Bacterial Reservoir Computer

**Bacterial Reservoir Computer**:

- Réf **ABG-128046**
- Sujet de Thèse
- 21/01/2025
- Autre financement public
- Jean-Loup Faulon
- Lieu de travail- Jouy-en-Josas - Ile-de-France - France
- Intitulé du sujet- Bacterial Reservoir Computer
- Champs scientifiques- Biotechnologie
- Mots clés- Systems Biology, Machine Learning, Reservoir Computing, Synthetic Biology, Genome-Scale Metabolic Models, Microbiology

**Description du sujet**:

- In contrast to traditional bottom-up approaches that build biological devices for computation within organisms [1-3], this PhD project proposes a novel top-down strategy. The goal is to leverage bacterial strains in a reservoir computing (RC) framework to solve complex computational tasks.
- Engineering bottom-up biological devices is challenging. These devices place a significant metabolic burden on host cells, are difficult to fine-tune, and are prone to noise [4, 5]. The design of such devices often draws inspiration from biological information-processing systems, akin to logic gates, switches, and perceptron, already found in nature [6, 7]. This raises an intriguing question**:_instead of constructing devices from the ground up, could natural microorganisms themselves be harnessed for complex computational tasks?_**

**Objective and context**
- Reservoir computing (RC) is a branch of artificial intelligence exploring the computational capabilities of physical, chemical, and biological systems [8]. Initially developed as an alternative to classical artificial neural networks, particularly recurrent neural networks (RNNs), RC offers a more efficient training process. There are two kinds of RC systems: conventional RC and physical RC.
- This PhD project aims to investigate the feasibility of using bacterial strains within an RC framework, assessing their potential as reservoirs for computation. Like other machine learning methods, RC relies on training data with features and labels, seeking to predict labels from features. In a bacterial reservoir approach, problem features are represented as nutrients provided to the bacteria, and bacterial responses are measured through phenotypic observations. These measurements are then processed by classical machine learning regressor or classifier to produce solutions to computational tasks.

**Methods and Work Plan**
- Practically, the project will begin (Year 1) by using _E. coli_ as a test strain, developing a hybrid model trained on media supplemented with different metabolites and acquiring growth curves. Various neural mechanistic models will be benchmarked among these Physics Informed Neural Networks (PINNs [13]), neural - flux balance analysis (FBA) [14] and neural dynamic-FBA. The computational capabilities of this _E. coli_ reservoir will then be benchmarked against classical machine learning techniques, such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), across various regression and classification tasks. As shown in Faulon _et al_. [15], preliminary results obtained on a set of _E. coli_ growth rates acquired for different nutrients (i.e., different sets of sugars, amino acids, nucleotides) indicate such approach can be used to classify linear and non-linear patterns.
- For all testing on clinical samples the PhD student will have access to prostate cancer cohort and Covid-19 cohort provided by the University Hospitals of Montpelier and Grenoble. Additional samples for other diseases may be acquired during the course of the project. The possibility of monitoring environmental pollutants (for instance in water) will also be investigated using the multi-species RC framework.

**References**
- 1. Purnick, P. E. M. & Weiss, R. The second wave of synthetic biology: from modules to systems. _Nat. Rev. Mol. Cell Biol. _**10**, 410-422 (2009).
- 2. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. _Nature _**403**, 339-342 (2000).
- 3. Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. _Nature _**403**, 335-338 (2000).
- 4. Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. _Proc. Natl. Acad. Sci. _**99**, 12795-12800 (2002).
- 5. Borkowski, O., Ceroni, F., Stan, G.B. & Ellis, T. Overloaded and stressed: whole-cell considerations for bacterial synthetic biology. _Curr. Opin. Microbiol. _**33**, 123-130 (2016).
- 6. Hellingwerf, K. J., Postma, P. W., Tommassen, J. & Westerhoff, H. V. Signal transduction in bacteria: phospho-neural network(s) in _Escherichia coli_ ? _FEMS Microbiol. Rev. _**16**, 309-321 (1995).
- 7. Scheres, B. & Van Der Putten, W. H. The plant perceptron connects environment to development. _Nature _**543**, 337-345 (2017).
- 8. Tanaka, G. _et al._ Recent advances in physical reservoir computing: A review. _Neural Netw. _**115**, 100-123 (2019).
- 10. Fernando, C. & Sojakka, S. Pattern Recognition