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arXiv2023

Applications of machine Learning to improve the efficiency and range of microbial biosynthesis: a review of state-of-art techniques

In the modern world, technology is at its peak. Different avenues in programming and technology have been explored for data analysis, automation, and robotics. Machine learning is key to optimize data analysis, make accurate predictions, and hasten/improve existing functions. Thus, presently, the field of machine learning in artificial intelligence is being developed and its uses in varying fields are being explored. One field in which its uses stand out is that of microbial biosynthesis. In this paper, a comprehensive overview of the differing machine learning programs used in biosynthesis is provided, alongside brief descriptions of the fields of machine learning and microbial biosynthesis separately. This information includes past trends, modern developments, future improvements, explanations of processes, and current problems they face. Thus, this paper's main contribution is to distill developments in, and provide a holistic explanation of, 2 key fields and their applicability to improve industry/research. It also highlights challenges and research directions, acting to instigate more research and development in the growing fields. Finally, the paper aims to act as a reference for academics performing research, industry professionals improving their processes, and students looking to understand the concept of machine learning in biosynthesis.

q-bio.SC↗
arXiv2023

Non-equilibrium cluster-cluster aggregation in the presence of anchoring sites

Non-equilibrium cluster-cluster aggregation of particles diffusing in or at the cell membrane has been hypothesized to lead to domains of finite size in different biological contexts such as lipid rafts, cell adhesion complexes, or postsynaptic domains in neurons. In this scenario, the desorption of particles balances a continuous flux to the membrane, imposing a cut-off on possible aggregate sizes and giving rise to a stationary size distribution. Here, we investigate the case of non-equilibrium cluster-cluster aggregation in two dimensions where diffusing particles and/or clusters remain fixed in space at specific anchoring sites, which should be particularly relevant for synapses but may also be present in other biological or physical systems. Using an effective mean-field description of the concentration field around anchored clusters, we derive an expression for their average size as a function of parameters such as the anchoring site density. We furthermore propose and solve appropriate rate equations that allow us to predict the size distributions of both diffusing and fixed clusters. We confirm our results with particle-based simulations, and discuss potential implications for biological and physical systems.

cond-mat.soft↗
arXiv2023

Learning dynamical models of single and collective cell migration: a review

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualizing the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasize how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour.

physics.bio-ph↗
arXiv2023

Discrete-to-continuum models of pre-stressed cytoskeletal filament networks

We introduce a mathematical model for the mechanical behaviour of the eukaryotic cell cytoskeleton. This discrete model involves a regular array of pre-stressed protein filaments that exhibit resistance to enthalpic stretching, joined at crosslinks to form a network. Assuming that the inter-crosslink distance is much shorter than the lengthscale of the cell, we upscale the discrete force balance to form a continuum system of governing equations and deduce the corresponding macroscopic stress tensor. We use these discrete and continuum models to analyse the imposed displacement of a bead placed in the domain, characterising the cell rheology through the force-displacement curve. We further derive an analytical approximation to the stress and strain fields in the limit of small bead radius, predicting the net force required to generate a given deformation and elucidating the dependency on the microscale properties of the filaments. We apply these models to networks of the intermediate filament vimentin and demonstrate good agreement between predictions of the discrete, continuum and analytical approaches. In particular, our model predicts that the network stiffness increases sublinearly with the filament pre-stress and scales logarithmically with the bead size.

q-bio.QM↗
arXiv2024

Extreme first passage times for populations of identical rare events

A collection of identical and independent rare event first passage times is considered. The problem of finding the fastest out of $N$ such events to occur is called an extreme first passage time. The rare event times are singular and limit to infinity as a positive parameter scaling the noise magnitude is reduced to zero. In contrast, previous work has shown that the mean of the fastest event time goes to zero in the limit of an infinite number of walkers. The combined limit is studied. In particular, the mean time and the most likely path taken by the fastest random walker are investigated. Using techniques from large deviation theory, it is shown that there is a distinguished limit where the mean time for the fastest walker can take any positive value, depending on a single proportionality constant. Furthermore, it is shown that the mean time and most likely path can be approximated using the solution to a variational problem related to the single-walker rare event.

physics.bio-ph↗
arXiv2023

Limits of economy and fidelity for programmable assembly of size-controlled triply-periodic polyhedra

