Biology subjects

Frank, S. A.

Publications and source records attributed to Frank, S. A..

15 recordsLinked to original sources

bioRxiv2022

Precise traits from sloppy components. I. Perception and the origin of phenotypic response

Organisms perceive their environment and respond. The origin of perception-response traits presents a puzzle. Perception provides no value without response. Response requires perception. Recent advances in machine learning may provide a solution. A randomly connected network creates a reservoir of perceptive information about the recent history of environmental states. In each time step, a relatively small number of inputs drives the dynamics of the relatively large network. Over time, the internal network states retain memory of past inputs. To achieve a functional response to past states or to predict future states, a system must learn only how to match states of the reservoir to the target response. In the same way, a random biochemical or neural network of an organism can provide an initial perceptive basis. With a solution for one side of the two-step perception-response challenge, evolving an adaptive response may not be so difficult. Two broader themes emerge. First, organisms may often achieve precise traits from sloppy components. Second, evolutionary puzzles often follow the same outlines as the challenges of machine learning. In each case, the basic problem is how to learn, either by artificial computational methods or by natural selection.

evolutionary biology↗

bioRxiv2022

An enhanced transcription factor repressilator that buffers stochasticity and entrains to an erratic external circadian signal

How do cellular regulatory networks solve the challenges of life? This article presents computer software to study that question, focusing on how transcription factor networks transform internal and external inputs into cellular response outputs. The example challenge concerns maintaining a circadian rhythm of molecular concentrations. The system must buffer intrinsic stochastic fluctuations in molecular concentrations and entrain to an external circadian signal that appears and disappears randomly. The software optimizes a stochastic differential equation of transcription factor protein dynamics and the associated mRNAs that produce those transcription factors. The cellular network takes as inputs the concentrations of the transcription factors and produces as outputs the transcription rates of the mRNAs that make the transcription factors. An artificial neural network encodes the cellular input-output function, allowing efficient search for solutions to the complex stochastic challenge. Several good solutions are discovered, measured by the probability distribution for the tracking deviation between the stochastic cellular circadian trajectory and the deterministic external circadian pattern. The solutions differ significantly from each other, showing that overparameterized cellular networks may solve a given challenge in a variety of ways. The computation method provides a major advance in its ability to find transcription factor network dynamics than can solve environmental challenges. The article concludes by drawing an analogy between overparameterized cellular networks and the dense and deeply connected overparameterized artificial neural networks that have succeeded so well in deep learning. Understanding how overparameterized networks solve challenges may provide insight into the evolutionary design of cellular regulation.

systems biology↗

bioRxiv2022

Numbers of mutations within multicellular bodies: why it matters

Multicellular organisms often start life as a single cell. Subsequent cell division builds the body. Each mutational event during those developmental cell divisions carries forward to all descendant cells. The overall number of mutant cells in the body follows the Luria-Delbruck process. This article first reviews the basic quantitative principles by which one can understand the likely number of mutant cells and the variation in mutational burden between individuals. A new Frechet distribution approximation greatly simplifies calculation of likelihoods and intuitive understanding of process. The second part of the article highlights consequences of somatic mutational mosaicism for understanding diseases such as cancer, neurodegeneration, and atherosclerosis.

evolutionary biology↗

bioRxiv2022

Robustness increases heritability: implications for familial disease

Robustness protects organisms in two ways. Homeostatic buffering lowers the variation of traits caused by internal or external perturbations. Tolerance reduces the consequences of bad situations, such as extreme phenotypes or infections. This article shows that both types of robustness increase the heritability of protected traits. Additionally, robustness strongly increases the heritability of disease. Perhaps the natural tendency for organisms to protect robustly against perturbations partly explains the high heritability that occurs for some diseases.

evolutionary biology↗

bioRxiv2022

Optimization of transcription factor genetic circuits

Transcription factors (TFs) affect the expression of mRNAs. In essence, the TFs form a large computation network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day-night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance, and in how large TF networks may acquire a tendency for genetic variation and disease.

systems biology↗

bioRxiv2022

Penetratin inhibits α-synuclein fibrillation and improves locomotor functions in mice model of Parkinsons disease

