We are interested in quantitative studies of biological systems. We apply, develop and integrate theoretical, computational and experimental methods and tools to address key biological questions. We believe that interdisciplinary approaches focusing on quantitative questions at systems level will gain new insights and uncover new principles in biological systems. Our current research areas include: relationship between function and topology in biological networks; design principles in cell cycle regulation; cellular decision-making; cell fate in development and reprogramming; microbial growth, metabolism and response to the environment; cellular oscillation in organs; application of information theory and artificial intelligence in biological systems.
Network function and topology
There is a close relationship between a network's function and its architecture (topology). Understanding this function-topology mapping would provide a framework to functionally classify and understand the complex biological networks, as well as a design table for synthetic biology. Using computational methods, we have been investigating the function-topology relationship for a number of functional modules. An example is the biochemical adaptation circuits. We have identified all the circuit architectures that can perform adaptation robustly. Despite the huge diversity of biochemical networks, there are only two core solutions to achieve perfect adaptation. We also studied the minimal circuits to generate cell polarity, in which computational method was combined with synthetic biology to identify and demonstrate the core topologies of cell polarity.
Cell cycle regulation
The eukaryotic cell cycle is a highly conserved process and its malfunction is a hallmark of cancer. This complex process of cell replication and division consists of a series of transitions between distinct events. We are interested in quantitative mechanisms and general principles in the design of the system to ensure precise, robust and decisive transitions and how perturbations/mutations can compromise the system. Using budding and fission yeasts as model organisms, we investigate these questions with combinations of mathematical modeling, yeast genetics, time-lapse fluorescent microscopy, single cell assays and microfluidic devices.
Cellular decision-making and fate determination
Cells have to make various decisions in response to external and internal cues, and often make fate choices. Examples range from the stress response of single cell organisms, development of multicellular organisms, and stem cell differentiation and reprogramming. We are interested in the strategies, mechanisms, information flow, general principles and mathematical framework in these systems and processes. Also, in collaboration with colleagues at Hong Kong Baptist University, we use C. elegans as the model system to try to understand the precision and robustness in embryogenesis and to decipher the “algorithm” of development.
Microbial growth, division and metabolism
The growth, metabolism and proliferation of microbials are closely coupled to the environments. Are there any quantitative laws and how to understand them? More than 70 years ago, Monod discovered the two strategies of bacteria in taking up carbon sources: diauxie and co-utilization. We found that this is a consequence of optimal growth and the topology of the metabolic network. Yeast cells have to coordinate growth and division in response to environmental conditions. We found that budding yeast implements long- and short-term molecular memories of the environment to assist this critical coordination.
Coupled cellular oscillation
There are many examples of biological oscillators, such as the circadian clock, heart beat and the pulsatile secretion of insulin. Under the stimulation of glucose, cells in pancreatic islets display periodic Ca2+ oscillations, which modulate the secretion of the hormones. We are interested in the mechanism, function and control of the various oscillation modes. In another project and in collaboration with colleagues at Osaka University, we studied the circulating reentrant waves in self-organized rings of hiPSC-derived heart cells.
Information theory and artificial intelligence
Biological systems are information processors. Can one make sense of these information processing in the language of information theory? On the other hand, can one learn the biological system by using machine learning? We have recently studied the encoding strategy of the olfaction system in fruit fly from an information theoretic point of view. We have also tried to use deep learning neuron network to decipher the underlying genetic network from the gene expression data.
