Search for: All records

Abstract A biological circuit is a neural or biochemical cascade, taking inputs and producing outputs. How have biological circuits learned to solve environmental challenges over the history of life? The answer certainly follows Dobzhansky’s famous quote that “nothing in biology makes sense except in the light of evolution.” But that quote leaves out the mechanistic basis by which natural selection’s trial-and-error learning happens, which is exactly what we have to understand. How does the learning process that designs biological circuits actually work? How much insight can we gain about the form and function of biological circuits by studying the processes that have made those circuits? Because life’s circuits must often solve the same problems as those faced by machine learning, such as environmental tracking, homeostatic control, dimensional reduction, or classification, we can begin by considering how machine learning designs computational circuits to solve problems. We can then ask: How much insight do those computational circuits provide about the design of biological circuits? How much does biology differ from computers in the particular circuit designs that it uses to solve problems? This article steps through two classic machine learning models to set the foundation for analyzing broad questions about the design of biological circuits. One insight is the surprising power of randomly connected networks. Another is the central role of internal models of the environment embedded within biological circuits, illustrated by a model of dimensional reduction and trend prediction. Overall, many challenges in biology have machine learning analogs, suggesting hypotheses about how biology’s circuits are designed. 

Abstract Natural selection acts on traits at different scales, often with opposing consequences. This article identifies the particular forces that act at each scale and how those forces combine to determine the overall evolutionary outcome. A series of extended models derive from the tragedy of the commons, illustrating opposing forces at different scales. Examples include the primary tension between conflict and cooperation and the evolution of virulence, sex ratios, dispersal, and evolvability. The unified analysis subsumes interactions within and between species by generalizing multitrait interactions. Expanded notions of recombination and cotransmission arise. The core theoretical approach isolates the fundamental forces of selection, including marginal valuation, correlation between interacting entities, and reproductive value. Those fundamental forces act as partial causes that combine at different temporal and spatial scales. Modeling focuses on statics, in the sense of how different forces at various scales tend to oppose each other, ultimately combining to shape traits. That type of static analysis emphasizes explanation rather than the calculation of dynamics. The article builds on the duality between explanation versus calculation in terms of statics versus dynamics. The literature often poses that duality as a controversy, whereas this article develops the pair as complementary tools that together provide deeper understanding. Along the way, the unified approach clarifies the subtle distinctions between kin selection, multilevel selection, and inclusive fitness, subsuming these topics into the broader perspectives of fundamental forces and multiple scales. 

Abstract Organisms gain by anticipating future changes in the environment. Those environmental changes often follow stochastic trends. The steeper the slope of the trend, the more likely the trend’s momentum carries the future trend in the same direction. This article presents a simple biological circuit that measures the momentum, providing a prediction about future trend. The circuit calculates the momentum by the difference between a short-term and a long-term exponential moving average. The time lengths of the two moving averages can be adjusted by changing the decay rates of state variables. Different time lengths for those averages trade off between errors caused by noise and errors caused by lags in predicting a change in the direction of the trend. Prior studies have emphasized circuits that make similar calculations about trends. However, those prior studies embedded their analyses in the details of particular applications, obscuring the simple generality and wide applicability of the approach. The model here contributes to the topic by clarifying the great simplicity and generality of anticipation for stochastic trends. This article also notes that, in financial analysis, the difference between moving averages is widely used to predict future trends in asset prices. The financial measure is called the moving average convergence–divergence indicator. Connecting the biological problem to financial analysis opens the way for future studies in biology to exploit the variety of highly developed trend models in finance. 

Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure despite their apparent differences. Here, I show that a simple notational partitioning of change by the Price equation reveals a universal force–metric–bias (FMB) law: Δθ=Mf+b+ξ. The force f drives improvement in parameters, Δθ, in proportion to the slope of performance with respect to the parameters. The metric M rescales movement by inverse curvature. The bias b adds momentum or changes in the frame of reference. The noise ξ enables exploration. This framework unifies natural selection, Bayesian updating, Newton’s method, stochastic gradient descent, stochastic Langevin dynamics, Adam optimization, and most other algorithms as special cases of the same underlying process. The Price equation also reveals why Fisher information, Kullback–Leibler divergence, and d’Alembert’s principle arise naturally in learning dynamics. By exposing this common structure, the FMB law provides a principled foundation for understanding, comparing, and designing learning algorithms across disciplines. 

Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limited attention in analyses of cellular and physiological circuits. This study builds on machine learning techniques—such as dimensionality reduction, boosted decision trees, and anomaly classification—to develop a conceptual framework for biological circuits. One problem is that machine learning circuits tend to be unrealistically large for use by cellular and physiological systems. I therefore focus on minimal circuits inspired by machine learning concepts, reduced to the cellular scale. Through illustrative models, I demonstrate that small circuits can provide useful classification of anomalies. The analysis also shows how principles from machine learning—such as temporal and atemporal anomaly detection, multivariate signal integration, and hierarchical decision-making cascades—can inform hypotheses about the design and evolution of cellular circuits. This interdisciplinary approach enhances our understanding of cellular circuits and highlights the universal nature of computational strategies across biological and artificial systems. 

Cancer is the origin of a novel tissue that attracts resources, spreads beyond boundaries, avoids normal controls, and escapes immunity. How does a novel tissue arise? The puzzle is that two seemingly different processes appear to be the primary driving force. On the one hand, overwhelming evidence links (epi)genetic driver mutations to the origin and progression of tumors. Common oncogenic mutations such asKRASaccelerate cell division, and common knockouts of tumor suppressors such asTP53abrogate cell death or checks on cell division. On the other hand, cancerous tissues create complex traits that require intricate changes in cells and multiple interactions between different cell types. Such novelty often arises by hijacking the developmental plasticity that normally creates the diverse cells and tissues of our bodies from a single original zygotic cell. How can we reconcile the simple genetic changes in carcinogenesis with the complex developmental plasticity that creates novel tissues? This perspective advocates a new model. (Epi)genetic mutations release developmental plasticity. That developmental plasticity creates novel cellular interactions and complex tissues. Initially, novel traits created by developmental plasticity may not be stably heritable, thus subsequent (epi)genetic changes must stabilize the phenotypic novelty. Recent studies show how classic oncogenic and tumor suppressor driver mutations, such asKRASandTP53, may primarily act in early carcinogenesis as broad releasers of developmental plasticity rather than as stimulators of cell division or knockout of limitations on cellular clonal expansion. In the new model, genetics releases, plasticity creates, and genetics stabilizes.