My CV, last updated in early February 2021, is available below. Please note that this is a temporary page.
Art: Kosmik Kaleidoscope (unpublished poem), Kosmos/Bios/Electronicos – a digital art gallery, Machine Children (unpublished visual poem), My digital art sampling, The Neon Hymenopterans, The Octopoid Occupation
Engineering and Biotechnology: Notes on a topological representation of branching neuronal morphologies, Notes on computer architecture, Notes on observing the cell in its native state imaging subcellular dynamics in multicellular organisms, Notes on medicinal chemistry, Notes on nanoparticle physics, Notes on nanoparticle self-assembly, Notes on nanowires and nanoparticles in the nervous system, Notes on neural mass models, Notes on nucleic acid bioconjugation, Notes on RNA-seq, Notes on two-photon microscopy, Notes on universal resilience patterns in complex networks, Notes on upconversion nanoparticles
Mathematics: Algebraic Graph Mappings, Method for m-Dimensional Integration, Modeling global influences on networks by embedding them in manifolds, Notes on abstract Algebra, Notes on Banach and Hilbert Spaces, Notes on differential Geometry, Notes on dynamical Systems, Notes on topology
Philosophy: Emotion as a property of information part 1: the physical basis for panpsychism, Emotion as a property of information part 2: engineering joy, Existential purpose: an engineering perspective, Nine answers from Logan Thrasher Collins (interview), Panpsychic epistemology: a physical basis for knowledge and justification, Rational romanticism overview, Towards an objective theory of morality
PDF version: The Future of Biotechnology
Less than 250 years after the conclusion of the Enlightenment, we have reached a point in human history where science has given us seemingly mystical abilities. We interact across thousands of kilometers nigh-instantaneously, we hold millions of libraries of knowledge in the palms of our hands, and hosts of shining buildings tower into the sky. Despite popular conceptions of doom and gloom, we are healthier, more peaceful, and less impoverished than ever before (Pinker, 2018). Our medicines can perform miracles such as making the blind see (Kumar et al., 2016; Lu et al., 2020), repairing damaged organs (Attanasio et al., 2016; Fioretta et al., 2018), and eradicating smallpox and rinderpest (Njeumi et al., 2012; Willis, 1997). When reflecting on all that is possible today, Arthur C. Clarke’s famous statement that “any sufficiently advanced technology is indistinguishable from magic” takes on more truth now than ever. But the next revolution, the revolution where we decipher biological complexity and rewrite biology itself for the better, has only just begun.
The convergence of new experimental methods, software, and hardware may act as a driving force for deciphering complex biological systems at a vastly deeper level than ever before. Enormously data-intensive experimental techniques in areas such as spatial transcriptomics and high-resolution volume and video microscopy will provide the foundation for advancing our understanding of biological systems (Liao et al., 2021; McDole et al., 2018; Titze & Genoud, 2016; Vogt, 2020; Wan et al., 2019). Robotic laboratory automation may further enhance the throughput of such methods (Angelone et al., 2021; HamediRad et al., 2019; Holland & Davies, 2020). In the realm of software, artificial intelligence (AI) advances will facilitate interpretation of patterns in massive amounts of biological data (Motta et al., 2019; Scheffer et al., 2020; Topol, 2019). At its heart, AI is a technology which extracts patterns from data. This means that AI can automate the process of sifting through oceans of complex multidimensional data and isolating a manageable number of insights with relevance to human affairs. In addition to AI, detailed integrative simulation techniques will aid prediction and description of biological mechanisms (Bezaire et al., 2016; Billeh et al., 2020; Karr et al., 2012; Markram et al., 2015; Singharoy et al., 2019; Yu et al., 2016). Some examples of these include large-scale molecular dynamics (MD) simulations (Singharoy et al., 2019; Yu et al., 2016), kinetic simulations of whole cells (Karr et al., 2012), and neurobiological simulations with tens of thousands of detailed virtual neurons (Bezaire et al., 2016; Billeh et al., 2020; Markram et al., 2015). As essential supporting technologies for these software innovations, key hardware advances may take the forms of quantum computing architectures (Cao et al., 2019; Outeiral et al., 2021), neuroscience-optimized neuromorphic computing architectures (Brown et al., 2018; Indiveri et al., 2011; Schemmel et al., 2017), and neuromorphic tensor processing unit architectures (Bains, 2020). Quantum computing may support quantum mechanical MD simulations as well as MD simulations with more particles and longer timescales (Cao et al., 2019; Outeiral et al., 2021), neuroscience-optimized neuromorphic computing may support realistic brain simulations (Brown et al., 2018; Indiveri et al., 2011; Schemmel et al., 2017), and neuromorphic tensor processing unit architectures may support much more powerful AI (Bains, 2020). The advent of exascale supercomputing will also play a central role in aiding the outlined software methods for the biological sciences (Lee & Amaro, 2018; Service, 2018). These changes will facilitate massive enhancement of our ability to make accurate predictions of how biological systems behave.
The convergence of experimental methods, software, and hardware may further act as a driving force for rewriting complex biological systems in a scalable and reproducible manner. The previously mentioned hardware advances could enable a surge in computer-aided design (CAD) software for engineering biology with nanoscale precision. To design new biology, these CAD innovations particularly may leverage AI (Kriegman et al., 2020; Zielinski et al., 2020), in silico directed evolution (Benson et al., 2019; Kriegman et al., 2020), kinetic modeling of cellular signaling and metabolic networks (Karr et al., 2012; Zielinski et al., 2020), and molecular dynamics (Benson et al., 2019; Shi et al., 2017) as well as improved graphical user interfaces (Grun et al., 2015). On the experimental side, laboratory automation and novel experimental tools may align to rapidly synthesize, validate, and iteratively improve biological inventions (Angelone et al., 2021; Chao et al., 2015; HamediRad et al., 2019; Schneider, 2018). These changes will facilitate tremendous strides in our collective capacity to create entirely new biology and to interface this new biology with existing biology.
Advances in our capacity to decipher and rewrite biology will dramatically advance the biomedical sciences. For instance, immunotherapies have the potential to eventually cure most or all cancers (Eggermont et al., 2013; ‘Mac’ Cheever, 2008; Yong et al., 2017). Medical nanorobots, some of which will consist of an exciting material known as DNA origami (Jiang et al., 2019), may also contribute to cancer treatment (Tregubov et al., 2018) and treatment of other diseases. In the case of DNA origami especially, CAD and MD will likely play a significant role (Benson et al., 2019; Douglas et al., 2009; Shi et al., 2017). AI, classical MD, and quantum MD will also enable the creation of numerous protein-based nanomachines with diverse applications by enabling rational design of proteins which have sophisticated dynamics (Kuhlman & Bradley, 2019; Melo et al., 2018; Pirro et al., 2020). Experimental automation and computational methods involving AI and integrative simulations could enable extremely rapid responses in the form of treatments, vaccines, and diagnostics to future outbreaks of infectious disease (Angelone et al., 2021; Chao et al., 2015; Schneider, 2018; Singh et al., 2020). While the threat of antibiotic resistance is concerning, phage therapy and synthetic biology treatments may further combat future forms of bacterial infection (Collins et al., 2019; Kortright et al., 2019). AI may automate a large portion of biomedical image analysis in the clinical setting (Topol, 2019). Donor organ shortages may end with the advent of bioprinted replacement organs (Cui et al., 2017; Mir & Nakamura, 2017). CAD methods may help improve the quality of bioprinted organs (Fay, 2020). AI and integrative simulations might help unlock the secrets of aging, allowing development of treatments for aging as a disease. This could both greatly increase human longevity and greatly decrease the incidence of aging-related illnesses (Fontana et al., 2014; Zhavoronkov et al., 2019). Wearable medical devices such as electronic tattoos could monitor health and prevent tragedies by giving people early warnings before physiological dysfunctions occur (Jeong & Lu, 2019). These represent some of the many possible biomedical technologies which may make us happier and healthier in the relatively near future.
