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This technical monograph provides a comprehensive overview of the field of quantum biology. It approaches quantum biology from a physical perspective with core quantum mechanical concepts presented foremost to provide a theoretical foundation for the field. An extensive body of research is covered to clarify the significance of quantum biology as a scientific field, outlining the field's long-standing importance in the historical development of quantum theory. This lays the essential groundwork to enable further advances in nanomedicine and biotechnology. Written for academics, biological science researchers, physicists, biochemists, medical technologists, and students of quantum mechanics, this text brings clarity to fundamental advances being made in the emerging science of quantum biology.

This article frames the relation between biology and physics by characterizing the former as a subdiscipline rather than a special case of the latter. To do this, we posit biological physics as the science of living matter in contrast to classic biophysics, the study of organismal properties by physical techniques. At the scale of the individual cell, living matter is nonunitary, i.e., not composed of aggregated subunits, and has features (e.g., intracellular organizational arrangements and biomolecular condensates) that are unlike any materials of the nonliving world. In transiently or constitutively multicellular forms (social microorganisms, animals, plants), living matter sustains physical processes that are generic (shared with nonliving matter, e.g., subunit communication by molecular diffusion in cellular slime molds), biogeneric (analogous to nonliving matter but realized through cellular activities, e.g., subunit demixing in animal embryos) or nongeneric (pertaining to sui generis materials, e.g., budding of active solids in plants). This "forms of matter" perspective is philosophically situated in the dialectical materialism of Engels and Hessen and the multilevel physica

Biological systems are generally complicated and/or complex. In the former approach, one sets up a model with a large number of parameters to describe the system in detail. The latter approach focuses on understanding the universal aspects of biological systems. In this case, an appropriate simple model represents a universality class. The extraction of universal properties is supported by evolutionary robustness and the reduction of dimensionality in high-dimensional states. Integrating the data-driven omics approach with the universality approach is an important step in systems biology.

Understanding the biological mechanisms of disease is crucial for medicine, and in particular, for drug discovery. AI-powered analysis of genome-scale biological data holds great potential in this regard. The increasing availability of single-cell RNA sequencing data has enabled the development of large foundation models for disease biology. However, existing foundation models only modestly improve over task-specific models in downstream applications. Here, we explored two avenues for improving single-cell foundation models. First, we scaled the pre-training data to a diverse collection of 116 million cells, which is larger than those used by previous models. Second, we leveraged the availability of large-scale biological annotations as a form of supervision during pre-training. We trained the \model family of models comprising six transformer-based state-of-the-art single-cell foundation models with 70 million, 160 million, and 400 million parameters. We vetted our models on several downstream evaluation tasks, including identifying the underlying disease state of held-out donors not seen during training, distinguishing between diseased and healthy cells for disease conditions and

We developed a theory showing that under appropriate normalizations and rescalings, temperature response curves show a remarkably regular behavior and follow a general, universal law. The impressive universality of temperature response curves remained hidden due to various curve-fitting models not well-grounded in first principles. In addition, this framework has the potential to explain the origin of different scaling relationships in thermal performance in biology, from molecules to ecosystems. Here, we summarize the background, principles and assumptions, predictions, implications, and possible extensions of this theory.

In this paper, we propose and study several inverse problems of determining unknown parameters in nonlocal nonlinear coupled PDE systems, including the potentials, nonlinear interaction functions and time-fractional orders. In these coupled systems, we enforce non-negativity of the solutions, aligning with realistic scenarios in biology and ecology. There are several salient features of our inverse problem study: the drastic reduction in measurement/observation data due to averaging effects, the nonlinear coupling between multiple equations, and the nonlocality arising from fractional-type derivatives. These factors present significant challenges to our inverse problem, and such inverse problems have never been explored in previous literature. To address these challenges, we develop new and effective schemes. Our approach involves properly controlling the injection of different source terms to obtain multiple sets of mean flux data. This allows us to achieve unique identifiability results and accurately determine the unknown parameters. Finally, we establish a connection between our study and practical applications in biology, further highlighting the relevance of our work in real-

Systems biology relies on mathematical models that often involve complex and intractable likelihood functions, posing challenges for efficient inference and model selection. Generative models, such as normalizing flows, have shown remarkable ability in approximating complex distributions in various domains. However, their application in systems biology for approximating intractable likelihood functions remains unexplored. Here, we elucidate a framework for leveraging normalizing flows to approximate complex likelihood functions inherent to systems biology models. By using normalizing flows in the Simulation-based inference setting, we demonstrate a method that not only approximates a likelihood function but also allows for model inference in the model selection setting. We showcase the effectiveness of this approach on real-world systems biology problems, providing practical guidance for implementation and highlighting its advantages over traditional computational methods.

