Maryland, College Park. In this presentation, Oliver also described and discussed the OneNet theory.
• 12/20/2016: Oliver presented a BD2K-LINCS data science research (DSR) Webinar presentation titled "Cell Line Ontology-based Standardization, Integration and Analysis of LINCS Cell Lines" ( Available on YouTube: https://www.youtube.com/watch?v=mKQNUyHDeG8). In thie last part of this presentation, Oliver briefly introduced the OneNet theory (including the OneNet blueprint-manifestations hypothesis, and a related Christmas Lights model), and a derived cell line OneNet hypothesis, and how they can be used to systematically represent and analyze cell responses to different perturbagens (i.e., perturbing agents).
• 2/24/2016: Oliver He's paper titled "Ontology-based vaccine and drug adverse event representation and theory-guided systematic causal network analysis towards integrative pharmacovigilance research" was officially accepted by the journal in Current Pharmacology Reports. In this paper, Oliver extended the OneNet theory to cover the "OneNet Effectiveness" tenet, derived example new hypotheses out of the theory, and proposed to intergrate the theory and ontology to better study pharmacovigilance.
• 9/30/2015: Oliver He presented an invited seminar talk at the School of Bioinformatics Informatics (SBMI) at University of Texas Health Science Center at Houston (UTHealth). The title of his talk is: Ontology development and applications for clinical and biological adverse event data integration and analysis (Note: this is a YouTube video). In this talk, Oliver introduced his development and applications of ontologies related to adverse events. In the middle of his talk, he also introduced the OneNet theory and its possible usage.
• 9/29/2014: Oliver He presented a keynote presentation at the 3rd International Conference and Exhibition on Clinical & Cellular Immunology (Immunology Summit-2014), at Baltimore, MD, USA. The title of his presentation is: How a host organism responds to various microbial infections and vaccinations? In this talk, Oliver introduced how the OneNet theory can be used to comprehensively study the integrative complex interaction network of one organism that reacts to various pathogen infections or vaccinations.
• 9/9/2014: I presented a talk on 9/9/2014 in the Computer Science Department of the Wayne State University. His talk title is: From Ontologies to an "OneNet" Theory for Representation and Analysis of Biological and Molecular Interaction Networks. In this talk, Oliver introduced ontologies and the OneNet theory.

7. Notes about this web page:

• Updated on 3/14/2018: Updated the document by adding the information of the Life Ontology (LifO) and editing some of the web page text.
• Updated on 5/10/2017: Made a few editorial updates as suggested by Mr. Sam Smith from Detroit (Thanks!).
• Updated on 11/9/2016: Updated the text in the section "Derivation of the OneNet theory".
• Updated on 9/5/2016: Added the OneNet Motivation tenet, its related explanation, and one more reference.
• Updated on 3/15/2016: Added the section of hypotheses.
• Updated on 1/9/2016: Changed the "OneNet Efficacy" to "OneNet Effectiveness" by adopting the URREF ontology definition of "effectiveness", and added text explanation on this web page.
• Updated on 10/30/2015: Added the "OneNet Efficacy" characteristics under the theory definition section and Darwin's theory of evolution under the theory derivation section. The "OneNet Efficacy" tenet was derived by the author from studying the outcome of OneNet and referencing Darwin's theory of evolution. Also, updated the News section.
• Updates on: 7/21/2014, 9/10/2014, 2/8/2015.
• The first version of this document was posted on June 24, 2014.

Your suggestions, comments, and collaborations are appreciated. Thanks!