One biomedical technology which may particularly make gains throughout the coming decades is gene therapy. Through synthetic biology manufacturing techniques (Le et al., 2019), gene therapies may shake off their currently prohibitive level of expense. Multiscale computational methods for understanding the human body at general and personalized levels (through AI and integrative simulations), CRISPR tools (Doudna, 2020), and superior nanobiotechnology delivery systems (Lundstrom, 2018; Wang et al., 2019) may allow gene therapy to start treating complex polygenic disorders (Carlson-Stevermer et al., 2020). These factors may even someday enable genetic modifications which make the human body more suited to space colonization (Norman & Reiss, 2020). If political polarization declines and the specter of genetic inequality loses its imminence, gene therapy could even enhance cognitive abilities and empathy in humans. While these prospects may seem frightening to some, it is important to realize that even a few more highly intelligent and empathetic people may make dramatic positive changes in our world (Rinn & Bishop, 2015). Gene therapy may also make major contributions to increasing human longevity (Bernardes de Jesus et al., 2012). Gene therapy could result in many positive transformations to our lives and even help to preserve the long-term future of humanity.
Neurotechnology may also soon come of age. Connectomics techniques, AI, and integrative simulations may give far better understanding of how to treat brain diseases in precisely targeted ways (Bullmore & Sporns, 2009; Markram, 2006; Markram et al., 2015; Mizutani et al., 2019). In particular, nanoscale connectomics might soon undergo a revolution as 4th generation synchrotrons (Pacchioni, 2019) and the relatively cheap miniature synchrotrons called Lyncean Compact Light Sources (Hornberger et al., 2019) facilitate rapid imaging of brains at nanoscale resolution (Kuan et al., 2020). On the neuroelectronics side, brain-machine interfaces and electronic neural prostheses could treat traumatic brain injuries and sensory and motor ailments as well as extend human abilities to interface with the cloud and the physical environment (Acarón Ledesma et al., 2019; Flesher et al., 2016; Gaillet et al., 2020; Hampson et al., 2018; Liu et al., 2015; Musk, 2019). Optogenetic methods, which enable control of genetically modified neurons with pulses of light, might synergize with gene therapy to create much more precise and complex brain-computer interfaces (Balasubramaniam et al., 2018; Chen et al., 2018). Though currently in its infancy, neurotechnology will likely grow rapidly into a mature discipline which grants us new abilities in neuromedicine and beyond.
Novel biotechnologies will also have great influence on manufacturing and environmental conservation. Biological CAD methods, integrative simulations of metabolism and gene regulation, and laboratory automation may allow synthetic biology to create a panoply of new microorganisms which can cheaply and rapidly produce medicines (Meng & Ellis, 2020), nanostructures (Bhaskar & Lim, 2017; Furubayashi et al., 2020), and even useful macroscale materials (Tang et al., 2020). Engineered microorganisms may also act to clean up pollutants and greenhouse gases (Gong et al., 2016). Molecular CAD methods, MD simulations, and laboratory automation may further revolutionize manufacturing through the creation of artificial molecular factories (Krause & Feringa, 2020). These molecular factories could involve immobilizing optically programmable supramolecular complexes such as certain rotaxanes and catenanes (Bruns & Stoddart, 2014) on metal-organic frameworks or similar crystalline structures (Krause & Feringa, 2020). With these miniscule factories, the dream of molecularly or even atomically precise construction at scale might be in reach. In addition, molecular factories which clean up pollutants and greenhouse gases could also make great contributions to combatting environmental degradation (Aithal & Aithal, 2020; Subramanian et al., 2020). Another suite of emerging technologies for ecoengineering are gene drives. These propagate gene editing tools which modulate the reproduction of populations of mosquitos and other disease vectors, potentially helping to stop illnesses like malaria (Gantz et al., 2015; Noble et al., 2017). Synthetic biology may also provide “off switches” for these gene drives, preventing them from causing environmental problems if they get out of control (Xu et al., 2020). In the realm of food production, gene edited plants can be made more suited to vertical farming (Kwon et al., 2020; O’Sullivan et al., 2020), indoor farming on the moon or Mars (Cannon & Britt, 2019), or ocean-based agriculture (Simke, 2020). In vitro meat may eventually transform meat production into a much more sustainable industry while decreasing the prevalence of animal cruelty (Bryant & Barnett, 2020; Zhang et al., 2020). These innovations and others could go a long way towards combatting global challenges such as hunger and climate change.
The confluence of advances in experiment, software, and hardware will enable many exciting biotechnological changes in the coming decades. Clever new experimental techniques will couple with automation to produce oceans of biological data. AI and integrative simulations extract meaningful insights from those otherwise unmanageable data point oceans. Hardware advances in neuromorphic computing, quantum computing, and exascale supercomputing could enable the titanic computations necessary to push software to its full potential. With this trinity of drivers of scientific progress, a plethora of new biotechnologies may enter common use and radically transform how we live. Some major areas of impact for these biotechnologies will include biomedicine, neurotechnology, gene therapy, manufacturing, agriculture, environmental remediation, and space colonization. Some may raise objections about the risks of such rapid technological changes. To answer these objections, consider that any kind of human progress, technological or social, must involve missteps. Yet human ingenuity and determination corrects these missteps in an ever-evolving trajectory, leading to an overall better world. Technology will synergize with the indomitable human spirit to build a bright and beautiful future.
Acarón Ledesma, H., Li, X., Carvalho-de-Souza, J. L., Wei, W., Bezanilla, F., & Tian, B. (2019). An atlas of nano-enabled neural interfaces. Nature Nanotechnology, 14(7), 645–657. https://doi.org/10.1038/s41565-019-0487-x
Aithal, S., & Aithal, P. S. (2020). Cleaning the Environment using Nanotechnology–A Review based Mega-Machine Design. Environmental Information Sciences: With Aspects on Allied Areas & Other Emerging Interdisciplinary Environmental Concerns” Edited by PK Paul et Al. Published by New Delhi Publishers, New Delhi, India, 13–40.
Angelone, D., Hammer, A. J. S., Rohrbach, S., Krambeck, S., Granda, J. M., Wolf, J., Zalesskiy, S., Chisholm, G., & Cronin, L. (2021). Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine. Nature Chemistry, 13(1), 63–69. https://doi.org/10.1038/s41557-020-00596-9
Ariella Simke. (2020). You May Find Salt-Tolerant Rice Growing In The Ocean By 2021. Forbes. https://www.forbes.com/sites/ariellasimke/2020/02/21/you-may-find-salt-tolerant-rice-growing-in-the-ocean-by-2021/?sh=25f961cf4133
Attanasio, C., Latancia, M. T., Otterbein, L. E., & Netti, P. A. (2016). Update on Renal Replacement Therapy: Implantable Artificial Devices and Bioengineered Organs. Tissue Engineering Part B: Reviews, 22(4), 330–340. https://doi.org/10.1089/ten.teb.2015.0467
Bains, S. (2020). The business of building brains. Nature Electronics, 3(7), 348–351. https://doi.org/10.1038/s41928-020-0449-1
Balasubramaniam, S., Wirdatmadja, S. A., Barros, M. T., Koucheryavy, Y., Stachowiak, M., & Jornet, J. M. (2018). Wireless Communications for Optogenetics-Based Brain Stimulation: Present Technology and Future Challenges. IEEE Communications Magazine, 56(7), 218–224. https://doi.org/10.1109/MCOM.2018.1700917