Parkinsons disease (PD) is the second most common neurodegenerative disease. The presence of lewy bodies, primarily consisting of -synuclein (-syn) aggregates is one of the common features seen in the substantia nigra region of the brain in PD patients. The disease remains incurable and only symptomatic relief is available. We screened various cell-penetrating peptides and reveal that penetratin is a potent inhibitor of -syn aggregation in-vitro, and significantly improved locomotor coordination in mice models of PD in-vivo. The peptide inhibits -syn aggregation in vitro as well as in yeast, and C.elegans models. We further made a cyclic derivative of penetratin by disulfide coupling of N- and C-terminal cysteine residues. Both penetratin and its cyclized derivative interact with -syn. NMR studies show that both linear as well as cyclic derivative interact at the acidic C-terminal tail of the protein. Similar to penetratin, its cyclic derivative inhibited -syn aggregation in the C.elegans model of Parkinsons disease, and also improved worm motility. Molecular Dynamics studies show that penetratin interacts with -synuclein and prevents its conformational transition from disordered into {beta}-sheet rich structure. The therapeutic efficacy of penetratin was further confirmed in a transgenic mice model of the disease, wherein penetratin treatment over a period of 90 days improved locomotor coordination, and halted disease progression. Overall, the present work provides a potent therapeutic agent that could be further explored in the management of PD.

neuroscience↗

bioRxiv2020

The generalized Price equation: forces that change population statistics

The Price equation partitions the change in the expected value of a population measure. The first component describes the partial change caused by altered frequencies. The second component describes the partial change caused by altered measurements. In biology, frequency changes often associate with the direct effect of natural selection. Measure changes reflect processes during transmission that alter trait values. More broadly, the two components describe the direct forces that change population composition and the altered frame of reference that changes measured values. The classic Price equation is limited to population statistics that can expressed as the expected value of a measure. Many statistics cannot be expressed as expected values, such as the harmonic mean and the family of rescaled diversity measures. We generalize the Price equation to any population statistic that can be expressed as a function of frequencies and measurements. We obtain the generalized partition between the direct forces that cause frequency change and the altered frame of reference that changes measurements.

evolutionary biology↗

bioRxiv2020

Quantitating denaturation by formic acid: Imperfect repeats are essential to the stability of the functional amyloid protein FapC

Bacterial functional amyloids are evolutionarily optimized to aggregate to help them fulfil their biological functions, e.g. to provide mechanical stability to biofilm. Amyloid is formed in Pseudomonas sp. by the protein FapC which contains 3 imperfect repeats connected by long linkers. Stepwise removal of these repeats slows down aggregation and increases the propensity of amyloids to fragment during the fibrillation process, but how these mechanistic properties link to fibril stability is unclear. Here we address this question. The extreme robustness of functional amyloid makes them resistant to conventional chemical denaturants, but they dissolve in formic acid (FA) at high concentrations. To quantify this, we first measured the denaturing potency of FA using 3 small acid-resistant proteins (S6, lysozyme and ubiquitin). This revealed a linear relationship between [FA] and the free energy of unfolding with a slope of mFA, as well as a robust correlation between protein residue size and mFA. We then measured the solubilisation of fibrils formed from different FapC variants (with varying number of repeats) as a function of [FA]. The resulting mFA values revealed a decline in the number of residues driving amyloid formation when at least 2 repeats were deleted. The midpoint of denaturation declined monotonically with progressive removal of repeats and correlated with solubility in SDS. Complete removal of all repeats led to fibrils which were solubilized at FA concentrations 2-3 orders of magnitude lower than the repeat-containing variants, showing that at least one imperfect repeat is required for the stability of functional amyloid.

biophysics↗

bioRxiv2019

Sexual antagonism leads to a mosaic of X-autosome conflict

Males and females have different optimal values for some traits, such as body size. When the same genes control these traits in both sexes, selection pushes in opposite directions in males and females. Alleles at autosomal loci spend equal amounts of time in males and females, suggesting that the sexually antagonistic selective forces may approximately balance between the opposing optima. Frank and Crespi noted that alleles on the X chromosome spend twice as much time in diploid females as in haploid males. That distinction between the sexes may tend to favor X-linked genes that push more strongly toward the female optimum than the male optimum. The female bias of X-linked genes conflicts with the intermediate optimum of autosomal genes, potentially creating an X-autosome conflict. Patten has recently argued that explicit genetic assumptions about dominance and the relative magnitude of allelic effects may lead X-linked genes to favor the male rather than the female optimum, contradicting Frank and Crespi. This article combines the insights of those prior analyses into a new, more general theory. We find some parameter combinations for X-linked loci that favor a female bias and other parameter combinations that favor a male bias. We conclude that the X likely contains a mosaic pattern of loci that conflict with autosomes over sexually antagonistic traits. The overall tendency for a female or male bias on the X depends on prior assumptions about the distribution of key parameters across X-linked loci. Those parameters include the dominance coefficient and the way in which ploidy influences the magnitude of allelic effects.

