Computational Systems Biology

Uncovering biological mechanisms through the intersection of mechanistic modeling and scalable algorithms.

Our lab develops predictive models of cell metabolism, protein expression, and gene regulation. These models integrate multiple biological processes and large-scale networks. Interpreting biological data in the context of these integrated models provides a systems-level perspective on cellular functions. Application areas include microbial production of chemicals, infectious disease, and human diseases linked to nutritional deficiencies or distinct metabolic shifts.

Principal Investigator


Laurence Yang, PhD

Principal Investigator

Assistant Professor

Department of Chemical Engineering
Queen's University

Tel: 613-533-6000 ext. 75292
Fax: 613-533-6637
Email: laurence.yang [at] queensu.ca
Office: Dupuis Hall 304

Faculty profile page at Queen's

Dr. Laurence Yang is an Assistant Professor and Queen's National Scholar in Systems Biology in the Department of Chemical Engineering at Queen's University.


  • 2012: PhD in Chemical Engineering, University of Toronto
  • 2008: MASc in Chemical Engineering, University of Toronto
  • 2006: BASc in Chemical Engineering, University of Toronto

Research Experience

  • 2018-2019: Assistant Project Scientist, Department of Bioengineering, UCSD
  • 2014-2017: Postdoctoral Scholar, Department of Bioengineering, UCSD
  • 2012-2014: Scientist, Intrexon Corporation



  • Ej Jun Lung: CHEE 408 thesis student (2020-2021)
  • Sebastien Santoro-Gray: NSERC USRA researcher (2021)


Inquiries regarding graduate student positions are always welcome--please contact us!


A By imposing data-driven constraints onto ME models, we can more accurately predict phenotypes that reflect the generalist life-style of microbes like E. coli.


Modeling cell metabolism and protein expression

Cell metabolism is the set of chemical reactions that sustain life for an organism. Even the simplest microbes possess a complex metabolism comprised of a network of hundreds of enzyme-catalyzed reactions.

Given the genome sequence of an organism, systems biologists can reconstruct the metabolic reaction network. This network is formulated as a matrix, where rows represent metabolites and columns represent reactions. To predict the reaction rates (fluxes) throughout this network, we use flux balance analysis (FBA) (Orth et al., 2011). FBA was developed in the early 90s and in numerous studies since, FBA has accurately predicted systems-level metabolic responses in the context of biotechnology, infectious disease, cancer metabolism, and environment engineering.

The underlying concept of FBA is that cellular behavior can be described by an optimization model. We assume that the organism's metabolism has evolved to operate optimally. In this framework, researchers will have opportunities to express their biological knowledge and intuition in the form of computable, mathematical models. These models then provide an in silico platform to virtually test hypotheses and to design efficient experiments.

Related manuscripts:

B Limitations in protein expression machinery constrains how quickly the proteome can be reallocated under dynamic environmental changes. By expressing this constraint mathematically, our models more accurately predict protein expression dynamics, including shifts in energy metabolism (shifting reliance from oxidative phosphorylation to substrate-level phosphorylation) under dynamically changing nutrient availability.


A Cell metabolism can be modeled as a linear program.


B The cellular goal (objective function) is not always known.


C We developed scalable algorithms learn cellular goals from omics data.


Scalable algorithms for systems biology

Optimization models are used to predict cellular behavior--over 25 years in the case of cell metabolism. The constraints in these models are reconstructed from genome annotations, measured macromolecular composition, and by measuring phenotype in different conditions.

The cellular goal can be challenging to define for many organisms, including human tissue, microbial pathogens, and cancer cells. A promising approach is to estimate these goals directly from omics measurements, given a starting metabolic reconstruction. A particuarly flexible method is estimating new linear constraints that model unknown biochemical reactions that constrain the cell's operation.

However, this approach requires solving a nonconvex optimization problem, which may not scale to large models. To tackle this challenge, we develop scalable algorithms using distributed computing on CPUs and GPUs. Our algorithms thus learn new models from high-throughput data sets, leading to increasingly accurate prediction of cellular behavior under conditions that were previously difficult to model.

Researchers will have ample opportunity to deploy machine learning and distributed algorithms on big biological data sets. These algorithms can improve the accuracy of model predictions, or to help understand biological mechanisms by constructing explainable models from data.

Our lab aims to:

  • develop scalable algorithms to estimate model parameters from multi-omics data (i.e., data sets comprised of multiple omics technologies)
  • learn models of metabolism and protein expression from multi-omics data, including microbial community models and host-pathogen models

Related manuscripts:

A Genome-scale models of metabolism and protein expression can be reconstructed for microbes


B Microbial stress responses are reconstructed from molecular mechanisms


C These models can help discover new antimicrobials


Modeling microbial stress response

Aerobic organisms have evolved cellular responses against reactive oxygen species (ROS) over the coures of 3 billion years, since the Earth's atmosphere became oxygenated. ROS targets macromolecules and metal cofactors, which can impair cellular function. ROS is generated as a by-product of normal metabolic operation. It is also used as an offensive tool by competing microbes and the human immune system.

Adaptation against ROS stress can require system-level responses, including metabolic shifts, use of alternative cofactors, and change in macromolecular composition. Adaptation against other stresses (e.g., thermal, pH, osmotic) requires similar system level responses.

Such system-level stress responses can be modeled using genome-scale models that integrate the processes of metabolism and macromolecule expression (ME models). By extending these models with the thermodynamic and kinetic processes associated with stress-related damage and repair of cellular components, it is possible to model microbial stress response. These extended models are collectively termed StressME models.

Our lab aims to:

  • model additional key stress responses (e.g., osmotic stress)
  • investigate the role of stress tolerance in infectious disease
  • model cellular response to osmotic, oxidative, and pH stress in bioprocess environments

Related manuscripts:


Please see complete publication list on Google Scholar
†: corresponding author(s); bold: lab members