Benson, E., Lolaico, M., Tarasov, Y., Gådin, A., & Högberg, B. (2019). Evolutionary Refinement of DNA Nanostructures Using Coarse-Grained Molecular Dynamics Simulations. ACS Nano, 13(11), 12591–12598. https://doi.org/10.1021/acsnano.9b03473
Bernardes de Jesus, B., Vera, E., Schneeberger, K., Tejera, A. M., Ayuso, E., Bosch, F., & Blasco, M. A. (2012). Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer. EMBO Molecular Medicine, 4(8), 691–704. https://doi.org/https://doi.org/10.1002/emmm.201200245
Bezaire, M. J., Raikov, I., Burk, K., Vyas, D., & Soltesz, I. (2016). Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. ELife, 5, e18566. https://doi.org/10.7554/eLife.18566
Bhaskar, S., & Lim, S. (2017). Engineering protein nanocages as carriers for biomedical applications. NPG Asia Materials, 9(4), e371–e371. https://doi.org/10.1038/am.2016.128
Billeh, Y. N., Cai, B., Gratiy, S. L., Dai, K., Iyer, R., Gouwens, N. W., Abbasi-Asl, R., Jia, X., Siegle, J. H., Olsen, S. R., Koch, C., Mihalas, S., & Arkhipov, A. (2020). Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron, 106(3), 388-403.e18. https://doi.org/10.1016/j.neuron.2020.01.040
Brown, A. D., Chad, J. E., Kamarudin, R., Dugan, K. J., & Furber, S. B. (2018). SpiNNaker: Event-Based Simulation—Quantitative Behavior. IEEE Transactions on Multi-Scale Computing Systems, 4(3), 450–462. https://doi.org/10.1109/TMSCS.2017.2748122
Bruns, C. J., & Stoddart, J. F. (2014). Rotaxane-Based Molecular Muscles. Accounts of Chemical Research, 47(7), 2186–2199. https://doi.org/10.1021/ar500138u
Bryant, C., & Barnett, J. (2020). Consumer Acceptance of Cultured Meat: An Updated Review (2018–2020). In Applied Sciences (Vol. 10, Issue 15). https://doi.org/10.3390/app10155201
Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186. http://dx.doi.org/10.1038/nrn2575
Cannon, K. M., & Britt, D. T. (2019). Feeding One Million People on Mars. New Space, 7(4), 245–254. https://doi.org/10.1089/space.2019.0018
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., Sim, S., Veis, L., & Aspuru-Guzik, A. (2019). Quantum Chemistry in the Age of Quantum Computing. Chemical Reviews, 119(19), 10856–10915. https://doi.org/10.1021/acs.chemrev.8b00803
Carlson-Stevermer, J., Das, A., Abdeen, A. A., Fiflis, D., Grindel, B. I., Saxena, S., Akcan, T., Alam, T., Kletzien, H., Kohlenberg, L., Goedland, M., Dombroe, M. J., & Saha, K. (2020). Design of efficacious somatic cell genome editing strategies for recessive and polygenic diseases. Nature Communications, 11(1), 6277. https://doi.org/10.1038/s41467-020-20065-8
Chao, R., Yuan, Y., & Zhao, H. (2015). Building biological foundries for next-generation synthetic biology. Science China Life Sciences, 58(7), 658–665. https://doi.org/10.1007/s11427-015-4866-8
Chen, S., Weitemier, A. Z., Zeng, X., He, L., Wang, X., Tao, Y., Huang, A. J. Y., Hashimotodani, Y., Kano, M., Iwasaki, H., Parajuli, L. K., Okabe, S., Teh, D. B. L., All, A. H., Tsutsui-Kimura, I., Tanaka, K. F., Liu, X., & McHugh, T. J. (2018). Near-infrared deep brain stimulation via upconversion nanoparticle–mediated optogenetics. Science, 359(6376), 679 LP – 684. http://science.sciencemag.org/content/359/6376/679.abstract
Collins, L. T., Otoupal, P. B., Campos, J. K., Courtney, C. M., & Chatterjee, A. (2019). Design of a De Novo Aggregating Antimicrobial Peptide and a Bacterial Conjugation-Based Delivery System. Biochemistry, 58(11), 1521–1526. https://doi.org/10.1021/acs.biochem.8b00888
Cui, H., Nowicki, M., Fisher, J. P., & Zhang, L. G. (2017). 3D Bioprinting for Organ Regeneration. Advanced Healthcare Materials, 6(1), 1601118. https://doi.org/https://doi.org/10.1002/adhm.201601118
Doudna, J. A. (2020). The promise and challenge of therapeutic genome editing. Nature, 578(7794), 229–236. https://doi.org/10.1038/s41586-020-1978-5
Douglas, S. M., Marblestone, A. H., Teerapittayanon, S., Vazquez, A., Church, G. M., & Shih, W. M. (2009). Rapid prototyping of 3D DNA-origami shapes with caDNAno. Nucleic Acids Research, 37(15), 5001–5006. https://doi.org/10.1093/nar/gkp436
Eggermont, A. M. M., Kroemer, G., & Zitvogel, L. (2013). Immunotherapy and the concept of a clinical cure. European Journal of Cancer, 49(14), 2965–2967. https://doi.org/https://doi.org/10.1016/j.ejca.2013.06.019
Fay, C. D. (2020). Computer-Aided Design and Manufacturing (CAD/CAM) for Bioprinting BT – 3D Bioprinting: Principles and Protocols (J. M. Crook (ed.); pp. 27–41). Springer US. https://doi.org/10.1007/978-1-0716-0520-2_3
Fioretta, E. S., Dijkman, P. E., Emmert, M. Y., & Hoerstrup, S. P. (2018). The future of heart valve replacement: recent developments and translational challenges for heart valve tissue engineering. Journal of Tissue Engineering and Regenerative Medicine, 12(1), e323–e335. https://doi.org/https://doi.org/10.1002/term.2326
Flesher, S. N., Collinger, J. L., Foldes, S. T., Weiss, J. M., Downey, J. E., Tyler-Kabara, E. C., Bensmaia, S. J., Schwartz, A. B., Boninger, M. L., & Gaunt, R. A. (2016). Intracortical microstimulation of human somatosensory cortex. Science Translational Medicine. http://stm.sciencemag.org/content/early/2016/10/12/scitranslmed.aaf8083.abstract
Fontana, L., Kennedy, B. K., Longo, V. D., Seals, D., & Melov, S. (2014). Medical research: treat ageing. Nature News, 511(7510), 405.
Furubayashi, M., Wallace, A. K., González, L. M., Jahnke, J. P., Hanrahan, B. M., Payne, A. L., Stratis-Cullum, D. N., Gray, M. T., Liu, H., Rhoads, M. K., & Voigt, C. A. (2020). Genetic Tuning of Iron Oxide Nanoparticle Size, Shape, and Surface Properties in Magnetospirillum magneticum. Advanced Functional Materials, n/a(n/a), 2004813. https://doi.org/https://doi.org/10.1002/adfm.202004813
Gaillet, V., Cutrone, A., Artoni, F., Vagni, P., Mega Pratiwi, A., Romero, S. A., Lipucci Di Paola, D., Micera, S., & Ghezzi, D. (2020). Spatially selective activation of the visual cortex via intraneural stimulation of the optic nerve. Nature Biomedical Engineering, 4(2), 181–194. https://doi.org/10.1038/s41551-019-0446-8
Gantz, V. M., Jasinskiene, N., Tatarenkova, O., Fazekas, A., Macias, V. M., Bier, E., & James, A. A. (2015). Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi. Proceedings of the National Academy of Sciences, 112(49), E6736 LP-E6743. https://doi.org/10.1073/pnas.1521077112
Gong, F., Cai, Z., & Li, Y. (2016). Synthetic biology for CO2 fixation. Science China Life Sciences, 59(11), 1106–1114. https://doi.org/10.1007/s11427-016-0304-2
Grun, C., Werfel, J., Zhang, D. Y., & Yin, P. (2015). DyNAMiC Workbench: an integrated development environment for dynamic DNA nanotechnology. Journal of The Royal Society Interface, 12(111), 20150580. https://doi.org/10.1098/rsif.2015.0580
HamediRad, M., Chao, R., Weisberg, S., Lian, J., Sinha, S., & Zhao, H. (2019). Towards a fully automated algorithm driven platform for biosystems design. Nature Communications, 10(1), 5150. https://doi.org/10.1038/s41467-019-13189-z
Hampson, R. E., Song, D., Robinson, B. S., Fetterhoff, D., Dakos, A. S., Roeder, B. M., She, X., Wicks, R. T., Witcher, M. R., Couture, D. E., Laxton, A. W., Munger-Clary, H., Popli, G., Sollman, M. J., Whitlow, C. T., Marmarelis, V. Z., Berger, T. W., & Deadwyler, S. A. (2018). Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall. Journal of Neural Engineering, 15(3), 36014. https://doi.org/10.1088/1741-2552/aaaed7
Holland, I., & Davies, J. A. (2020). Automation in the Life Science Research Laboratory . In Frontiers in Bioengineering and Biotechnology (Vol. 8, p. 1326). https://www.frontiersin.org/article/10.3389/fbioe.2020.571777
Hornberger, B., Kasahara, J., Gifford, M., Ruth, R., & Loewen, R. (2019). A compact light source providing high-flux, quasi-monochromatic, tunable X-rays in the laboratory. Proc.SPIE, 11110. https://doi.org/10.1117/12.2527356
