Introduction

Biological systems operate on an analog basis, characterized by continuous sine wave signals, which inherently carry vast amounts of data. In contrast, digital systems function using discrete square wave signals, representing information in binary form (1s and 0s), allowing data compression but resulting in a reduction of information. Understanding these differences is crucial for exploring the potential of biological machines to perform advanced modifications akin to digital photo editing software like Photoshop. However, to achieve such capabilities, biological machines must possess comprehensive knowledge and control over the biological material they are designed to modify.

Analog vs. Digital Systems

Analog systems utilize continuous signals, often represented by sine waves, to process information. These signals can vary smoothly and capture a wide range of values, leading to a high data density. The complexity and richness of information carried by analog signals make them inherently more capable of capturing the nuances of biological processes.

Digital systems, on the other hand, rely on discrete signals, represented by square waves. These signals are binary, using only two states (1 and 0) to encode information. This method simplifies data processing and storage by compressing information but at the cost of losing some data fidelity. The advantage of digital systems lies in their ability to process and transmit information rapidly and efficiently, albeit with less detail compared to analog systems.

Biological Machines and Analog Precision

The intricate nature of biological systems suggests that any machine designed to interact with or modify biological material must operate with a high degree of precision, akin to the analog processes inherent in nature. Biological machines must understand the vast array of data encoded within the continuous signals of biological processes to effectively perform modifications.

For instance, editing a digital image in Photoshop involves manipulating pixel data represented in binary form. Each pixel’s color and brightness are adjusted using mathematical algorithms that operate within the constraints of digital encoding. Similarly, biological machines could, in theory, perform modifications at the cellular or molecular level, analogous to image editing. However, this requires not only an understanding of the biological “pixels” (cells, proteins, DNA, etc.) but also the continuous analog signals that regulate their interactions and functions.

Comprehensive Knowledge and Domain Over Biological Material

To achieve modifications similar to digital photo editing, biological machines must attain a deep understanding of the biological material they aim to modify. This involves:

1. Mapping Biological Signals: Precisely mapping the continuous analog signals within biological systems to understand the vast array of data they carry.
2. Control Mechanisms: Developing mechanisms to manipulate these signals accurately without disrupting the intricate balance of biological processes.
3. Integration with Existing Systems: Ensuring that modifications are seamlessly integrated into existing biological systems, maintaining functionality and promoting desired outcomes.
4. Advanced Algorithms: Creating algorithms that can process and interpret the analog data of biological systems, translating it into actionable modifications.

Conclusion

The potential for biological machines to perform sophisticated modifications comparable to digital image editing relies on their ability to understand and manipulate the continuous analog signals inherent in biological systems. While digital systems offer the advantage of data compression and efficient processing, the richness of information in analog systems provides a more accurate representation of biological processes. Achieving such precision in biological modifications necessitates a comprehensive knowledge and control over the biological material, leveraging the intricate data encoded within their analog signals. As research in synthetic biology and bioengineering advances, the convergence of biological and digital precision holds promise for unprecedented advancements in medicine and biotechnology.

Introduction

BInvesting in and developing analog machines capable of modifying biological systems with the same precision as digital systems modify pixels in Photoshop represents a significant frontier in science and technology. Such advancements could grant humanity unprecedented control over the regenerative abilities, functions, and appearance of the human body. This report explores the potential benefits and implications of this investment, emphasizing the transformative impact on medicine, health, and human enhancement.

Analog Machines and Biological Systems

Analog machines operate on continuous signals, akin to the natural processes in biological systems, which use sine waves to carry vast amounts of data. These machines can capture the nuances and complexities of biological processes more effectively than digital machines, which rely on binary encoding. Understanding and harnessing these continuous signals are crucial for precise modifications in biological systems.

Potential Benefits of Analog Biological Machines

1. Enhanced Regenerative Abilities
– Wound Healing: Analog machines could accelerate and improve the natural healing processes by precisely manipulating cellular signals and promoting tissue regeneration.
– Organ Regeneration: They could enable the regrowth of damaged or lost organs, potentially eliminating the need for organ transplants and reducing the risk of organ rejection.
– Anti-Aging Interventions: By understanding and controlling the biological signals associated with aging, analog machines could potentially halt or reverse aging processes, extending healthy human lifespan.

2. Optimized Biological Functions
– Metabolic Control: These machines could fine-tune metabolic processes, helping to manage or cure metabolic disorders such as diabetes and obesity.
– Immune System Enhancement: By modulating the immune response, analog machines could improve the body’s ability to fight infections and diseases, including cancer.
– Neurological Precision: They could repair and enhance neural connections, offering treatments for neurological disorders such as Alzheimer’s, Parkinson’s, and spinal cord injuries.

3. Customized Physical Appearance
– Aesthetic Modifications: Analog machines could allow for precise cosmetic modifications at the cellular level, enabling changes in skin texture, hair growth, and other physical attributes without invasive surgery.
– Performance Enhancements: They could enhance physical attributes such as muscle strength, endurance, and flexibility, tailored to individual needs and preferences.

