{"id":128,"date":"2019-08-01T19:35:50","date_gmt":"2019-08-01T19:35:50","guid":{"rendered":"https:\/\/solvingbiology.com\/?page_id=128"},"modified":"2025-02-05T20:01:03","modified_gmt":"2025-02-05T20:01:03","slug":"chapters","status":"publish","type":"page","link":"https:\/\/solvingbiology.com\/?page_id=128","title":{"rendered":"CHAPTERS"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.16″ custom_margin=”-111px|||||” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font=”|700|||||||” hover_enabled=”0″ sticky_enabled=”0″ text_font_size=”22px” text_text_color=”#000000″ text_orientation=”center”]PRIMER 1: Solving Biology[\/et_pb_text][et_pb_divider color=”#000000″ divider_position=”bottom” _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ min_height=”47px” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Chapter 1 – LEVELS OF COMPLEXITY<\/strong><\/h4>\n

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Solving biology is an exercise in solving complexities by creating complexities. The process consists of using published data to create complexities parallel to the ones used by biology to solve a given problem. In effect, we recruit biology to solve our problems for us, being confident that it has already worked out the best solutions. We can safely assume that biology is qualified to do the heavy lifting because it knows what rules to apply and how to summon the necessary resources. It understands full well that survival depends on its ability to adapt quickly and effectively.<\/p>\n

Chapter 1, which introduces the first six levels of complexity, tells the story simply and succinctly. Although touching on the central themes and principal findings, it keeps details to a minimum. Getting the big picture at the outset will make it easier for us to tackle the specifics in the ensuing chapters.<\/p>\n

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Chapter 2 – EQUATIONS <\/strong><\/h4>\n

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Capturing biology\u2019s rules with equations allows us to communicate with biology mathematically.\u00a0 We ask biology a question by giving it a problem to solve by way of an experiment.\u00a0 In turn, biology prepares an answer by tweaking its rules to solve the problem we created.\u00a0 If biology repeatedly applies the same general rule under different circumstances, it becomes a candidate for a first principle.<\/p>\n

Let\u2019s look at a few examples of first principles.<\/p>\n

First Principle:<\/strong> Biology operates its business with ratios, relating one part to another quantitatively:<\/p>\n

x:y:z\u2026n<\/strong><\/p>\n

First Principle:<\/strong> Biology defines biochemical homogeneity mathematically in two-dimensional space:<\/p>\n

f(x)=mx<\/strong><\/p>\n

First Principle:<\/strong> Biology uses three spatial dimensions (0, 1, and 2) to define a change:<\/p>\n

f(x)=mx+b<\/strong><\/p>\n

But why do these three expressions qualify as 1st principles?\u00a0 Because they cannot be reduced further.
If biological complexity is based on such simple expressions, why isn\u2019t this fact already widely known?\u00a0 As a science, biology currently operates under the same theory structure as physics and chemistry.\u00a0 It\u2019s called reductionism. For biology, which operates as a complex adaptive system, reductionism is not the theory of choice because its purpose is to decrease or eliminate complexity.<\/p>\n

By simplifying biology, we tacitly agree to throwing away its complexity, along with most of its rules and first principles.\u00a0 We fail to see a pristine biology because it no longer exists.\u00a0 The primer explains how to claw back the complexity, rules and first principles.<\/p>\n

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Chapter 3 – Visualizing Complexity<\/p>\n

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Advanced technologies simplify the task of capturing patterns, rules, equations, and first principles.\u00a0 For example, we can use the first level of complexity (i.e., patterns) to generate large homogeneous data sets capable of diagnosing complex disorders such as those found in the human brain.\u00a0 This requires translating the reduced data of research papers back into complex data types called mathematical markers and connection ratios.<\/p>\n

The following table summarizes a data cage database.\u00a0 It\u2019s populated with unique mathematical markers for 26 brain disorders and designed to deliver the correct diagnosis 100% of the time.<\/p>\n

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To test the effectiveness of the data cage visually, we can select twenty markers for the bipolar disorder, relabel them as unknowns, and then use the database to diagnose the unknowns.\u00a0 As expected, all the unknown markers are identified as belonging to the bipolar disorder.<\/p>\n

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By looking for patterns created by\u00a0 mathematical markers with cluster analysis, we can begin to understand the relationship of one disorder to another.\u00a0 For example, schizophrenia shares many of its abnormal markers with other disorders, whereas Down Syndrome shares about half of its abnormal markers with epilepsy.<\/p>\n

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Chapter 4 – Reproducibility<\/p>\n

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We have two approaches to demonstrating reproducibility, our way and biology\u2019s.\u00a0 Unfortunately, our way is currently under attack because it feeds a crisis of confidence in our experimental methods. How might we resolve this bewildering problem?<\/p>\n

Starting with the same original data set, we can look for reproducibility our way (with statistics) and biology\u2019s way (with rules).\u00a0 This generates two different answers, one far more interesting than the other.<\/p>\n

Question:<\/strong> Do the data displayed in the figure below, which include 61 MRI estimates for the volume of the amygdala, demonstrate reproducibility across studies?<\/p>\n

Answer:<\/strong> No. In fact, many of the data points would appear to be statistically different.<\/p>\n

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Now let\u2019s ask biology.<\/p>\n

Question:<\/strong> Is the human amygdala reproducible?<\/p>\n

Answer:<\/strong> Yes.\u00a0 Biology solved this reproducibility problem by applying what amounts to a ratio rule (x:y:z:\u2026n). The 61 estimates for the amygdala are in fact highly reproducible (all the MRI data detected the same left to right volume ratio \u2013 4 (left):5(right)).<\/p>\n

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The primer explains why we\u2019re currently suffering a reproducibility crisis and what we can do about it.\u00a0 By copying the way biology does reproducibility, the crisis quickly goes away.<\/p>\n