Indiveri, G., Linares-Barranco, B., Hamilton, T., van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S.-C., Dudek, P., Häfliger, P., Renaud, S., Schemmel, J., Cauwenberghs, G., Arthur, J., Hynna, K., Folowosele, F., SAÏGHI, S., Serrano-Gotarredona, T., Wijekoon, J., Wang, Y., & Boahen, K. (2011). Neuromorphic Silicon Neuron Circuits . In Frontiers in Neuroscience (Vol. 5, p. 73). https://www.frontiersin.org/article/10.3389/fnins.2011.00073
Jeong, H., & Lu, N. (2019). Electronic tattoos: the most multifunctional but imperceptible wearables. Proc.SPIE, 11020. https://doi.org/10.1117/12.2518994
Jiang, Q., Liu, S., Liu, J., Wang, Z.-G., & Ding, B. (2019). Rationally Designed DNA-Origami Nanomaterials for Drug Delivery In Vivo. Advanced Materials, 31(45), 1804785. https://doi.org/https://doi.org/10.1002/adma.201804785
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival Jr., B., Assad-Garcia, N., Glass, J. I., & Covert, M. W. (2012). A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell, 150(2), 389–401. https://doi.org/10.1016/j.cell.2012.05.044
Kortright, K. E., Chan, B. K., Koff, J. L., & Turner, P. E. (2019). Phage Therapy: A Renewed Approach to Combat Antibiotic-Resistant Bacteria. Cell Host & Microbe, 25(2), 219–232. https://doi.org/https://doi.org/10.1016/j.chom.2019.01.014
Krause, S., & Feringa, B. L. (2020). Towards artificial molecular factories from framework-embedded molecular machines. Nature Reviews Chemistry, 4(10), 550–562. https://doi.org/10.1038/s41570-020-0209-9
Kriegman, S., Blackiston, D., Levin, M., & Bongard, J. (2020). A scalable pipeline for designing reconfigurable organisms. Proceedings of the National Academy of Sciences, 117(4), 1853 LP – 1859. https://doi.org/10.1073/pnas.1910837117
Kuan, A. T., Phelps, J. S., Thomas, L. A., Nguyen, T. M., Han, J., Chen, C.-L., Azevedo, A. W., Tuthill, J. C., Funke, J., Cloetens, P., Pacureanu, A., & Lee, W.-C. A. (2020). Dense neuronal reconstruction through X-ray holographic nano-tomography. Nature Neuroscience, 23(12), 1637–1643. https://doi.org/10.1038/s41593-020-0704-9
Kuhlman, B., & Bradley, P. (2019). Advances in protein structure prediction and design. Nature Reviews Molecular Cell Biology, 20(11), 681–697. https://doi.org/10.1038/s41580-019-0163-x
Kumar, S. R. P., Markusic, D. M., Biswas, M., High, K. A., & Herzog, R. W. (2016). Clinical development of gene therapy: results and lessons from recent successes. Molecular Therapy – Methods & Clinical Development, 3, 16034. https://doi.org/https://doi.org/10.1038/mtm.2016.34
Kwon, C.-T., Heo, J., Lemmon, Z. H., Capua, Y., Hutton, S. F., Van Eck, J., Park, S. J., & Lippman, Z. B. (2020). Rapid customization of Solanaceae fruit crops for urban agriculture. Nature Biotechnology, 38(2), 182–188. https://doi.org/10.1038/s41587-019-0361-2
Le, D. T., Radukic, M. T., & Müller, K. M. (2019). Adeno-associated virus capsid protein expression in Escherichia coli and chemically defined capsid assembly. Scientific Reports, 9(1), 18631. https://doi.org/10.1038/s41598-019-54928-y
Lee, C. T., & Amaro, R. (2018). Exascale Computing: A New Dawn for Computational Biology. Computing in Science & Engineering, 20(5), 18–25. https://doi.org/10.1109/MCSE.2018.05329812
Liao, J., Lu, X., Shao, X., Zhu, L., & Fan, X. (2021). Uncovering an Organ’s Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics. Trends in Biotechnology, 39(1), 43–58. https://doi.org/10.1016/j.tibtech.2020.05.006
Liu, J., Fu, T.-M., Cheng, Z., Hong, G., Zhou, T., Jin, L., Duvvuri, M., Jiang, Z., Kruskal, P., Xie, C., Suo, Z., Fang, Y., & Lieber, C. M. (2015). Syringe-injectable electronics. Nature Nanotechnology, 10, 629. http://dx.doi.org/10.1038/nnano.2015.115
Lu, Y., Brommer, B., Tian, X., Krishnan, A., Meer, M., Wang, C., Vera, D. L., Zeng, Q., Yu, D., Bonkowski, M. S., Yang, J.-H., Zhou, S., Hoffmann, E. M., Karg, M. M., Schultz, M. B., Kane, A. E., Davidsohn, N., Korobkina, E., Chwalek, K., … Sinclair, D. A. (2020). Reprogramming to recover youthful epigenetic information and restore vision. Nature, 588(7836), 124–129. https://doi.org/10.1038/s41586-020-2975-4
Lundstrom, K. (2018). Viral Vectors in Gene Therapy. In Diseases (Vol. 6, Issue 2). https://doi.org/10.3390/diseases6020042
‘Mac’ Cheever, M. A. (2008). Twelve immunotherapy drugs that could cure cancers. Immunological Reviews, 222(1), 357–368. https://doi.org/https://doi.org/10.1111/j.1600-065X.2008.00604.x
Markram, H. (2006). The Blue Brain Project. Nature Reviews Neuroscience, 7, 153. http://dx.doi.org/10.1038/nrn1848
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., Kahou, G. A. A., Berger, T. K., Bilgili, A., Buncic, N., Chalimourda, A., Chindemi, G., Courcol, J.-D., Delalondre, F., Delattre, V., … Schürmann, F. (2015). Reconstruction and Simulation of Neocortical Microcircuitry. Cell, 163(2), 456–492. https://doi.org/10.1016/j.cell.2015.09.029
McDole, K., Guignard, L., Amat, F., Berger, A., Malandain, G., Royer, L. A., Turaga, S. C., Branson, K., & Keller, P. J. (2018). In Toto Imaging and Reconstruction of Post-Implantation Mouse Development at the Single-Cell Level. Cell, 175(3), 859-876.e33. https://doi.org/https://doi.org/10.1016/j.cell.2018.09.031
Melo, M. C. R., Bernardi, R. C., Rudack, T., Scheurer, M., Riplinger, C., Phillips, J. C., Maia, J. D. C., Rocha, G. B., Ribeiro, J. V, Stone, J. E., Neese, F., Schulten, K., & Luthey-Schulten, Z. (2018). NAMD goes quantum: an integrative suite for hybrid simulations. Nature Methods, 15(5), 351–354. https://doi.org/10.1038/nmeth.4638
Meng, F., & Ellis, T. (2020). The second decade of synthetic biology: 2010–2020. Nature Communications, 11(1), 5174. https://doi.org/10.1038/s41467-020-19092-2
Mir, T. A., & Nakamura, M. (2017). Three-Dimensional Bioprinting: Toward the Era of Manufacturing Human Organs as Spare Parts for Healthcare and Medicine. Tissue Engineering Part B: Reviews, 23(3), 245–256. https://doi.org/10.1089/ten.teb.2016.0398
Mizutani, R., Saiga, R., Takeuchi, A., Uesugi, K., Terada, Y., Suzuki, Y., De Andrade, V., De Carlo, F., Takekoshi, S., Inomoto, C., Nakamura, N., Kushima, I., Iritani, S., Ozaki, N., Ide, S., Ikeda, K., Oshima, K., Itokawa, M., & Arai, M. (2019). Three-dimensional alteration of neurites in schizophrenia. Translational Psychiatry, 9(1), 85. https://doi.org/10.1038/s41398-019-0427-4
Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., & Helmstaedter, M. (2019). Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science, 366(6469), eaay3134. https://doi.org/10.1126/science.aay3134
Musk, E. (2019). An integrated brain-machine interface platform with thousands of channels. BioRxiv, 703801. https://doi.org/10.1101/703801
Njeumi, F., Taylor, W., Diallo, A., Miyagishima, K., Pastoret, P.-P., Vallat, B., & Traore, M. (2012). The long journey: a brief review of the eradication of rinderpest. Revue Scientifique et Technique (International Office of Epizootics), 31(3), 729–746. https://doi.org/10.20506/rst.31.3.2157
Noble, C., Olejarz, J., Esvelt, K. M., Church, G. M., & Nowak, M. A. (2017). Evolutionary dynamics of CRISPR gene drives. Science Advances, 3(4), e1601964. https://doi.org/10.1126/sciadv.1601964
Norman, Z., & Reiss, M. J. (2020). Two Planets, One Species: Does a Mission to Mars Alter the Balance in Favour of Human Enhancement? BT – Human Enhancements for Space Missions: Lunar, Martian, and Future Missions to the Outer Planets (K. Szocik (ed.); pp. 151–167). Springer International Publishing. https://doi.org/10.1007/978-3-030-42036-9_11
O’Sullivan, C. A., McIntyre, C. L., Dry, I. B., Hani, S. M., Hochman, Z., & Bonnett, G. D. (2020). Vertical farms bear fruit. Nature Biotechnology, 38(2), 160–162. https://doi.org/10.1038/s41587-019-0400-z
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021). The prospects of quantum computing in computational molecular biology. WIREs Computational Molecular Science, 11(1), e1481. https://doi.org/https://doi.org/10.1002/wcms.1481
Pacchioni, G. (2019). An upgrade to a bright future. Nature Reviews Physics, 1(2), 100–101. https://doi.org/10.1038/s42254-019-0019-5
Pinker, S. (2018). Enlightenment now: The case for reason, science, humanism, and progress. Penguin.