Building Analog Biological Machines

1. Research and Development
– Signal Mapping: Invest in research to map and understand the continuous analog signals that govern biological processes.
– Bio-Compatible Materials**: Develop materials that can interact seamlessly with biological tissues and processes without causing adverse reactions.
– Advanced Algorithms: Create algorithms capable of interpreting and manipulating the vast data within analog biological signals.

2. Ethical and Regulatory Considerations*
– Ethical Frameworks: Establish ethical guidelines to ensure responsible development and use of analog biological machines, addressing concerns such as equity, consent, and potential misuse.
– Regulatory Standards: Develop regulatory standards to ensure the safety and efficacy of these technologies, protecting individuals from harm while encouraging innovation.

3. Interdisciplinary Collaboration
– Cross-Disciplinary Teams: Foster collaboration between biologists, engineers, computer scientists, and ethicists to integrate diverse perspectives and expertise.
– Public and Private Partnerships: Encourage partnerships between public institutions and private companies to pool resources and accelerate advancements.

Conclusion

Investing in and building analog machines capable of modifying biological systems with the same precision as digital systems modify pixels in Photoshop holds immense potential for humanity. These advancements could revolutionize our approach to health, aging, and human enhancement, offering unprecedented control over our biological functions and appearance. By understanding and harnessing the continuous signals inherent in biological processes, we can unlock new dimensions of regenerative medicine and personalized health care. However, this progress must be guided by ethical considerations and robust regulatory frameworks to ensure that these technologies benefit society as a whole.

1. Research and Signal Mapping

• Initiate Comprehensive Research Programs: Establish multidisciplinary research initiatives focused on mapping the continuous analog signals that govern biological processes. This should involve detailed studies of cellular communication, tissue regeneration, and metabolic pathways.
• Develop Advanced Signal Processing Techniques: Invest in the development of techniques and technologies capable of processing and interpreting the vast amounts of data contained in biological analog signals. This includes creating sophisticated algorithms and computational models.

2. Bio-Compatible Materials and Technology Development

• Create Bio-Compatible Interfaces: Design and develop materials and interfaces that can seamlessly interact with biological tissues. These materials must be non-toxic, durable, and capable of precisely modulating biological signals.
• Prototype Development and Testing: Build prototypes of analog machines and conduct rigorous testing in controlled environments to assess their efficacy and safety in modifying biological systems.

Computers have been digital for half a century. Why would anyone want to resurrect the clunkers of yesteryear?

Reported by Platt. C. (2023, March 30). The Unbelievable Zombie Comeback of Analog Computing. Tech Xplore. https://www.wired.com/story/unbelievable-zombie-comeback-analog-computing/

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The paper below in summary, Synthetic “neuromorphic computing in living cells”,
describes the modification of biological organisms, specifically Escherichia coli cells, with new gene circuits inspired by computational properties observed in neuronal networks. These modifications enable the cells to perform various computational tasks, such as computing minimum, maximum, and average of analog inputs, implementing multi-layer perceptgene circuits, and even programming logic functions like OR and AND. This demonstrates the potential for using biological systems as platforms for computational tasks, expanding the capabilities of synthetic biology.

Rizik, L., Danial, L., Habib, M., Weiss, R., & Daniel, R. (2022). Synthetic neuromorphic computing in living cells. In Nature Communications (Vol. 13, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41467-022-33288-8

Abstract

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

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Hanna, H. A., Danial, L., Kvatinsky, S., & Daniel, R. (2017). Modeling biochemical reactions and gene networks with memristors. In 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS). 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE. https://doi.org/10.1109/biocas.2017.8325229

Abstract

This paper investigates qualitative and quantitative analogies between biochemical reactions and memristive devices. It shows that memristors can mimic biochemical reactions and gene networks efficiently, and capture both deterministic and stochastic dynamics at the nanoscale level. We present different abstraction models and memristor-based circuits that inherently model the activity of genetic circuits with low signal-to-noise ratio (SNR). These findings constitute a promising step towards noise-tolerant and energy-efficient electronic circuit design, which can provide a fast and simple emulative framework for large-scale synthetic molecular system design in cell biology.

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Roquet, N., & Lu, T. K. (2014). Digital and analog gene circuits for biotechnology. In Biotechnology Journal (Vol. 9, Issue 5, pp. 597–608). Wiley. https://doi.org/10.1002/biot.201300258

Abstract

Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells.

