Reprogramming biology
Biology offers many capabilities that could address the global challenges we face as a society. The Biocompute Lab is attempting to understand how to effectively reprogram biology to co-opt and install new functionalities into living systems across scales; from networks of interacting molecules, to cellular collectives, and even entire ecosystems.
The only way to learn a new programming language is by writing programs in it.
— Dennis Ritchie
Core strands
Adaptive genetic circuits
In the words of Darwin: "It is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able to adapt to and to adjust". With this in mind, we are developing new types of genetic part and circuitry whose function can adapt as needed over time. This allows their function to be optimised and robustly maintained even when exposed to diverse and changing environments.
Data-centric biological design
Modern experimental methods can collect vast amounts of information and provide a detailed window into the inner workings of cells. To make sense of these rich, multi-dimensional datasets, we are employing machine learning and artificial intelligence approaches to unravel the complex interactions and rules that guide the behavior of biological systems. Using this information we aim to create improved biological design workflows.
Unconventional computing
Biology often stores and processes information in very different ways than the typical electronic computers we commonly use. The Biocompute Lab is interested in exploring this diversity in computational architecture for inspiration into new ways to build computers that function requiring less power, that are able to function in harsh environments, and that have the potential to outperform those in use today. We value the weirder and the wackier!
Biodesign across scales
From collections of molecules within a cell to our planetary ecosystem, biology functions at many length and time scales. Understanding how these functions emerge at differing scales is challenging, but essential if we are to effectively reprogram biology. We are tackling this question using high-performance computing and advanced multi-agent simulation to guide experiments and explore the key ingredients for emergence across scales.
Selected publications
Engineering is evolution: a perspective on design processes to engineer biology
Castle S.D., Stock M., Gorochowski T.E.
Nature Communications, 2024. (in press)
Open-endedness in synthetic biology: a route to continual innovation for biological design
Stock M., Gorochowski T.E.
Science Advances 10, eadi3621, 2024.
An open platform for high-resolution light-based control of microscopic collectives
Rubio Dennis A., Gorochowski T.E., Hauert S.
Advanced Intelligent Systems , 2200009, 2022.
Massively parallel characterisation of engineered transcript isoforms using direct RNA sequencing
Tarnowski M.J., Gorochowski T.E.
Nature Communications 13, 434, 2022.
Efficient multiplexed gene regulation in Saccharomyces cerevisiae using dCas12a
Ciurkot K., Gorochowski T.E., Roubos J., Verwaal R.
Nucleic Acids Research 49, 7775-7790, 2021.
Harnessing the central dogma for stringent multi-level control of gene expression
Greco F.V., Pandi A., Erb E.J., Grierson C.S., Gorochowski T.E.
Nature Communications 12, 1738, 2021.
Tunable genetic devices through simultaneous control of transcription and translation
Bartoli V., Meaker G.A., di Bernardo M., Gorochowski T.E.
Nature Communications 11, 2095, 2020.
Pathways to cellular supremacy in biocomputing
Grozinger L., Amos M., Gorochowski T.E., Carbonell P., Oyarzún D.A., Stoof R., Fellermann H., Zuliani P., Tas H., Goñi-Moreno A.
Nature Communications 10, 5250, 2019.
Burden-driven feedback control of gene expression
Ceroni F., Boo A., Furini S., Gorochowski T.E., Borkowski O., Ladak Y.N., Awan A.R., Gilbert C., Stan G.B., Ellis T.
Nature Methods 15, 387-393, 2018.
Key people
Visiting Student
PhD Student
Postdoctoral Research Associate
PhD Student
PhD Student
PhD student