Pirro, F., Schmidt, N., Lincoff, J., Widel, Z. X., Polizzi, N. F., Liu, L., Therien, M. J., Grabe, M., Chino, M., Lombardi, A., & DeGrado, W. F. (2020). Allosteric cooperation in a de novo-designed two-domain protein. Proceedings of the National Academy of Sciences, 117(52), 33246 LP – 33253. https://doi.org/10.1073/pnas.2017062117
Rinn, A. N., & Bishop, J. (2015). Gifted Adults: A Systematic Review and Analysis of the Literature. Gifted Child Quarterly, 59(4), 213–235. https://doi.org/10.1177/0016986215600795
Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., Clements, J., Hubbard, P. M., Katz, W. T., Umayam, L., Zhao, T., Ackerman, D., Blakely, T., Bogovic, J., Dolafi, T., … Plaza, S. M. (2020). A connectome and analysis of the adult Drosophila central brain. ELife, 9, e57443. https://doi.org/10.7554/eLife.57443
Schemmel, J., Kriener, L., Müller, P., & Meier, K. (2017). An accelerated analog neuromorphic hardware system emulating NMDA- and calcium-based non-linear dendrites. 2017 International Joint Conference on Neural Networks (IJCNN), 2217–2226. https://doi.org/10.1109/IJCNN.2017.7966124
Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery, 17(2), 97–113. https://doi.org/10.1038/nrd.2017.232
Service, R. F. (2018). Design for U.S. exascale computer takes shape. Science, 359(6376), 617 LP – 618. http://science.sciencemag.org/content/359/6376/617.abstract
Shi, Z., Castro, C. E., & Arya, G. (2017). Conformational Dynamics of Mechanically Compliant DNA Nanostructures from Coarse-Grained Molecular Dynamics Simulations. ACS Nano, 11(5), 4617–4630. https://doi.org/10.1021/acsnano.7b00242
Singh, E., Khan, R. J., Jha, R. K., Amera, G. M., Jain, M., Singh, R. P., Muthukumaran, J., & Singh, A. K. (2020). A comprehensive review on promising anti-viral therapeutic candidates identified against main protease from SARS-CoV-2 through various computational methods. Journal of Genetic Engineering and Biotechnology, 18(1), 69. https://doi.org/10.1186/s43141-020-00085-z
Singharoy, A., Maffeo, C., Delgado-Magnero, K. H., Swainsbury, D. J. K., Sener, M., Kleinekathöfer, U., Vant, J. W., Nguyen, J., Hitchcock, A., Isralewitz, B., Teo, I., Chandler, D. E., Stone, J. E., Phillips, J. C., Pogorelov, T. V, Mallus, M. I., Chipot, C., Luthey-Schulten, Z., Tieleman, D. P., … Schulten, K. (2019). Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism. Cell, 179(5), 1098-1111.e23. https://doi.org/https://doi.org/10.1016/j.cell.2019.10.021
Subramanian, K. S., Karthika, V., Praghadeesh, M., & Lakshmanan, A. (2020). Nanotechnology for Mitigation of Global Warming Impacts BT – Global Climate Change: Resilient and Smart Agriculture (V. Venkatramanan, S. Shah, & R. Prasad (eds.); pp. 315–336). Springer Singapore. https://doi.org/10.1007/978-981-32-9856-9_15
Tang, T.-C., An, B., Huang, Y., Vasikaran, S., Wang, Y., Jiang, X., Lu, T. K., & Zhong, C. (2020). Materials design by synthetic biology. Nature Reviews Materials. https://doi.org/10.1038/s41578-020-00265-w
Titze, B., & Genoud, C. (2016). Volume scanning electron microscopy for imaging biological ultrastructure. Biology of the Cell, 108(11), 307–323. https://doi.org/https://doi.org/10.1111/boc.201600024
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Tregubov, A. A., Nikitin, P. I., & Nikitin, M. P. (2018). Advanced Smart Nanomaterials with Integrated Logic-Gating and Biocomputing: Dawn of Theranostic Nanorobots. Chemical Reviews, 118(20), 10294–10348. https://doi.org/10.1021/acs.chemrev.8b00198
Vogt, N. (2020). X-ray connectomics. Nature Methods, 17(11), 1072. https://doi.org/10.1038/s41592-020-00994-4
Wan, Y., McDole, K., & Keller, P. J. (2019). Light-Sheet Microscopy and Its Potential for Understanding Developmental Processes. Annual Review of Cell and Developmental Biology, 35(1), 655–681. https://doi.org/10.1146/annurev-cellbio-100818-125311
Wang, D., Tai, P. W. L., & Gao, G. (2019). Adeno-associated virus vector as a platform for gene therapy delivery. Nature Reviews Drug Discovery, 18(5), 358–378. https://doi.org/10.1038/s41573-019-0012-9
Willis, N. J. (1997). Edward Jenner and the Eradication of Smallpox. Scottish Medical Journal, 42(4), 118–121. https://doi.org/10.1177/003693309704200407
Xu, X.-R. S., Bulger, E. A., Gantz, V. M., Klanseck, C., Heimler, S. R., Auradkar, A., Bennett, J. B., Miller, L. A., Leahy, S., Juste, S. S., Buchman, A., Akbari, O. S., Marshall, J. M., & Bier, E. (2020). Active Genetic Neutralizing Elements for Halting or Deleting Gene Drives. Molecular Cell, 80(2), 246-262.e4. https://doi.org/https://doi.org/10.1016/j.molcel.2020.09.003
Yong, C. S. M., Dardalhon, V., Devaud, C., Taylor, N., Darcy, P. K., & Kershaw, M. H. (2017). CAR T-cell therapy of solid tumors. Immunology & Cell Biology, 95(4), 356–363. https://doi.org/https://doi.org/10.1038/icb.2016.128
Yu, I., Mori, T., Ando, T., Harada, R., Jung, J., Sugita, Y., & Feig, M. (2016). Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm. ELife, 5, e19274. https://doi.org/10.7554/eLife.19274
Zhang, G., Zhao, X., Li, X., Du, G., Zhou, J., & Chen, J. (2020). Challenges and possibilities for bio-manufacturing cultured meat. Trends in Food Science & Technology, 97, 443–450. https://doi.org/https://doi.org/10.1016/j.tifs.2020.01.026
Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019). Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Research Reviews, 49, 49–66. https://doi.org/https://doi.org/10.1016/j.arr.2018.11.003
Zielinski, D. C., Patel, A., & Palsson, B. O. (2020). The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale. In Microorganisms (Vol. 8, Issue 12). https://doi.org/10.3390/microorganisms8122050
I have seen a variety of online resources which recommend books for learning physics and mathematics (e.g. Chicago undergraduate mathematics bibliography, Susan Fowler’s So You Want To Learn Physics, How to Learn Math and Physics, etc.), yet there seems to be a paucity of similar resources for biological fields. To help fill this gap, I have compiled a handpicked list of textbooks which may aid those with a desire to learn biology.
I have also included books from fields such as mathematics, computer science, chemistry, physics, imaging, and nanotechnology which are important in biology. The books from adjacent fields which I recommend here are mostly targeted towards those readers who come from backgrounds which are not greatly quantitative. For this reason, books filled with lots of detailed mathematics are located in the advanced category. That said, I do assume some familiarity with mathematics and physics in the lower levels also.