We propose and investigate an extension of the Caspar-Klug symmetry principles for viral capsid assembly to the programmable assembly of size-controlled triply-periodic polyhedra, discrete variants of the Primitive, Diamond, and Gyroid cubic minimal surfaces. Inspired by a recent class of programmable DNA origami colloids, we demonstrate that the economy of design in these crystalline assemblies -- in terms of the growth of the number of distinct particle species required with the increased size-scale (e.g. periodicity) -- is comparable to viral shells. We further test the role of geometric specificity in these assemblies via dynamical assembly simulations, which show that conditions for simultaneously efficient and high-fidelity assembly require an intermediate degree of flexibility of local angles and lengths in programmed assembly. Off-target misassembly occurs via incorporation of a variant of disclination defects, generalized to the case of hyperbolic crystals. The possibility of these topological defects is a direct consequence of the very same symmetry principles that underlie the economical design, exposing a basic tradeoff between design economy and fidelity of programmable, size controlled assembly.

cond-mat.soft↗
arXiv2024

Lévy distributed fluctuations in the living cell cortex

The actomyosin cortex is an active material that provides animal cells with a strong but flexible exterior, whose mechanics, including non-Gaussian fluctuations and occasional large displacements or cytoquakes, have defied explanation. We study the active fluctuations of the cortex using nanoscale tracking of arrays of flexible microposts adhered to multiple cultured cell types. When the confounding effects of static heterogeneity and tracking error are removed, the fluctuations are found to be heavy-tailed and well-described by a truncated Lévy alpha-stable distribution over a wide range of timescales, in multiple cell types. The largest random displacements closely resemble the earlier-reported cytoquakes, but notably, we find these cytoquakes are not due to earthquake-like cooperative rearrangement of many cytoskeletal elements. Rather, they are indistinguishable from chance large excursions of a super-diffusive random process driven by heavy-tailed noise. The non-cooperative microscopic events driving these fluctuations need not be larger than the expected elastic energy of single tensed cortical actin filaments, and the implied distribution of microscopic event energies will need to be accounted for by future models of the cytoskeleton.

physics.bio-ph↗
arXiv2023

Regulation of store-operated calcium entry

Plasma membrane calcium influx through ion channels is crucial for many events in cellular physiology. Cell surface stimuli lead to the production of inositol 1,4,5-trisphosphate (IP3), which binds to IP3 receptors in the endoplasmic reticulum (ER) to release calcium pools from the ER lumen. This leads to depletion of ER calcium pools which has been termed store-depletion. Store-depletion leads the dissociation of calcium ions from the EF-hand motif of the ER calcium sensor Stromal Interaction Molecule 1 (STIM1). This leads to a conformational change in STIM1 which helps it to interact with a plasma membrane (PM) at ER:PM junctions. At these ER:PM junctions, STIM1 binds to and activates a calcium channel known as Orai1 to form calcium-release activated calcium (CRAC) channels. Activation of Orai1 leads to calcium influx, known as store-operated calcium entry (SOCE). In addition to Orai1 and STIM1, the homologs of Orai1 and STIM1, such as Orai2/3 and STIM2 also play a crucial role in calcium homeostasis. The influx of calcium through the Orai channel activates a calcium current that has been termed CRAC currents. CRAC channels form multimers and cluster together in large macromolecular assemblies termed puncta. How these CRAC channels form puncta has been contentious since their discovery. In this review, we will outline the history of SOCE, the molecular players involved in this process (Orai and STIM proteins, TRP channels, SOCE-associated regulatory factor etc.), as well as the models that have been proposed to explain this important mechanism in cellular physiology.

q-bio.CB↗
arXiv2023

Newcomb-Benford Law in Neuromuscular Transmission: Validation at Hyperkalemic Conditions

Recently, we demonstrated the validity of anomalous numbers law, known as Newcomb-Benford's law, at the mammalian neuromuscular transmission, considering different extracellular calcium concentrations arXiv:2002.01986. The present work continues to examine how alterations in extracellular solution modulate the first digit law in the context of the spontaneous release of acetylcholine from the neuromuscular junction. We investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. The analysis showed that the interval between the miniature potentials at high potassium concentrations follows Newcomb-Benford's law. Also, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.

