Research Projects

Gene expression is a fundamental biological process through which genetic instructions encoded in DNA are transformed into functional proteins. This process is often controlled by regulatory molecules such as transcription factors, small RNAs, and RNA-binding proteins, whose activity depends on specific cellular conditions and determines when and to what extent a gene is expressed. However, gene expression is an inherently noisy process, with stochastic fluctuations arising from transcription factor binding, bursty transcription, variable mRNA lifetimes, and probabilistic translation events, etc. These sources of noise make reliable information transfer in gene expression a significant challenge, meaning it becomes difficult to ensure that changes in the concentration of regulatory molecules consistently lead to distinguishable and predictable changes in protein levels. Previous models studying this phenomenon primarily emphasized transcriptional regulation, often neglecting the potential impact of noise introduced during translation. Recent research, supported by our preliminary computational results, indicates that translational noise can substantially increase protein-level noise and thus weaken the correlation between mRNA and protein abundance. This strongly suggests that translational noise must be explicitly incorporated to accurately assess how reliably regulatory input signal can be translated into the desired gene expression output—an aspect that remains largely under-investigated in prior studies. The central aim of this proposal is to systematically quantify how reliably gene expression transmits information when both transcriptional and translational noise are explicitly considered. In this framework, we treat gene expression as an information channel where the regulatory input is the concentration of molecules such as transcription factors or other regulatory elements, and the output is the resulting protein concentration. Using information-theoretic tools like mutual information and channel capacity, we will assess how accurately changes in the regulatory input are reflected in the protein output despite the inherent noise present in the system. This project seeks to answer four fundamental and unresolved questions: 1. How reliably can regulatory input signals control protein expression under combined transcriptional and translational noise? 2. How does information transfer differ between transcriptional, translational, and combined regulation of gene expression? 3. How does shifting the balance between transcription and translation dominated gene expression, at fixed protein output, affect information transfer efficiency? 4. How are gene expression noise and information capacity related, and how evolutionary selection pressure has optimized this balance through evolution? Our initial results provide strong support for the proposed hypothesis and approach. Through stochastic simulations using Gillespie’s algorithm, we found that translational noise can amplify gene expression variability by over 300%. Additionally, experimental data from mouse fibroblast cells reveal only weak-to-moderate correlations between mRNA and protein levels, highlighting the importance of explicitly accounting for translational noise in gene expression models. We have also confirmed the accuracy of our computational framework for channel capacity estimation by successfully validating the Blahut-Arimoto algorithm against known analytical benchmarks. The novelty of this proposal lies in its computational rigor and biological relevance. The outcomes of this work will advance fundamental understanding in molecular biology and have practical relevance for synthetic biology, gene therapy, and precision biosensors. The insights gained can guide the design of genetic circuits with improved noise control and reliable information transmission, which are essential for creating smart, predictable, and efficient bioengineered systems.