Though this page so far does not include resources beyond textbooks, there are many other useful tools for learning about biology. Video lectures, educational books which are not textbooks (e.g. Thieme FlexiBooks, Lippincott’s Illustrated Reviews, etc.), scientific journal articles (especially review papers), reputable scientific news articles (e.g. Nature News and Views, Science Daily, Neuroscience News, etc.), Wikipedia, other educational websites, and research experience come to mind.
While this list is certainly not comprehensive, I have tried to cover as much ground as possible for the interested autodidact. These books represent the ones that I personally feel are the best for the given subjects at the given levels (beginner, lower intermediate, upper intermediate, and advanced). There are a lot of texts related to microbiology, biochemistry, and neuroscience. This bias reflects my own background in synthetic biology, nanobiotechnology, and connectomics. My list is currently lacking in ecology and evolutionary biology texts. If anyone is interested in contributing their own recommendations for these or other missing topics, feel free to contact me and we can figure out how to incorporate your texts.
One point that I would like to make is that you by no means need to read these books from cover to cover. It is much more efficient to learn biology by creating a curriculum for yourself and reading selected chapters and sections as they interest you. Over time, the knowledge will build up and you will start to see how it all connects. You will eventually begin to gain the ability to think critically about biological mechanisms and how perturbing them may influence the systems. I would recommend practicing this kind of thinking early on. You can begin to do thought experiments even when you are starting out. As you carry out these thought experiments, you can explore your books and the internet to try and figure out any missing pieces. This will exercise your ability to understand and make predictions about biological systems.
Biology is an expansive, interdisciplinary, and extremely exciting field. I hope that you enjoy your journey into the biological sciences!
These represent foundational texts which introduce biology and associated fields which are essential for understanding biology (i.e. chemistry, physics, and mathematics). They are at a high school or maybe college freshman level.
Campbell Biology – by Urry, Cain, Wasserman, Minorsky, Reece || An authoritative introduction to biology and its subdisciplines. It features clear explanations, good organization, and helpful illustrations. Though lengthy, you can often read desired subsections in any order. That said, I would recommend reading some molecular biology and genetics chapters before diving into physiology. It should be noted that this text is a primary source for the high school Biology Olympiad competition.
Chemistry – by Zumdahl, Zumdahl, and DeCoste || An introductory chemistry text which has good organization and illustrations. Though other general chemistry books could work just as well, I have a mild personal preference for this one.
Calculus – by Stewart || Though lengthy, this book is a good introduction to calculus. It explains single-variable calculus and multivariable calculus and even gives a small taste of differential equations. This is excellent since calculus and differential equations are so central to computational modeling of biological systems.
Physics for Scientists and Engineers: A Strategic Approach with Modern Physics – by Knight || While I have not used this book personally, I have heard good things with regards to its applicability for biology. As such, I picked out Knight’s text for this list entry because of its organization, its inclusion of modern physics, and its emphasis on practical applications.
These books introduce a range of key subfields in biology. Though some of the texts are quite long (e.g. 800+ pages), I will say again that they do not need to be read cover to cover. These do not require greatly specialized knowledge to understand. They are typically used for first year or second year university courses. As with the previous section, I have included some non-biology texts covering fields adjacent to biology. Note that, because biology is an interdisciplinary enterprise, these adjacent fields are vitally important for understanding and applying biological knowledge.
Lehninger’s Biochemistry – by Nelson and Cox || Great textbook which discusses biochemistry with both depth and breadth. It is not as detailed as Voet’s book (see the upper intermediate section), but it is not a light treatment either. This text features beautiful illustrations which are very helpful for gaining a deeply visual appreciation of how biochemistry works. In my opinion, it also has well-written treatments of the mathematics of enzyme kinetics and related topics.
MATLAB: A Practical Introduction to Programming and Problem Solving – by Attaway || Since computer science is an integral part of biology research, it is important to have at least some understanding of programming and modeling. For those who are not already familiar with programming, Attaway’s MATLAB book provides an excellent entry point. It instructs on how to use MATLAB in a clear and concise way and also discusses essential mathematics that come up in scientific computing. Another strength of this text is its clean organization, which allows one to jump around the different sections more easily as required by one’s explorations in MATLAB coding. MATLAB is one of the most user-friendly programming languages and so it is great for beginners. Though MATLAB is not as grounded in the fundamentals of computational logic as some languages, it is quite useful as a tool for many scientific computing applications such as modeling, image processing, and data analysis. It should be noted that MATLAB itself is not free, though if you are affiliated with a university, the school will probably pay for your license.
Python Programming: An Introduction to Computer Science – by Zelle || This text provides another excellent entry point into programming. Zelle acts as a well-organized reference for learning the basics of Python. It is clear and reasonably concise. By contrast to MATLAB, Python is freely available. Another benefit of Python is the wide array of user-created software packages that you can easily install into your Python infrastructure. Many of these packages provide tools that handle specific areas of computational biology such as nucleic acid sequence analysis or biologically realistic neuron simulation.
Essentials of Genetics – by Klug, Cummings, Spencer, Palladino, Killian || A standard text which introduces the various branches of genetics. Though there is perhaps not enough focus on modern techniques for my personal taste, I do appreciate the clarity of this book’s molecular genetics sections.
Gene Cloning and DNA Analysis: An Introduction – by Brown || Excellent book which describes molecular genetics techniques. It is concise and clear and yet still covers a lot of important methods in sufficient detail to convey real understanding.
Cellular and Molecular Immunology – by Abbas, Lichtman, Pillai || Explains immunological principles in a through yet digestible way. It features very consistent diagrams which carefully represent specific molecules and cell types with the same images throughout the book.
Basic Immunology: Functions and Disorders of the Immune System – by Abbas, Lichtman, Pillai || This text is essentially a more concise version of Cellular and Molecular Immunology. Since it is written by the same authors, it also features its sister text’s helpfully consistent diagrams.
Fundamentals of Differential Equations and Boundary Value Problems – by Nagle, Saff, and Snider || Differential equations are vitally important for modeling and simulation in biology, so if you want to go into any kind of biotechnology-related field, you should learn about this branch of mathematics. This text covers differential equations in a clear manner, provides lots of good exercises, and focuses on application rather than theory.
Linear Algebra: Step by Step – by Kuldeep Singh || Linear algebra is another area of mathematics which is vitally important for modeling and simulation in biology and bioengineering fields. This book goes over linear algebra in a clear fashion, has some illustrations to aid intuitive understanding, includes many good exercises, and emphasizes application rather than theory.
Brock Biology of Microorganisms – by Madigan, Bender, Buckley, Sattley, Stahl || For those who want to explore infectious disease and/or synthetic biology, it can be valuable to get acquainted with microbiology. This authoritative text is friendly to beginners in biology and has strong illustrations.
Molecular and Cellular Biology of Viruses – by Lostroh || This is a good book for virology in general. It has very pretty illustrations which are quite helpful to the reader. I do think that the book meanders too much in its explanations. The organization of the book as a whole seems a little haphazard as well. Nonetheless, this text can serve as a good reference if you want to read up on a specific type of virus and are looking for intuitive comprehension of its mechanisms.
Molecular Biology of the Cell – by Alberts, Johnson, Lewis, Raff, Roberts, Walter || A comprehensive and yet approachable book on molecular biology. It has numerous excellent illustrations, a crucial feature in any molecular biology text. It thoroughly covers a large array of important topics. There are even supplemental digital chapters on further topics in molecular biology for interested readers.
Essential Cell Biology – by Alberts, Hopkin, Johnson, Morgan, Raff, Roberts, Walter || Though this book is somewhat less detailed and thorough than the Molecular Biology of the Cell, it provides a more concise introduction to cell biology, while still covering enough detail to grant a good understanding of the subject. It also has great illustrations.
Neuroscience: Exploring the Brain – by Bear, Connors, Paradiso || This book talks about a wide range of topics in neurobiology, so it is useful for introducing neuroscience as a broad field of study. I found the chapters on sensory neuroscience to be especially strong. In my admittedly biased opinion, the book neglects computational neuroscience and modern neuroscientific techniques. If you are coming from a highly mathematical background and/or wanting to go into a mathematically-focused field of neuroscience, you might want to supplement this text with some computational neuroscience books (see the intermediate and advanced sections of this page).
