Biology subjects

Ham, L.

Publications and source records attributed to Ham, L..

9 recordsLinked to original sources

bioRxiv2025

Intercellular signaling drives robust cell fate and patterning in multicellularsystems

Cells do not act in isolation; they communicate with each other through a complex network of signals. In multicellular organisms, cell signaling buffers against fluctuations in gene expression. Its effect, however, on multistability--the ability of genetically identical cells to take on and maintain diverse stable states or phenotypes--is unclear. We develop spatially explicit stochastic models that couple fine-grained gene regulatory dynamics with intercellular signaling to study cell fate control in multicellular tissues. We show that intercellular signaling acts as a switch-like controller of cell fate, driving transitions from transient to stable states across tissues. Even weak signaling stabilizes short-lived expression states, and we derive mathematical expressions for the threshold signaling strength required to trigger tissue-wide stabilization. We also derive a universal spatial limit: stable region size grows only sublinearly with signaling strength, implying that large stable regions are prohibitively costly to maintain. The resulting trade-off between robustness and signaling in cell fate control highlights the delicate balance required during developmental processes to maintain spatial precision and provides insight into how organisms regulate tissue structure. OverviewCell fate decisions in developing tissues are governed by gene regulatory networks, but how communication between neighboring cells influences these decisions is poorly understood. We develop a mathematical framework showing that intercellular signaling acts as a controller of cell fate, suppressing spontaneous switching, and inducing stable, coordinated behaviors across tissues. Even weak signaling induces a sharp transition from unstable to stable population-wide dynamics. We also derive fundamental scaling laws showing that the size of stable regions increases sublinearly with signaling strength, revealing a trade-off between developmental robustness and resource cost. These results offer insight into how organisms achieve reliable spatial organization during development, despite molecular noise, and provide design principles for engineering synthetic tissues that balance stability, scalability, and resource efficiency.

systems biology↗

bioRxiv2025

Molecular noise modulates transitions in the cell-fate differentiation landscape

Waddingtons epigenetic landscape has become one of biologys cornerstone metaphors, widely used both conceptually and computationally. Cell types are often associated with the stable stationary points or valleys of this landscape. In previous work, we showed that the molecular noise dominating sub-cellular dynamics can distort and profoundly reshape this landscape. In non-equilibrium systems, an equally profound question arises: to what extent does noise alter the transition paths between valleys in such dynamic landscapes. We tackle this question using a set of illustrative exemplars, and show that noise gives rise to paths that differ substantially from the canonical least-action paths calculated under deterministic dynamics. We dissect the dynamics of these exemplars and determine the reactive density and transition currents, which show us, respectively, where and how transitions occur for different realisations of stochastic dynamics. Our analysis unambiguously demonstrates that reaction paths for stochastic dynamics diverge non-trivially from their deterministic least-action paths or simple barrier crossing models.

developmental biology↗

bioRxiv2023

The Topological Properties of the Protein Universe

Deep learning methods have revolutionized our ability to predict protein structures, allowing us a glimpse into the entire protein universe. As a result, our understanding of how protein structure drives function is now lagging behind our ability to determine and predict protein structure. Here, we describe how topology, the branch of mathematics concerned with qualitative properties of spatial structures, provides a lens through which we can identify fundamental organizing features across the known protein universe. We identify topological determinants that capture global features of the protein universe, such as domain architecture and binding sites. Additionally, our analysis also identified highly specific properties, so-called topological generators, that can be used to provide deeper insights into protein structure-function and evolutionary relationships. We used our approach to determine structural, functional and disease consequences of mutations, explain differences in properties of proteins in mesophiles and thermophiles, and the likely structural and functional consequences of polymorphisms in a protein. Overall, we present a practical methodology for mapping the topology of the known protein universe at scale.

bioinformatics↗

bioRxiv2023

The timing of cellular events: a stochastic vs deterministic perspective

Changes in cell state are driven by key molecular events whose timing can often be measured experimentally. Of particular interest is the time taken for the levels of RNA or protein molecules to reach a critical threshold defining the triggering of a cellular event. While this mean trigger time can be estimated by numerical integration of deterministic models, these ignore intrinsic noise and hence their predictions may be inaccurate. Here we study the differences between deterministic and stochastic model predictions for the mean trigger times using simple models of gene expression, post-transcriptional feedback control, and enzyme-mediated catalysis. By comparison of the two predictions, we show that when promoter switching is present there exists a transition from a parameter regime where deterministic models predict a longer trigger time than stochastic models to a regime where the opposite occurs. Furthermore, the ratio of the trigger times of the two models can be large, particularly for auto-regulatory genetic feedback loops. Our theory provides intuitive insight into the origin of these effects and shows that deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells.

