Physics of Life (2022) / Chapter Skim
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7 Health, Medicine, and Technology
7 Health, Medicine, and Technology
Pages 219-242
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From page 219... ...
This chapter explores some of this infrastructure, how parts of it emerged from the biological physics community, and how it has influenced the progress of human health, medicine, and technology more generally; an overview is given in Table 7.1. The practice of medicine has been revolutionized by our ability to see what is happening inside the body and in isolated cells, on scales from single molecules to whole organs.
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Neural networks and artificial Emulate human and animal performance Ongoing revolution in intelligence at challenging tasks, ranging artificial intelligence. from walking on rough terrain to understanding language.
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The biological physics community studied the spread of droplets experimen tally, using particle image velocimetry and other physics techniques, and theo retically, using fluid dynamics simulations. Such studies provided evidence that aerosols are extremely important in transmission of the virus, at a moment when this was controversial.
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Cancer treatment may involve the targeted delivery of X rays, gamma rays, or even protons to solid tumors. Microfluidic and gene sequencing assays may be used to detect circulating tumor cells.
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Further development and refinement of X-ray–based medical imaging continues to the present day. The invention of tomographic three-dimensional X-ray imaging, now widely known as X-ray computed tomography (CT)
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. FIGURE 7.2 X rays are not the only portion of the electromagnetic spectrum with an important role in medical imaging; gamma ray cameras are also used for imaging and diagnosis.
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Today, PET imaging is a workhorse technique for cancer diagnosis. Moreover, thanks to its ability to target specific biological processes of interest -- often limited only by the ability of radiochemists to produce a suitable tracer -- PET also has a forefront role in the growing radiological subfield of molecular imaging.
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From page 226... ...
Of course, optical emissions also have an enormous role in medical imaging. Nearly all of modern pathology rests on the ability to examine excised tissue specimens under a light microscope.
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These develop ments depend both on advances in technology and on theoretical ideas about the representation of information in the brain. Some sense for the liveliness of this enterprise comes from the continued emergence of startup companies based on technologies that have emerged from the biological physics community, as described in Box 7.1.
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From page 228... ...
These ultrafast laser systems make up roughly 10 percent of sales for the world's largest laser manufacturers. Advances in imaging methods coming from the biological physics community have gen erated startup companies targeting optical brain imaging, nonlinear optical approaches to in vivo cancer detection, and the application of optical voltage imaging to drug screening.
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From page 229... ...
In a startling develop ment, the problem of predicting protein structure from the amino acid sequence has inspired the development of a direct machine learning approach, "AlphaFold," which achieves a precision close to that of experimental structure determination by X-ray crystallography. A corollary of the ideas mentioned above is that the number of protein se quences seen in nature today is much larger than the number of protein folds, or folding motifs, that have been found by studying protein structures.
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it is essential that the design procedure in- mization of sequence for a fixed backbone e atomic radii were reparameterized on the There has been considerable progress in clude a search of nearby conformational conformation and gradient-based optimiza sis of the distances of closest approach of atom the development of computational methods space in addition to sequence space. With the tion of the backbone coordinates for a fixed irs in high-resolution protein structures, ex- for identifying amino acid sequences compat- exception of the method used by Desjarlais sequence.
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From page 231... ...
Research in synthetic biology is powered by technological advances in genetic engineering, live cell imaging, sequencing, protein engineering, and so on, and often pursued in interdisciplinary teams that bring together physicists, engineers, biochemists, and cell biologists. As with almost every other scientific discovery, the discovery of gene regulation provided the means and impetus to assert human control over nature, in this case the genetic networks in cells.
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Antecedents to the synthetic biology revolution are biofuels produced from starch, sugar, animal fats, and vegetable oils, which are playing an increasingly large role in our energy landscape. Ethanol, a first-generation biofuel made primarily from corn, is one of the main biobased products produced worldwide and is present in more than 98 percent of the gasoline sold in the United States.
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To fashion synthetic biology into a true engineering discipline will require under standing the quantitative relationships between the microscopic interactions of the molecular parts and the emergent, cell-scale properties of the network. Again, basic scientific questions in biological physics are linked closely to opportunities for new technologies.
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One excellent example of the contribution of biological physicists to this endeavor is in the development of Nextstrain, a widely used analysis and visualization tool (see Figure 7.5)
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Theoretical work from the then nascent biological physics community showed that, combining these observations, es sentially all single- and double-point mutations were accessible to the virus on time scales that matter for treatment, but triple mutations are not. This means that there are almost no paths for the virus to evolve resistance to three different drugs simultaneously.
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From page 236... ...
Perhaps surprisingly for those outside the field, some of the biggest challenges are with everyday tasks, such as walking or running over complex, real world terrain. For the biological physics community, trying to understand why these problems are so hard is part of making precise what is meant by biological function, or specifying the physics problems that organisms must solve in order to survive (Chapter 1)
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If walking is complicated, perhaps it would be easier to move without legs at all. Several groups in the biological physics community have focused their attention on the mechanisms of locomotion in snakes.
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From page 238... ...
ing actual sidewinders revealed a new "two wave" template that captures the sidewinding dynamics and reveals how phasing and modulation of the waves can lead to high-performance propulsion, maneuverability and stability. This description lends itself naturally to gauge theory formulation, importing ideas from the analysis of life at low Reynolds number (Chapter 1)
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living systems, enabling engineered devices to move in hydro-, aero-, and terra dynamic environments that are presently challenging for human-made systems. NEURAL NETWORKS AND ARTIFICIAL INTELLIGENCE One of the most consistent themes in the history of technology is the idea of building machines that emulate the extraordinary functionality of living systems.
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Twentieth-century discoveries about the dynamics of individual neurons, and the nature of their connections, seeded the idea that models of neural networks could embody com putational functions. As described in Chapter 3, even simple models of individual neurons, if connected in arbitrary ways, can generate complex dynamics and have considerable computational power.
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The influx of ideas from statistical physics to neural networks emphasizes that signal processing often involves an implicit model for the probability distribution of the incoming signals. This idea has been central to thinking about coding in real brains (Chapter 2)
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An alternative, which also goes back to the origins of these models in the biological physics community, is to build special purpose hardware, taking seriously the analog nature of computation in the nervous system. This is a rich field, which requires thinking about how basic mathematical operations carried out by neurons could be realized by semiconductor device physics.
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