Organic Chemistry as a Second Language: First Semester Topics – by Klein || Klein’s short books on organic chemistry are amazing at helping the reader to understand the core principles of the subject. The first semester topics text is especially good for explaining the principles governing structure and mechanisms in organic chemistry.
Organic Chemistry as a Second Language: Second Semester Topics – by Klein || The second installment in Klein’s short texts on organic chemistry is similarly fantastic for gaining intuitive understanding. It goes into more depth on why certain reaction mechanisms happen as well as covering spectroscopy topics.
Organic Chemistry – by Klein || Klein’s full-length textbook provides further detail on organic chemistry while still emphasizing skills and principles rather than memorization.
Principles of Anatomy and Physiology – by Tortora and Derrickson || Very long book, but wonderfully illustrated, clearly explained, and highly informative. I really appreciate how this text discusses molecular biology and biochemistry in the context of human physiology. It includes a wealth of fascinating details on how physiology works from the molecular level on up to the whole body. I especially enjoyed the chapter on endocrinology. For those who are medically inclined, there is also a lot of detail on the anatomical terminology (but this can easily be skimmed if you are not planning on going into medicine). Finally, there are numerous boxes which discuss specific diseases and other clinical subjects of special interest.
Raven Biology of Plants – by Evert and Eichhorn || An authoritative text on plant biology. Though I never got into this book much, I have heard great reviews from others. It covers a wide range of topics in botany and offers clear explanations as well as very nice illustrations and photographs. It spends a lot of time reviewing content from other areas of biology, which can be good or bad depending on your level of background.
Books which cover more specialized topics in various subfields of biology or cover broader fields of biology in more depth. In contrast to the previous texts, these books tend to go into more detail and assume that the reader has more background. They are often employed in upper-level undergraduate elective courses. It should be noted that the degree of background required for my “lower intermediate” and “upper intermediate” categories is a matter of opinion. People may find certain texts more challenging or less challenging depending on their background and learning style. That said, I think that these categories can still serve as a rough guide for those seeking to expand their knowledge of the biological sciences.
Introduction to Proteins: Structure, Function, and Motion – by Kessel and Ben-Tal || Discusses protein biochemistry and biophysics. This text does not go into great mathematical detail (it only includes relatively simple equations), but it does discuss the conceptual underpinnings of biophysical phenomena in a lot of detail. As an example, it contains some excellent biophysical explanations of why protein folding is such a challenging computational problem. The book also provides a wealth of information about how proteins operate in the larger cellular and physiological contexts. The illustrations are only moderately attractive, but still helpful from a practical perspective.
Biochemistry – by Voet and Voet || Though I have not personally used this book much, I have heard it is an excellent text from a number of sources, so I wanted to include it here. Voet’s textbook is known for going into a lot of detail, so it should serve you well if you are looking for a comprehensive discussion of general biochemistry. It also has very good illustrations.
An Introduction to Medicinal Chemistry – by Patrick || Beautiful book on drug design, drug development, and how drugs interact with the body. This textbook is really great because it clearly explains the fundamental principles of medicinal chemistry in a highly generalizable fashion. Its writing and diagrams really help the reader to understand the “why” underlying pharmacology. The text is also quite concise, direct, and practical in its presentation.
Developmental Biology – by Gilbert and Barresi || This book contains impressive details on the development of various organisms. It has beautiful diagrams and describes complicated signaling pathways in an engaging and meaningful manner. When I read Gilbert’s text, I get excited about how the process of organismal development follows a gorgeously complex extrapolation of fundamental chemical logic.
Fluorescence Microscopy: From Principles to Biological Applications – edited by Ulrich Kubitscheck || An excellent introduction to the engineering principles of fluorescence microscopy. This book provides background on optical physics, explains the physical mechanisms behind key types of modern fluorescence microscopy systems (e.g. confocal microscopy, light-sheet microscopy, etc.), and discusses how fluorescence itself works and is applied. While the text does not shy away from using the necessary mathematical tools to properly explain the subject, it is clear enough that even readers with relatively light backgrounds in physics should find it reasonably understandable.
Introduction to Medical Imaging: Physics, Engineering and Clinical Applications – by Barrie Smith and Webb || Clear and well-organized introduction to the main modalities of medical imaging. This text explains physical principles behind the operation of technologies such as magnetic resonance imaging, x-ray computed tomography, ultrasound, and more. It also discusses some important concepts in computational image processing. While mathematics certainly plays a key role in this book, it is overall fairly light on quantitative aspects. Depending on your goals, this can be advantageous or a drawback. The illustrations are helpful from a practical perspective, though not especially lush.
Bacterial Pathogenesis: A Molecular Approach – by Wilson, Winkler, Ho || Really nice book on the molecular mechanisms of bacterial pathogenesis. This book has a fair amount of detail on the subject but explains clearly. I own the 3rd edition rather than the more recent 4th edition, but I have had a chance to look through the 4th edition. It should be noted that the 4th edition has major updates including beautiful full-color illustrations which greatly enhance its explanatory power. The 3rd edition already had quite helpful diagrams, but the 4th edition appears to have taken this to a new level entirely.
Virology: Principles and Applications – by Carter and Saunders || This virology text is less comprehensive than many other virology books, but it makes up for this in that it explains viruses in a highly concise and pragmatic manner. The sections on bacteriophages and HIV are especially strong. For the reader who seeks to gain clear and direct understanding of the key molecular mechanisms used by viruses, this text is excellent.
Principles of Virology – by Flint, Racaniello, Rall, Skalka, Enquist || This book comes in two volumes. The first emphasizes molecular biology of viruses and the second emphasizes the pathogenesis and control of viruses. The diagrams are quite consistent, beautiful, and helpful. The text explains clearly and covers a lot of valuable topics. As a result of its thoroughness, this book may seem somewhat overwhelming, but it still is excellent as a reference and as a general source of virology knowledge.
Molecular biology and genetics
Molecular Biology of the Gene – by Watson, Baker, Bell, Gann, Levine, Losick || Classic text which discusses molecular genetics at a somewhat higher level than a typical introductory molecular biology book. Great illustrations and clear explanations aid the reader’s understanding of the intricate molecular machines which tirelessly carry out the myriad of tasks necessary to run the genome and transcriptome. The book is fairly long, but if you already know some molecular biology, you can certainly jump around to learn more details about specific areas of interest.
Molecular Genetics of Bacteria – by Snyder, Peters, Henkin, Champness || Similar to Watson’s text (above), but specifically covering bacterial molecular genetics rather than molecular genetics in general. In the 4th edition, the illustrations convey strong understanding of molecular mechanisms, though they are not as sumptuous as the diagrams in some biology books. In the 5th edition, the illustrations are both sumptuous and convey strong understanding of molecular mechanisms. There is a lot of great material here which can be especially useful for biohackers (and other researchers) who want to use the bacterial cell as a chassis for synthetic biology.
Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience – by Baars and Gage || Discusses cognitive neuroscience from both neuropsychological and neurophysiological perspectives. This text goes over a lot of psychological experiments for those who are interested in behavioral neuroscience, but also discusses mechanisms for those who want to focus more on the underlying ways that the brain operates. In my opinion, the largest drawback of this book is that it is weak on cellular neurophysiology.
Fundamentals of Computational Neuroscience – by Trappenberg || An excellent introduction to computational neuroscience for someone coming into the area from a less quantitatively-focused background. You will still need to know calculus and maybe a small amount of differential equations, but the book is less mathematically intense than most other computational neuroscience texts. Furthermore, the book explains key ideas from areas of mathematics such as linear algebra and probability so that the reader does not necessarily have to already know these subjects. It is fairly concise yet still clearly explains a wide variety of topics from the field.
Molecular Driving Forces: Statistical Thermodynamics in Biology, Chemistry, Physics, and Nanoscience – by Dill and Bromberg || This book features elegant explanations of how statistical thermodynamics and molecular physics apply to biology and nanotechnology. In my opinion, one of its strengths is its excellent organization. The text also features very clean formatting which makes it a smoother read. Though this text is mathematics-focused, it reviews key concepts in probability and multivariable calculus for readers who have less quantitative backgrounds. There are some great chapters on foundational topics (e.g. entropy, the Boltzmann distribution, electrostatics, etc.) as well as numerous chapters on exciting applications such as polymer physics, biochemical machines and nanomachines, and cooperative binding.