In fact, biology is so good at enforcing reproducibility, that the primer routinely uses rule-based equations to predict and verify outcomes. Since reproducibility is fundamental to the success of a science, the primer will argue that it should be an essential part of its theory structure.<\/p>\n

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Chapter 5 – Data<\/p>\n

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In biology, we can identify two types of data: simple and complex.\u00a0 Simple data supply simple answers, whereas complex data supply complex answers.<\/p>\n

For a complexity such as biology, all questions and answers exist somewhere between complex and whatever comes after complex.\u00a0 Our current reductionist approach to experimental biology runs largely on simple data types (zero-dimensional data points), which are information poor. This severely limits out ability to deliver reliable and reproducible results. The primer explains why.<\/p>\n

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In contrast, complexity theory provides access to complex data types (n-dimensional) that can track biological changes and allow us to scale the dimensional ladder from zero dimensional points up to one-dimensional lines and two-dimensional planes.\u00a0 Each increase in dimension increases the complexity and holding capacity (richness) of the data.<\/p>\n

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Chapter 6 – Databases<\/p>\n

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Databases fuel complexity theory by becoming surrogate complexities running parallel to biology.\u00a0 The primer uses two literature databases, one populated with stereological data and the other with MRI data.\u00a0 From these two starting points, we can generate a host of derivative databases \u2013 each charged with a specific task.<\/p>\n

The primary stereology database uses a hierarchical design extending from genes to organisms, whereas the primary MRI database deals exclusively with the volumes of brain parts.<\/p>\n

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When translated into mathematical markers and connection ratios, for example, the primary MRI database uncovers widespread reproducibility in the clinical literature, as shown in the image below.\u00a0 Since complex data types allow us to phenotype the brain quantitatively in health and disease, we can address a wide range of unsolved problems in the basic and clinical sciences.<\/p>\n

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Chapter 7 – Calculations<\/p>\n

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For each level of complexity, sample calculations serve to demonstrate how we can use complexity theory to interact with the biomedical literature. For example, we\u2019ll combine published data from several studies to describe a biological change by following it with and without complexity. As the analysis unfolds, we\u2019ll see firsthand the effectiveness of a first principles approach as we extract biology\u2019s rules from the literature. Along the way, we\u2019ll ask challenging questions and understand why we can or cannot answer them.<\/p>\n

REDUCTIONISM (without biological complexity)<\/strong>
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Question:<\/strong> Can we find a biological rule describing a quantitative relationship of structure to function?<\/p>\n

Answer:<\/strong> No, the equation (R^2 = 0.4785) without an R^2 = 1 or \u2248 1 fails to detect the rule and its underlying first principle.<\/p>\n

COMPLEXITY THEORY (with biological complexity)<\/strong>
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Question:<\/strong> Can we find a biological rule describing a quantitative relationship of structure to function?<\/p>\n

Answer:<\/strong> Yes, the equations have R^2s = 1 or \u2248 1 (R^2 = 1.0000, R^2 = 0.9952, R^2 = 0.9725) and detect the rules (identified by the three equations) and the underlying first principle (f(x)=mx+b).<\/p>\n

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Chapter 8 – How to solve biology<\/p>\n

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Taking our lead from genetics and molecular biology, we\u2019ll assemble mashups and learn how to use them to solve phenotypes.\u00a0 This approach involves learning how to optimize our experimental models by relying on the inherent strengths of our theory structure.\u00a0 But what constitutes an optimal experimental model?\u00a0 It\u2019s the same one that biology uses to optimize its relationships of structure to function.<\/p>\n

The model shown below, for example, optimizes results while minimizing costs.\u00a0 It addresses a fundamental problem of our current experimental model.<\/p>\n

Typically, the experimental variables we choose to follow in an experiment are swimming in a sea of unaccounted for variables that create all kinds of mischief.\u00a0 In short, an optimized model manages these \u201crogue\u201d variables within the context of a complexity model.<\/p>\n

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Chapter 9 – Complexity theory<\/p>\n

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Complexity theory states that it takes a complexity to solve a complexity.\u00a0 Since the theory comes from biology, it offers a robust way to discover, understand, and innovate.\u00a0 Complexity theory works because it\u2019s copied from biology.<\/p>\n

Consider, for example, the way in which biology changes a cytoplasmic membrane in response to a threat.\u00a0 As shown in the figure below, it instigates a change by applying two basic principles – both of which define relationships of structure to function.\u00a0 Notice what happens.\u00a0 During a biological change, complexity develops step-wise as it spreads across distinct levels of the biological hierarchy.\u00a0 In fact, a biological change involves so many complex events and interactions that it can only be captured and interpreted with equations.<\/p>\n

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The Point?\u00a0 Without help from biology, we discover chaos.\u00a0 With help, we discover order.<\/p>\n

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PRIMER 1: Solving BiologyChapter 1 – LEVELS OF COMPLEXITYSolving biology is an exercise in solving complexities by creating complexities. The process consists of using published data to create complexities parallel to the ones used by biology to solve a given problem. In effect, we recruit biology to solve our problems for us, being confident that […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-128","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/solvingbiology.com\/index.php?rest_route=\/wp\/v2\/pages\/128","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/solvingbiology.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/solvingbiology.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/solvingbiology.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/solvingbiology.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=128"}],"version-history":[{"count":66,"href":"https:\/\/solvingbiology.com\/index.php?rest_route=\/wp\/v2\/pages\/128\/revisions"}],"predecessor-version":[{"id":3251,"href":"https:\/\/solvingbiology.com\/index.php?rest_route=\/wp\/v2\/pages\/128\/revisions\/3251"}],"wp:attachment":[{"href":"https:\/\/solvingbiology.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=128"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}