The Chem2Bio2RDF portal is a Linked Open Data (LOD) portal for systems chemical biology aiming for facilitating drug discovery. It converts around 25 different datasets on genes, compounds, drugs, pathways, side effects, diseases, and MEDLINE/PubMed documents into RDF triples and links them to other LOD bubbles, such as Bio2RDF, LODD and DBPedia. The portal is based on D2R server and provides a SPARQL endpoint, but adds on few unique features like RDF faceted browser, user-friendly SPARQL query generator, MEDLINE/PubMed cross validation service, and Cytoscape visualization plugin. Three use cases demonstrate the functionality and usability of this portal.

AlphaFold 3 represents a transformative advancement in computational biology, enhancing protein structure prediction through novel multi-scale transformer architectures, biologically informed cross-attention mechanisms, and geometry-aware optimization strategies. These innovations dramatically improve predictive accuracy and generalization across diverse protein families, surpassing previous methods. Crucially, AlphaFold 3 embodies a paradigm shift toward differentiable simulation, bridging traditional static structural modeling with dynamic molecular simulations. By reframing protein folding predictions as a differentiable process, AlphaFold 3 serves as a foundational framework for integrating deep learning with physics-based molecular

The discovery of general principles underlying the complexity and diversity of cellular and developmental systems is a central and long-standing aim of biology. Whilst new technologies collect data at an ever-accelerating rate, there is growing concern that conceptual progress is not keeping pace. We contend that this is due to a paucity of appropriate conceptual frameworks to serve as a basis for general theories of mesoscale biological phenomena. In exploring this issue, we have developed a foundation for one such framework, termed the Core and Periphery (C&P) hypothesis, which reveals hidden generality across the diverse and complex behaviors exhibited by cells and tissues. Here, we present the C&P concept, provide examples of its applicability across multiple scales, argue its consistency with evolution, and discuss key implications and open questions. We propose that the C&P hypothesis could unlock new avenues of conceptual progress in cell and developmental biology.

I believe an atomic biology is needed to supplement present day molecular biology, if we are to design and understand proteins, as well as define, make, and use them. Topics in the paper are molecular biology and atomic biology. Electrodiffusion in the open channel. Electrodiffusion in mixed electrolytes. Models of permeation. State Models of Permeation are Inconsistent with the Electric Field. Making models in atomic biology. Molecular dynamics. Temporal Limitations; Spatial Limitations; Periodic boundary conditions. Hierarchy of models of the open channel. Stochastic Motion of the Channel. Langevin Dynamics. Simulations of the Reaction Path: the Permion. Chemical reactions. What was wrong? Back to the hierarchy: Occam's razor can slit your throat. Poisson-Nernst-Planck PNP Models Flux Ratios; Pumping by Field Coupling. Gating in channels of one conformation. Gating by Field Switching; Gating Current; Gating in Branched Channels; Blocking. Back to the hierarchy: Linking levels. Is there a theory? At what level will the adaptation be found? Simplicity, evolution, and natural function.

The number of available constraints on the Universe during and before cosmic reionization is rapidly growing. These are often scattered across inhomogeneous formats, unit systems and sampling strategies. In this paper, I introduce CoReCon, a Python package designed to provide a growing set of constraints on key physical quantities related to the Epoch of Reionization and a platform for the high-redshift research community to collect and store, in an open way, current and forthcoming observational constraints.

OpenPodcar is a low-cost, open source hardware and software, autonomous vehicle research platform based on an off-the-shelf, hard-canopy, mobility scooter donor vehicle. Hardware and software build instructions are provided to convert the donor vehicle into a low-cost and fully autonomous platform. The open platform consists of (a) hardware components: CAD designs, bill of materials, and build instructions; (b) Arduino, ROS and Gazebo control and simulation software files which provide standard ROS interfaces and simulation of the vehicle; and (c) higher-level ROS software implementations and configurations of standard robot autonomous planning and control, including the move_base interface with Timed-Elastic-Band planner which enacts commands to drive the vehicle from a current to a desired pose around obstacles. The vehicle is large enough to transport a human passenger or similar load at speeds up to 15km/h, for example for use as a last-mile autonomous taxi service or to transport delivery containers similarly around a city center. It is small and safe enough to be parked in a standard research lab and be used for realistic human-vehicle interaction studies. System build cost f

Though it goes without saying that linear algebra is fundamental to mathematical biology, polynomial algebra is less visible. In this article, we will give a brief tour of four diverse biological problems where multivariate polynomials play a central role -- a subfield that is sometimes called "algebraic biology." Namely, these topics include biochemical reaction networks, Boolean models of gene regulatory networks, algebraic statistics and genomics, and place fields in neuroscience. After that, we will summarize the history of discrete and algebraic structures in mathematical biology, from their early appearances in the late 1960s to the current day. Finally, we will discuss the role of algebraic biology in the modern classroom and curriculum, including resources in the literature and relevant software. Our goal is to make this article widely accessible, reaching the mathematical biologist who knows no algebra, the algebraist who knows no biology, and especially the interested student who is curious about the synergy between these two seemingly unrelated fields.