physics.bio-ph↗
arXiv2024

Minimal Mechanisms of Microtubule Length Regulation in Living Cells

The microtubule cytoskeleton is responsible for sustained, long-range intracellular transport of mRNAs, proteins, and organelles in neurons. Neuronal microtubules must be stable enough to ensure reliable transport, but they also undergo dynamic instability, as their plus and minus ends continuously switch between growth and shrinking. This process allows for continuous rebuilding of the cytoskeleton and for flexibility in injury settings. Motivated by \textit{in vivo} experimental data on microtubule behavior in \textit{Drosophila} neurons, we propose a mathematical model of dendritic microtubule dynamics, with a focus on understanding microtubule length, velocity, and state-duration distributions. We find that limitations on microtubule growth phases are needed for realistic dynamics, but the type of limiting mechanism leads to qualitatively different responses to plausible experimental perturbations. We therefore propose and investigate two minimally-complex length-limiting factors: limitation due to resource (tubulin) constraints and limitation due to catastrophe of large-length microtubules. We combine simulations of a detailed stochastic model with steady-state analysis of a mean-field ordinary differential equations model to map out qualitatively distinct parameter regimes. This provides a basis for predicting changes in microtubule dynamics, tubulin allocation, and the turnover rate of tubulin within microtubules in different experimental environments.

math.DS↗
arXiv2023

Form, function, mind: what doesn't compute (and what might)

The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, dynamicism is similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life.

q-bio.NC↗
arXiv2023

A Colour-Encoded Nanometric Ruler for Axial Super-Resolution Microscopies

Recent progress has boosted the resolving power of optical microscopies to spatial dimensions well below the diffraction limit. Yet, axial super-resolution and axial single-molecule localisation typically require more complicated implementations than their lateral counterparts. In the present work, we propose a simple solution for axial metrology by providing a multi-layered single-excitation, dual-emission test slide, in which axial distance is colour-encoded. Our test slide combines on a standard microscope coverslip substrate two flat, thin, uniform and brightly emitting fluorophore layers, separated by a nanometric transparent spacer layer having a refractive index close to a biological cell. The ensemble is sealed in an index-matched protective polymer. As a proof-of-principle, we estimate the light confinement resulting from evanescent-wave excitation in total internal reflection fluorescence (TIRF) microscopy. Our test sample permits, even for the non-expert user, a facile axial metrology at the sub-100-nm scale, a critical requirement for axial super-resolution, as well as near-surface imaging, spectroscopy and sensing.

physics.optics↗
arXiv2023

Gene expression in growing cells: A biophysical primer

Cell growth and gene expression, essential elements of all living systems, have long been the focus of biophysical interrogation. Advances in single-cell methods have invigorated theoretical studies into these processes. However, until recently, there was little dialog between the two areas of study. Most theoretical models for gene regulation assumed gene activity to be oblivious to the progression of the cell cycle between birth and division. But there are numerous ways in which the periodic character of all cellular observables can modulate gene expression. The molecular factors required for transcription and translation increase in number during the cell cycle, but are also diluted due to the continuous increase in cell volume. The replication of the genome changes the dosage of those same cellular players but also provides competing targets for regulatory binding. Finally, cell division reduces their number again, and so forth. Stochasticity is inherent to all these biological processes, manifested in fluctuations in the synthesis and degradation of new cellular components as well as the random partitioning of molecules at each cell division. The notion of gene expression as stationary is thus hard to justify. In this review, we survey the emerging paradigm of cell-cycle regulated gene expression, with an emphasis on the global expression patterns rather than gene-specific regulation. We discuss recent experimental reports where cell growth and gene expression were simultaneously measured in individual cells, providing first glimpses into the coupling between the two. While the experimental findings, not surprisingly, differ among genes and organisms, several theoretical models have emerged that attempt to reconcile these differences and form a unifying framework for understanding gene expression in growing cells.