The Biology of Cancer – by Weinberg || This book provides an amazing introduction to the molecular biology, genetics, biochemistry, and treatment of cancer. Lots of great content on tumor pathogenesis from perspectives of cell signaling, DNA repair and recombination, tissue microenvironment, immunobiological aspects, virology, and more. The book features a wealth of breathtaking diagrams and histological photographs which are colorful, detailed, and highly informative. Though some of the book goes through a lot of basic molecular biology review, readers who feel comfortable with that material can easily skip to more advanced sections.
Books that cover specialized topics in depth and books that involve somewhat complicated mathematics are listed here. These texts typically assume that you have a fair amount of background. They are usually employed at the senior undergraduate level or at the graduate level (but please do not let this discourage you from trying them out regardless). Note that a few of these might be called monographs rather than textbooks. Because of the breadth of the biological sciences, there are many thousands of possible titles to include in this section, so please realize that these texts represent a small sampling.
Protein Actions: Principles and Modeling – by Bahar, Jernigan, Dill || Excellent text on the biophysics of proteins. This book goes through a lot of challenging content on physical chemistry and computational modeling, yet it is presented in a very understandable way. Full color illustrations, clearly organized equations, and elegant explanations contribute to its pedagogical strength.
Epigenetics – by Allis, Caparros, Jenuwein, Reinberg || Very detailed but also very rewarding, this book goes over epigenetics in a series of engaging chapters written by expert authors. Despite having different authors for different chapters, the book uses consistent illustrations throughout. The illustrations are also of high quality and are in full color, which helps to motivate the reader and aids understanding. This text covers the epigenetics of a series of model organisms as well as a myriad of key topics in mammalian epigenetic research.
Mobile DNA III – edited by Craig, Chandler, Gellert, Lambowitz, Rice, Sandmeyer || Very long and highly technical, this monograph delves deep into research on topics such as transposons, recombination, and programmed DNA rearrangements. Despite its technical character, this book still includes a myriad of helpful (and colorful) diagrams and usually has good explanations. I especially enjoyed the chapter on integrons.
Fundamentals of Biomedical Optics || A good text on microscopy and other forms of imaging as well as the underlying optical physics involved in the engineering of imaging systems. The book is well-organized, engagingly illustrated, detailed, and emphasizes generalizable principles. Many parts of this text can be a struggle for a reader without a strong physics background, but this makes sense given the subject matter and level of depth.
Bioconjugate Techniques – by Hermanson || A great reference text for those interested in nanobiotechnology, drug delivery, contrast agents, and other areas involving bioconjugates. This book is filled with beautiful diagrams which aid understanding. The explanations are a less concise than would be ideal, though they are still effective. The text also provides lots of clear laboratory protocols for interested researchers.
The Nature of the Mechanical Bond: From Molecules to Machines – by Bruns and Stoddart || Beautiful and comprehensive text on supramolecular chemistry, an area which is highly relevant to bioengineering disciplines. It focuses on the synthesis and dynamics of supramolecular structures which perform desired mechanical actions. The book is somewhat long due to its high level of detail and coverage, but it is gorgeously illustrated and well-written. There is a fair amount of historical content included throughout and the first chapter discusses some connections between supramolecular chemistry and art. I would recommend having a strong understanding of your chemical thermodynamics, chemical kinetics, organic chemistry, and perhaps even some organometallic chemistry when reading this book. While this kind of background knowledge is not absolutely necessary, it can certainly help to get more out of the text.
Dendrites – edited by Stuart, Spruston, Häusser || Beautiful text which goes through the biology of dendrites in a series of engaging chapters by expert authors. Exceptionally well-made diagrams (with full color also) help the reader to understand concepts and useful tables facilitate referencing of detailed information. One drawback of the book is that it is lacking in concision, though this is partly due to the need to discuss ambiguity in content at the frontiers of dendrite research.
Handbook of Brain Microcircuits – edited by Shepherd and Grillner || This book provides a series of short reviews on the mechanistic workings of neuronal microcircuits in both vertebrate and invertebrate systems. Though brief, each chapter packs in a lot of interesting information. As with many of the texts I have chosen for this list, the text features many full color diagrams to aid the reader. If you want to see a myriad of examples of the precise mechanisms which produce cognition and behavior, this book is excellent. Of course, the book is far from comprehensive; there are many papers which examine other neural circuits and there remains a vast universe of neural circuits still waiting to be uncovered.
Neuronal Dynamics: From single neurons to networks and models of cognition – by Gerstner, Kistler, Naud, Paninski || An elegantly-written computational neuroscience book which has been made freely available by the authors online. Lots of mathematical modeling is discussed in this text, but it explains the mathematics clearly and does not muddle understanding through unnecessary digressions. Note that this book focuses much more on the mathematical models than on actual coding (depending on your goals, you may find this beneficial or detrimental). This textbook is great for facilitating deeper understanding of computational neuroscience.
Fundamentals of Brain Network Analysis – by Fornito, Zalesky, Bullmore || An excellent text on using graph theory in neuroscience. It is beautifully illustrated, well-organized, and clearly explained. The mathematical tools of graph theory and complex networks are made accessible to those coming from a biological background. My only complaint about this book is that it is somewhat lacking in conciseness. My personal view is that it would have been possible to explain the subject more concisely without losing out on the depth and other beneficial qualities. Nonetheless, the book can be very rewarding (and enjoyable).
cover images are from Amazon.com
banner image is from ThailandTatler.com
Disclaimer: this list is non-comprehensive, these are just the people who happened to pop into my head. There are certainly many more people who may belong on this list. Furthermore, everyone is “interesting” in his/her own unique way, so please do not feel badly if you do not happen to have made it into this personal compilation.
Adam Marblestone, Albert Einstein, Aleksei Aksimentiev, Allen Ginsberg, Anders Sandberg, Anton Arkhipov, Anushree Chatterjee, Arthur C. Clarke, Aubrey de Grey, Barack Obama, Bertrand Russell, Bill Gates, Bobby Fischer, Brian David Johnson, Carson Bruns, Charles Lieber, Christof Koch, Christopher Voigt, Cole Hugelmeyer, Conrad Farnsworth, Craig Venter, David Baker, David Pearce, Deblina Sarkar, Dhash Shrivathsa, Donald Ingber, Donna Haraway, Drue Kataoka, Easton LaChappelle, Ed Boyden, Elon Musk, Emily St. John Mandel, Emma Watson, Eric Betzig, Erik Drexler, Erin Smith, Fei Chen, Feng Zhang, Francis Collins, Francis Crick, Freeman Dyson, F. Scott Fitzgerald, Garry Kasparov, Gene Roddenberry, George Church, George Whitesides, Greg Bear, Greg Egan, Greta Thunberg, Grimes, Hannu Rajaniemi, Henry Markram, Hod Lipson, Isaac Asimov, Jack Andraka, Jacob Barnett, James Collins, James Watson, Jay Keasling, Jeff Gore, Jennifer Doudna, J.K. Rowling, John von Neumann, Kai Kloepfer, Karan Jerath, Karl Deisseroth, Karl Friston, Kazuo Ishiguro, Ken Rinaldo, Kevin Esvelt, Kurt Gödel, Kwanghun Chung, Laura Deming, Liz Parrish, Magnus Carlsen, Mark Bathe, Martin Fussenegger, M.C. Escher, Neri Oxman, Nicole Ticea, Nikola Tesla, Norman Borlaug, Octavia Butler, Orit Peleg, Pamela Silver, Peter Diamandis, Peter Singer, Poppy, Ray Kurzweil, Raymond Wang, Robert Langer, Robert McCall, Ron Weiss, Rosalind Franklin, Ryan Robinson, Sanath Devalapurkar, Sebastian Seung, Simon Stålenhag, Srinivasa Ramanujan, Stephen Baxter, Steven Pinker, Suganth Kannan, Taylor Swift, Taylor Wilson, Terence Tao, Theodore Berger, Thomas Crowther, Tracy K. Smith, Wei-Chung Allen Lee, William Shih, Yayoi Kusama
Cover image: the photographs which comprise the cover image were taken from various online sources. If you own one of these pictures and would like for it to be removed from the cover image, feel free to let me know and I will do so.
Note on links: as this page ages, some of the links may begin to break due to changes at the target sites. Feel free to let me know if you see this happen with any particular entries and I will see what I can do about fixing them.