Location tracking with global navigation satellite systems (GNSS), such as the GPS, is used in many applications, including the tracking of wild animals for research. Snapshot GNSS is a technique that only requires milliseconds of satellite signals to infer the position of a receiver. This is ideal for low-power applications such as animal tracking. However, there are few existing snapshot systems, none of which is open source. To address this, we developed SnapperGPS, a fully open-source, low-cost, and low-power location tracking system designed for wildlife tracking. SnapperGPS comprises three parts, all of which are open-source: (i) a small, low-cost, and low-power receiver; (ii) a web application to configure the receiver via USB; and (iii) a cloud-based platform for processing recorded data. This paper presents the hardware side of this project. The total component cost of the receiver is under $30, making it feasible for field work with restricted budgets and low recovery rates. The receiver records very short and low-resolution samples resulting in particularly low power consumption, outperforming existing systems. It can run for more than a year on a 40 mAh battery. We eval

We determine the conditions for the equivalence between the multi-time expectation values of a general finite-dimensional open quantum system when interacting with, respectively, an environment undergoing a free unitary evolution or a discrete environment under a free evolution fixed by a proper Gorini-Kossakowski-Lindblad-Sudarshan generator. We prove that the equivalence holds if both environments are bosonic and Gaussian and if the one- and two-time correlation functions of the corresponding interaction operators are the same at all times. This result leads to a non-perturbative evaluation of the multi-time expectation values of operators and maps of open quantum systems interacting with a continuous set of bosonic modes by means of a limited number of damped modes, thus setting the ground for the investigation of open-system multi-time quantities in fully general regimes.

It is well known that the state operator of an open quantum system can be generically represented as the solution of a time-local equation -- a quantum master equation. Unraveling in quantum trajectories offers a picture of open system dynamics dual to solving master equations. In the unraveling picture, physical indicators are computed as Monte-Carlo averages over a stochastic process valued in the Hilbert space of the system. This approach is particularly adapted to simulate systems in large Hilbert spaces. We show that the dynamics of an open quantum system generically admits an unraveling in the Hilbert space of the system described by a Markov process generated by ordinary stochastic differential equations for which rigorous concentration estimates are available. The unraveling can be equivalently formulated in terms of norm-preserving state vectors or in terms of linear ``ostensible" processes trace preserving only on average. We illustrate the results in the case of a two level system in a simple boson environment. Next, we derive the state-of-the-art form of the Diosi-Gisin-Strunz Gaussian random ostensible state equation in the context of a model problem. This equation pro

Synthetic biology is the engineering of cellular networks. It combines principles of engineering and the knowledge of biological networks to program the behavior of cells. Computational modeling techniques in conjunction with molecular biology techniques have been successful in constructing biological devices such as switches, oscillators, and gates. The ambition of synthetic biology is to construct complex systems from such fundamental devices, much in the same way electronic circuits are built from basic parts. As this ambition becomes a reality, engineering concepts such as interchangeable parts and encapsulation will find their way into biology. We realize that there is a need for computational tools that would support such engineering concepts in biology. As a solution, we have developed the software Athena that allows biological models to be constructed as modules. Modules can be connected to one another without altering the modules themselves. In addition, Athena houses various tools useful for designing synthetic networks including tools to perform simulations, automatically derive transcription rate expressions, and view and edit synthetic DNA sequences. New tools can be i

We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely the largest generally available multilingual collection of LLM pre-training data. These datasets are derived from web crawls from different sources and accompanied with a complete, open-source pipeline for document selection from web archives, text extraction from HTML, language identification for noisy texts, exact and near-deduplication, annotation with, among others, register labels, text quality estimates, and personally identifiable information; and final selection and filtering. We report on data quality probes through contrastive and analytical statistics, through manual inspection of samples for 24 languages, and through end-to-end evaluation of various language model architectures trained on this data. For multilingual LLM evaluation, we provide a comprehensive collection of benchmarks for nine European languages, with special emphasis on natively created tasks, mechanisms to mitigate prompt sensitivity, and refined normalization and aggregation of scores. Additionally, we train and evaluate a fami