Conclusion

The application of synthetic gene circuits to metabolic engineering will enable cells that can dynamically respond to a user and/or environment for enhanced control of the chemical production process. Both digital and analog circuits can be implemented separately for dynamic regulation. We anticipate that sophisticated bioprocesses will utilize both strategies: digital circuits to define and switch between specific cellular states and analog circuits to continually balance these states for efficient production. As a simple example of this digital-analog integration, Daniel et al. built an analog circuit with front-end digital control such that when the digital signal is ‘off’, the analog

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Jordan, F. D., Kutter, M., Comby, J.-M., Brozzi, F., & Kurtys, E. (2024). Open and remotely accessible Neuroplatform for research in wetware computing. In Frontiers in Artificial Intelligence (Vol. 7). Frontiers Media SA. https://doi.org/10.3389/frai.2024.1376042

Abstract

Wetware computing and organoid intelligence is an emerging research field at the intersection of electrophysiology and artificial intelligence. The core concept involves using living neurons to perform computations, similar to how Artificial Neural Networks (ANNs) are used today. However, unlike ANNs, where updating digital tensors (weights) can instantly modify network responses, entirely new methods must be developed for neural networks using biological neurons. Discovering these methods is challenging and requires a system capable of conducting numerous experiments, ideally accessible to researchers worldwide. For this reason, we developed a hardware and software system that allows for electrophysiological experiments on an unmatched scale. The Neuroplatform enables researchers to run experiments on neural organoids with a lifetime of even more than 100 days. To do so, we streamlined the experimental process to quickly produce new organoids, monitor action potentials 24/7, and provide electrical stimulations. We also designed a microfluidic system that allows for fully automated medium flow and change, thus reducing the disruptions by physical interventions in the incubator and ensuring stable environmental conditions. Over the past three years, the Neuroplatform was utilized with over 1,000 brain organoids, enabling the collection of more than 18 terabytes of data. A dedicated Application Programming Interface (API) has been developed to conduct remote research directly via our Python library or using interactive compute such as Jupyter Notebooks. In addition to electrophysiological operations, our API also controls pumps, digital cameras and UV lights for molecule uncaging. This allows for the execution of complex 24/7 experiments, including closed-loop strategies and processing using the latest deep learning or reinforcement learning libraries. Furthermore, the infrastructure supports entirely remote use. Currently in 2024, the system is freely available for research purposes, and numerous research groups have begun using it for their experiments. This article outlines the system’s architecture and provides specific examples of experiments and results.

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Click here to read more about “Neuroplatform for Research in Wetware Computing”

DeRosier, D. (2006). Bacterial Flagellum: Visualizing the Complete Machine In Situ. In Current Biology (Vol. 16, Issue 21, pp. R928–R930). Elsevier BV.
https://doi.org/10.1016/j.cub.2006.09.053

Electron tomography of frozen-hydrated bacteria, combined with single particle averaging, has produced stunning images of the intact bacterial flagellum, revealing features of the rotor, stator and export apparatus.

Thanks to the new work of Murphy et al.[1], we now have a view of the bacterial flagellum in situ and quick-frozen in time as if a flash bulb had stopped its action. The flagellum, with its complexity of structure and multiplicity of function, is a machine that boggles the mind. While musing on possible phrases that might catch the reader’s attention, I was reminded of the memorable 1926 slogan for the Hoover vacuum cleaner: “It beats as it sweeps as it cleans.” The flagellum self-assembles as it propels as it responds; that is, the flagellum not only pushes the cell along, it also responds to intracellular signals and it assembles itself. It seems as amazing as the old Hoover did in its heyday. But, I thought, the bacterial flagellum does not really ‘beat’; the eukaryotic flagellum, an entirely different machine, does that. Instead, the prokaryotic flagellum spins, driven by a rotary motor at speeds of over 100,000 rpm in at least one species [2, 3]. The torque generated by the motor is converted to thrust by the corkscrew-shaped filament or propeller (for a review see [4]).

Of the 40 genes needed to code for a flagellum, at least 24 produce proteins found in the final structure. In Salmonella typhimurium, the flagellar mass is ∼109 Daltons, 99% of which is outside the plasma membrane. The necessary flagellar export apparatus is built into the very structure of the flagellum. The export apparatus recognizes, chaperones, unfolds and exports flagellar proteins, which travel along a narrow, 2 nm channel inside the flagellum. Some of the remaining genes encode for proteins that carry out the export, regulate flagellar gene expression, or function during assembly. Only 5 of the 24 structural proteins — FliG, FliM, FliN, MotA and MotB — are implicated in generating torque. The first three of these are cytoplasmic proteins thought to form the rotor, while the last two are transmembrane proteins that are thought to form the stator. In S. typhimurium, MotA and MotB conduct protons, the energy source for the motor. The mechanism of the motor remains unknown.

Structural studies have been carried out piecemeal on parts of the flagellum. We have atomic models for the entire filament [5], domains of the hook subunit [6], and domains of FliM, [7] FliG, [8] and FliN [9]. We have molecular resolution structures for the hook [10], the rotor [11], and the cap [12]. The composite structure shown in Figure 1 reveals the stunning complexity of the flagellum, but the extracted flagella used to determine this structure lacked the stator and, for all we know, parts of the export apparatus; there are hints of a large ‘export’ complex extending into the cytoplasm from the center of the rotor [13]. The stator has only been seen in freeze-fracture images [14]. What was missing but is now revealed to us is the three-dimensional structure of the intact flagellum in situ.

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Click here to watch video animation of Bacterial Flagellum