evolutionary biology↗

bioRxiv2019

Invariance in ecological pattern

The abundance of different species in a community often follows the log series distribution. Other ecological patterns also have simple forms. Why does the complexity and variability of ecological systems reduce to such simplicity? Common answers include maximum entropy, neutrality, and convergent outcome from different underlying biological processes. This article proposes a more general answer based on the concept of invariance, the property by which a pattern remains the same after transformation. Invariance has a long tradition in physics. For example, general relativity emphasizes the need for the equations describing the laws of physics to have the same form in all frames of reference. By bringing this unifying invariance approach into ecology, we show that the log series pattern dominates when the consequences of processes acting on abundance are invariant to the addition or multiplication of abundance by a constant. The lognormal pattern dominates when the processes acting on net species growth rate obey rotational invariance (symmetry) with respect to the summing up of the individual component processes. Recognizing how these invariances connect pattern to process leads to a synthesis of previous approaches. First, invariance provides a simpler and more fundamental maximum entropy derivation of the log series distribution. Second, invariance provides a simple derivation of the key result from neutral theory: the log series at the metacommunity scale and a clearer form of the skewed lognormal at the local community scale. The invariance expressions are easy to understand because they uniquely describe the basic underlying components that shape pattern.

ecology↗

bioRxiv2018

Evolutionary design of regulatory control. II. Robust error-correcting feedback increases genetic and phenotypic variability

As systems become more robust against perturbations, they can compensate for greater sloppiness in the performance of their components. That robust compensation reduces the force of natural selection on the systems components, leading to component decay. The paradoxical coupling of robustness and decay predicts that robust systems evolve cheaper, lower performing components, which accumulate greater mutational genetic variability and which have greater phenotypic stochasticity in trait expression. Previous work noted the paradox of robustness. However, no general theory for the evolutionary dynamics of system robustness and component decay has been developed. This article takes a first step by linking engineering control theory with the genetic theory of evolutionary dynamics. Control theory emphasizes error-correcting feedback as the single greatest principle in robust system design. Linking control theory to evolution leads to a theory for the evolutionary dynamics of error-correcting feedback, a unifying approach for the evolutionary analysis of robust systems. In this article, I study how, in theory, increasingly robust systems accumulate more genetic variability and greater stochasticity of expression in their components. The theory predicts different levels of variability between different regulatory control architectures and different levels of variability between different components within a particular regulatory control system. Those predictions provide a way to understand the accumulating data on genetic variability and single-cell stochasticity of gene expression. I also show that increasing robustness reduces the frequency of system failures associated with disease and, simultaneously, causes a strong increase in the heritability of disease. Thus, robust error correction in biological regulatory control may partly explain the puzzlingly high heritability of disease and, more generally, the surprisingly high heritability of fitness.

systems biology↗

bioRxiv2018

Measurement invariance explains the universal law of generalization for psychological perception

The universal law of generalization describes how animals discriminate between alternative sensory stimuli. On an appropriate perceptual scale, the probability that an organism perceives two stimuli as similar typically declines exponentially with the difference on the perceptual scale. Exceptions often follow a Gaussian probability pattern rather than an exponential pattern. Previous explanations have been based on underlying theoretical frameworks such as information theory, Kolmogorov complexity, or empirical multidimensional scaling. This article shows that the few inevitable invariances that must apply to any reasonable perceptual scale provide a sufficient explanation for the universal exponential law of generalization. In particular, reasonable measurement scales of perception must be invariant to shift by a constant value, which by itself leads to the exponential form. Similarly, reasonable measurement scales of perception must be invariant to multiplication, or stretch, by a constant value, which leads to the conservation of the slope of discrimination with perceptual difference. In some cases, an additional assumption about exchangeability or rotation of underlying perceptual dimensions leads to a Gaussian pattern of discrimination, which can be understood as a special case of the more general exponential form. The three measurement invariances of shift, stretch, and rotation provide a sufficient explanation for the universally observed patterns of perceptual generalization. All of the additional assumptions and language associated with information, complexity, and empirical scaling are superfluous with regard to the broad patterns of perception.