systems biology↗

bioRxiv2021

The chemical Langevin equation for biochemical systems in dynamic environments

Modelling and simulation of complex biochemical reaction networks form cornerstones of modern biophysics. Many of the approaches developed so far capture temporal fluctuations due to the inherent stochasticity of the biophysical processes, referred to as intrinsic noise. Stochastic fluctuations, however, predominantly stem from the interplay of the network with many other -- and mostly unknown -- fluctuating processes, as well as with various random signals arising from the extracellular world; these sources contribute extrinsic noise. Here we provide a computational simulation method to probe the stochastic dynamics of biochemical systems subject to both intrinsic and extrinsic noise. We develop an extrinsic chemical Langevin equation--a physically motivated extension of the chemical Langevin equation-- to model intrinsically noisy reaction networks embedded in a stochastically fluctuating environment. The extrinsic CLE is a continuous approximation to the Chemical Master Equation (CME) with time-varying propensities. In our approach, noise is incorporated at the level of the CME, and can account for the full dynamics of the exogenous noise process, irrespective of timescales and their mismatches. We show that our method accurately captures the first two moments of the stationary probability density when compared with exact stochastic simulation methods, while reducing the computational runtime by several orders of magnitude. Our approach provides a method that is practical, computationally efficient and physically accurate to study systems that are simultaneously subject to a variety of noise sources.

systems biology↗

bioRxiv2020

Shaping the Epigenetic Landscape: Complexities and Consequences

The Waddington epigenetic landscape has become an iconic representation of the cellular differentiation process. Recent single-cell transcriptomic data provide new opportunities for quantifying this originally conceptual tool, offering insight into the gene regulatory networks underlying cellular development. While many methods for constructing the landscape have been proposed, by far the most commonly employed approach is based on computing the landscape as the negative logarithm of the steady-state probability distribution. Here, we use simple models to highlight the complexities and limitations that arise when reconstructing the potential landscape in the presence of stochastic fluctuations. We consider how the landscape changes in accordance with different stochastic systems, and show that it is the subtle interplay between the deterministic and stochastic components of the system that ultimately shapes the landscape. We further discuss how the presence of noise has important implications for the identifiability of the regulatory dynamics from experimental data.

developmental biology↗

bioRxiv2020

Pathway dynamics can delineate the sources of transcriptional noise in gene expression

Single-cell expression profiling has opened up new vistas on cellular processes. Among other important results, one stand-out observation has been the confirmation of extensive cell-to-cell variability at the transcriptomic and proteomic level. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges in inferring dynamics, as well as causes of cell-to-cell variability. In particular, we are typically unable to separate dynamic variability from within individual systems ("intrinsic noise") from variability across the population ("extrinsic noise"). Here we mathematically formalise this non-identifiability; but we also use this to identify how new experimental set-ups coupled to statistical noise decomposition can resolve this non-identifiability. For single-cell transcriptomic data we find that systems subject to population variation invariably inflate the apparent degree of burstiness of the underlying process. Such identifiability problems can, in principle, be remedied by dual-reporter assays, which separates total gene expression noise into intrinsic and extrinsic contributions; unfortunately, however, this requires pairs of strictly independent and identical gene reporters to be integrated into the same cell, which is difficult to implement experimentally in most systems. Here we demonstrate mathematically that, in some cases decomposition of transcriptional noise is possible with non-identical and not-necessarily independent reporters. We use our result to show that generic reporters lying in the same biochemical pathways (e.g. mRNA and protein) can replace dual reporters, enabling the noise decomposition to be obtained from only a single gene. Stochastic simulations are used to support our theory, and show that our "pathway-reporter" method compares favourably to the dual-reporter method.

biophysics↗

bioRxiv2020

Exactly solvable models of stochastic gene expression

Stochastic models are key to understanding the intricate dynamics of gene expression. But the simplest models which only account for e.g. active and inactive states of a gene fail to capture common observations in both prokaryotic and eukaryotic organisms. Here we consider multistate models of gene expression which generalise the canonical Telegraph process, and are capable of capturing the joint effects of e.g. transcription factors, heterochromatin state and DNA accessibility (or, in prokaryotes, Sigma-factor activity) on transcript abundance. We propose two approaches for solving classes of these generalised systems. The first approach offers a fresh perspective on a general class of multistate models, and allows us to "decompose" more complicated systems into simpler processes, each of which can be solved analytically. This enables us to obtain a solution of any model from this class. We further show that these models cannot have a heavy-tailed distribution in the absence of extrinsic noise. Next, we develop an approximation method based on a power series expansion of the stationary distribution for an even broader class of multistate models of gene transcription. The combination of analytical and computational solutions for these realistic gene expression models also holds the potential to design synthetic systems, and control the behaviour of naturally evolved gene expression systems, e.g. in guiding cell-fate decisions.

systems biology↗

bioRxiv2019

Extrinsic noise and heavy-tailed laws in gene expression

Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our Ansatz allows us to incorporate the effects of extrinsic noise - encompassing factors external to the transcription of the individual gene - and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

systems biology↗