q-bio.SC↗
arXiv2024

Reshaping and Enzymatic Activity may allow Viruses to move through the Mucus

Filamentous viruses like influenza and torovirus often display systematic bends and arcs of mysterious physical origin. We propose that such viruses undergo an instability from a cylindrically symmetric to a toroidally curved state. This ``toro-elastic'' state emerges via spontaneous symmetry breaking under prestress due to short range spike protein interactions magnified by %the filament's surface topography. Once surface stresses are sufficiently large, the filament buckles and the curved state constitutes a soft mode that can potentially propagate through the filament's material frame around a mexican-hat-type potential. In the mucus of our airways, which constitutes a soft, porous 3D network, glycan chains are omnipresent and influenza's spike proteins are known to efficiently bind and cut them. We next show that such a non-equilibrium enzymatic reaction can induce spontaneous rotation of the curved state, leading to a whole body reshaping propulsion similar to -- but different from -- eukaryotic flagella and spirochetes.

cond-mat.soft↗
arXiv2024

A simulation method for the wetting dynamics of liquid droplets on deformable membranes

Biological cells utilize membranes and liquid-like droplets, known as biomolecular condensates, to structure their interior. The interaction of droplets and membranes, despite being involved in several key biological processes, is so far little understood. Here, we present a first numerical method to simulate the continuum dynamics of droplets interacting with deformable membranes via wetting. The method combines the advantages of the phase-field method for multi-phase flow simulation and the arbitrary Lagrangian-Eulerian (ALE) method for an explicit description of the elastic surface. The model is thermodynamically consistent, coupling bulk hydrodynamics with capillary forces, as well as bending, tension, and stretching of a thin membrane. The method is validated by comparing simulations for single droplets to theoretical results of shape equations, and its capabilities are illustrated in 2D and 3D axisymmetric scenarios.

cond-mat.soft↗
arXiv2024

Target search on DNA -- effect of coiling

Some proteins can find their targets on DNA faster than by pure diffusion in the three-dimensional cytoplasm, through the process of facilitated diffusion: They can loosely bind to DNA and temporarily slide along it, thus being guided by the DNA molecule itself to the target. This chapter examines this process in mathematical detail with a focus on including the effect of DNA coiling on the search process.

q-bio.BM↗
arXiv2023

mRNA translation from a unidirectional traffic perspective

mRNA translation is a crucial process that leads to protein synthesis in living cells. Therefore, it is a process that needs to work optimally for a cell to stay healthy and alive. With advancements in microscopy and novel experimental techniques, a lot of the intricate details about the translation mechanism are now known. However, the why and how of this mechanism are still ill understood, and therefore, is an active area of research. Theoretical studies of mRNA translation typically view it in terms of the Totally Asymmetric Simple Exclusion Process or TASEP. Various works have used the TASEP model in order to study a wide range of phenomena and factors affecting translation, such as ribosome traffic on an mRNA under noisy (codon-dependent or otherwise) conditions, ribosome stalling, premature termination, ribosome reinitiation and dropoff, codon-dependent elongation and competition among mRNA for ribosomes, among others. In this review, we relay the history and physics of the translation process in terms of the TASEP framework. In particular, we discuss the viability and evolution of this model and its limitations while also formulating the reasons behind its success. Finally, we also identify gaps in the existing literature and suggest possible extensions and applications that will lead to a better understanding of ribosome traffic on the mRNA.

q-bio.SC↗
arXiv2024

Beyond Michaelis-Menten: Allosteric Rate Control of Chaos

The method developed by Michaelis and Menten was foundational in the development of our understanding of biochemical reaction kinetics. Extended models of metabolism encapsulated by reaction rate theory, stochastic reaction models, and dynamic flux estimation, amongst others, address aspects of this fundamental idea. The limitations of these approaches are well understood, and efforts to overcome those issues so far have been plentiful but with limited success. The known issues can be summarised as the sole dependent relation with substrate concentration, the encapsulation of rate in a single relevant scalar, and the subsequent lack of functional control that results from this assumption. The Rate Control of Chaos (RCC) is a nonlinear control method that has been shown to be effective in controlling the dynamic state of biological oscillators based on the concept of rate limitation of the exponential growth in chaotic systems. Extending RCC with allosteric properties allows robust control of the enzymatic process, and replicates the Michaelis-Menten kinetics. The emergent dynamics is robust to perturbations and noise but susceptible to regulatory adjustments. This control method adapts the control parameters dynamically in the presence of a ligand, and permits introduction of energy relations into the control function. The dynamic nature of the control eliminates the steady-state requirements and allows the modelling of large-scale dynamic behaviour, potentially addressing issues in metabolic disorder and failure of metabolic control.

q-bio.MN↗