animal behavior and cognition↗

bioRxiv2018

Homeostasis, environmental tracking and phenotypic plasticity. I. A robust control theory approach to evolutionary design tradeoffs

The evolutionary design of regulatory control balances various tradeoffs in performance. Fast reaction to environmental change tends to favor plastic responsiveness at the expense of greater sensitivity to perturbations that degrade homeostatic control. Greater homeostatic stability against unpredictable disturbances tends to reduce performance in tracking environmental change. This article applies the classic principles of engineering control theory to the evolutionary design of regulatory systems. The engineering theory clarifies the conceptual aspects of evolutionary tradeoffs and provides analytic methods for developing specific predictions. On the conceptual side, this article clarifies the meanings of integral control, feedback, and design, concepts that have been discussed in a confusing way within the biological literature. On the analytic side, this article presents extensive methods and examples to study error-correcting feedback, which is perhaps the single greatest principle of design in both human-engineered and naturally designed systems. The broad framework and associated software code provide a comprehensive how-to guide for making models that focus on functional aspects of regulatory control and for making comparative predictions about regulatory design in response to various kinds of environmental challenge. The second article in this series analyzes how alternative regulatory designs influence the relative levels of genetic variability, stochasticity of trait expression, and heritability of disease.

evolutionary biology↗

bioRxiv2017

Receptor Uptake Arrays For Vitamin B12, Siderophores And Glycans Shape Bacterial Communities

Molecular variants of vitamin B12, siderophores and glycans occur. To take up variant forms, bacteria may express an array of receptors. The gut microbe Bacteroides thetaiotaomicron has three different receptors to take up variants of vitamin B12 and 88 receptors to take up various glycans. The design of receptor arrays reflects key processes that shape cellular evolution. Competition may focus each species on a subset of the available nutrient diversity. Some gut bacteria can take up only a narrow range of carbohydrates, whereas species such as B. thetaiotaomicron can digest many different complex glycans. Comparison of different nutrients, habitats, and genomes provide opportunity to test hypotheses about the breadth of receptor arrays. Another important process concerns fluctuations in nutrient availability. Such fluctuations enhance the value of cellular sensors, which gain information about environmental availability and adjust receptor deployment. Bacteria often adjust receptor expression in response to fluctuations of particular carbohydrate food sources. Some species may adjust expression of uptake receptors for specific siderophores. How do cells use sensor information to control the response to fluctuations? That question about regulatory wiring relates to problems that arise in control theory and artificial intelligence. Control theory clarifies how to analyze environmental fluctuations in relation to the design of sensors and response systems. Recent advances in deep learning studies of artificial intelligence focus on the architecture of regulatory wiring and the ways in which complex control networks represent and classify environmental states. I emphasize the similar design problems that arise in cellular evolution, control theory, and artificial intelligence. I connect those broad conceptual aspects to many testable hypotheses for bacterial uptake of vitamin B12, siderophores and glycans.

microbiology↗

bioRxiv2016

Universal expressions of population change by the Price equation: natural selection, information, and maximum entropy production

The Price equation shows the unity between the fundamental expressions of change in biology, in information and entropy descriptions of populations, and in aspects of thermodynamics. The Price equation partitions the change in the average value of a metric between two populations. A population may be composed of organisms or particles or any members of a set to which we can assign probabilities. A metric may be biological fitness or physical energy or the output of an arbitrarily complicated function that assigns quantitative values to members of the population. The first part of the Price equation describes how directly applied forces change the probabilities assigned to members of the population when holding constant the metrical values of the members--a fixed metrical frame of reference. The second part describes how the metrical values change, altering the metrical frame of reference. In canonical examples, the direct forces balance the changing metrical frame of reference, leaving the average or total metrical values unchanged. In biology, relative reproductive success (fitness) remains invariant as a simple consequence of the conservation of total probability. In physics, systems often conserve total energy. Nonconservative metrics can be described by starting with conserved metrics, and then studying how coordinate transformations between conserved and nonconserved metrics alter the geometry of the dynamics and the aggregate values of populations. From this abstract perspective, key results from different subjects appear more simply as universal geometric principles for the dynamics of populations subject to the constraints of particular conserved quantitiesa.

evolutionary biology↗