Biology subjects

Lin, W.-H.

Publications and source records attributed to Lin, W.-H..

21 records · Page 2Linked to original sources

bioRxiv2020

Origin of exponential growth in nonlinear reaction networks

Exponentially growing systems are prevalent in nature, spanning all scales from biochemical reaction networks in single cells to food webs of ecosystems. How exponential growth emerges in nonlinear systems is mathematically unclear. Here, we describe a general theoretical framework that reveals underlying principles of long-term growth: scalability of flux functions and ergodicity of the rescaled systems. Our theory shows that nonlinear fluxes can generate not only balanced growth, but also oscillatory or chaotic growth modalities, explaining non-equilibrium dynamics observed in cell cycles and ecosystems. Our mathematical framework is broadly useful in predicting long-term growth rates from natural and synthetic networks, analyzing the effects of system noise and perturbations, validating empirical and phenomenological laws on growth rate, and studying autocatalysis and network evolution. SignificanceNatural systems (e.g., cells, ecosystems) generally consist of reaction networks (e.g., metabolic networks, food webs) with nonlinear flux functions (e.g., Michaelis-Menten kinetics, density-dependent selection). Despite their complex nonlinearities, these systems often exhibit simple exponential growth in the long term. How exponential growth emerges from nonlinear networks remains elusive. Our work demonstrates mathematically how two principles, multivariate scalability of flux functions and ergodicity of the rescaled system, guarantee a well-defined rate of growth. By connecting ergodic theory, a powerful branch of mathematics, to the study of growth in biology, our theoretical framework can recapitulate various growth modalities (from balanced growth to periodic, quasi-periodic or even chaotic behaviors), greatly expanding the types of growing systems that can be studied.

systems biology↗

bioRxiv2019

Context-dependent probability estimation and its neurocomputational substrates

Many decisions rely on how we evaluate potential outcomes associated with the options under consideration and estimate their corresponding probabilities of occurrence. Outcome valuation is subjective as it requires consulting internal preferences and is sensitive to context. In contrast, probability estimation requires extracting statistics from the environment and therefore imposes unique challenges to the decision maker. Here we show that probability estimation, like outcome valuation, is subject to context effects that bias probability estimates away from other stimuli present in the same context. However, unlike valuation, these context effects appeared to be scaled by estimated uncertainty, which is largest at intermediate probabilities. BOLD imaging showed that patterns of multivoxel activity in dorsal anterior cingulate cortex (dACC) and ventromedial prefrontal cortex (VMPFC) predicted individual differences in context effects on probability estimate. These results establish VMPFC as the neurocomputational substrate shared between valuation and probability estimation and highlight the additional involvement of dACC that can be uniquely attributed to probability estimation. As probability estimation is a required component of computational accounts from sensory inference to higher cognition, the context effects found here may affect a wide array of cognitive computations.\n\nHighlightsO_LIContext impacts subjective estimates on reward probability - Stimuli carrying greater variance are more strongly affected by other stimuli present in the same context\nC_LIO_LIThis phenomenon can be explained by reference-dependent computations that are gated by reward variance\nC_LIO_LIMultivoxel patterns of dACC and VMPFC activity predicts individual differences in context effect on probability estimate\nC_LI

neuroscience↗

bioRxiv2018

Mef-2 and p300 interact to regulate expression of the homeostatic regulator Pumilio in Drosophila

Abstract (174 words)Pumilio (Pum) is a key component of neuron firing-rate homeostasis that maintains stability of neural circuit activity. Whilst Pum is ubiquitously expressed, we understand little about how synaptic excitation regulates its expression. Here, we characterised the Drosophila dpum promoter and identified multiple Myocyte enhancer factor-2 (Mef2)-binding elements. To understand the transactivation capability of dMef2, we cloned 12 dmef2 splice variants and used a luciferase-based assay to monitor dpum promoter activity. Whilst all 12 dMef2 splice variants enhance dpum promoter activity, exon 10-containing variants induce greater transactivation. Previous work shows dPum expression increases with synaptic excitation. However, we observe no change in dmef2 transcript in CNS exposed to picrotoxin (PTX). The lack of activity-dependence is indicative of additional regulation. We identified p300 as a likely candidate. We show that by binding to dMef2, p300 represses dpum transactivation. Significantly, p300 transcript is down-regulated by enhanced synaptic excitation (PTX) which, in turn, increases transcription of dpum through derepression of dMef2. These results suggest the activity-dependent expression of dpum is regulated by an interaction between p300 and dMef2.

neuroscience↗