cMFA for multi-omics data integration in microbial
community models

Sthyve Junior Tatho Djeanou, Simon Labarthe, Valentina Baldazzi

► To cite this version:

Sthyve Junior Tatho Djeanou, Simon Labarthe, Valentina Baldazzi. cMFA for multi-omics data integration in microbial community models. CMSB 2025 - 23rd International Conference on Computational Methods in Systems Biology, Sep 2025, Lyon, France. hal-05261543

HAL Id: hal-05261543

https://hal.science/hal-05261543v1

Submitted on 15 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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## INTRODUCTION

### Biology-informed inference from multi-omics data

Understanding natural microbial communities presents significant challenges due to the complex interactions and metabolite exchanges between microorganisms. To gain deeper insights into these dynamics, we are focusing on:

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* Developing mathematical models based on metabolic networks.

* Inferring metabolic fluxes using biology-informed high-dimensional analysis methods to integrate multi-omics data.

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### Microbial Community

### Scientific Question:

How can mathematical models help us better understand the functioning of microbial communities ?

* Internal flux reconstruction based on some external reaction rates.

* Estimating the contribution of each microorganism to the community.

## COMMUNITY METABOLIC FLUX ANALYSIS (CMFA)

### METABOLIC NETWORK

A metabolic network refers to the interconnected set of biochemical reactions and pathways that occur within a cell or organism.

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$$S\nu = 0$$

### COMMUNITY MFA (cMFA)

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### MATHEMATICAL FORMULATION

Find, for $t \ge 0$, for a set $\mathcal{M}$ of $N_m$ metabolites and for a set $\mathcal{B}_m$ of $N_b$ micro-organisms the flux vector

$$\hat{v}(t) = (\nu^{(\hat{b}_1)}(t), \dots, \nu^{(\hat{b}_{N_b})}(t)) \text{ of dimension } N_r$$

such that

$$\hat{v}(t) := \arg \min_{\nu_t} \sum_{m \in \mathcal{M}} \left\| R_{m,t} - \sum_{b \in \mathcal{B}_m} \chi_t^{(b)} \nu_{m,t} \right\|^2 + \lambda \mathcal{R}(T\nu) \\ \text{subject to: } \begin{cases} S^{(b)} \cdot \nu^{(b)} \\ C_{\min}^{(b)} \le \nu^{(b)} \end{cases} = 0 \quad \text{for } b \in \mathcal{B}_m \\ \le C_{\max}^{(b)},$$

Regression problem is:

* High-dimensional ($N_r \sim 2500 \times N_b$)

* Highly-underdetermined ($N_r \gg N_m$)

* Biology-informed ($S^{(b)}$)

* Constrained by metatranscriptomics $\mathcal{R}(T\nu)$: where $T$ is the reaction weight reflecting the level of gene expression.

* Regularized by a lasso ($\mathcal{R}(\nu) = \|\nu\|_1$) penalty to promote sparsity

### CO-CULTURE DEFINITION

Different biological interaction of E. coli

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### BENCHMARK METHODOLOGY

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### GENERATING DATA: DFBA

Dynamic Flux Balance Analysis (DFBA) is a mathematical modeling approach to predict the metabolites fluxes in metabolic networks.

* **Model origin**

BIGG Models
Database iJO1366.

$$\begin{aligned} \partial_t b_i &= B \hat{v}_i (M, b) b_i \\ \partial_t M_j &= \sum_{i \in C} \hat{v}_{i,j} (M, b) b_i \end{aligned}$$

* **model adjustment**

knocking out specific
genes and reactions

$$S.T \left\{ \begin{array}{l} \hat{v}_i = \arg \max B \nu_i \\ S_i \hat{v}_i = 0 \\ C_M \le \hat{v}_i^s \le 0 \\ 0 \le \hat{v}_i^p \le C_{max} \\ -C_{max} \le \hat{v}_i^{in} \le C_{max} \end{array} \right.$$

### IMPLEMENTATION

1. NumPy, Pandas for data handling and structure.

2. PyGAM for spline smoothing.

3. Matplotlib for visual representations.

4. COBRApy for constraint-based modeling.

5. OSQP for quadratic programming problems.

## RESULTS

### INFERRING INDIVIDUAL FLUXES: DFBA VS CMFA

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Internal fluxes cMFA vs dFBA

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### BENCHMARK ON METHOD ROBUSTNESS

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## CONCLUSION & PERSPECTIVES

**Conclusion** : cMFA is robust to noise, metabolic import rates, and exchange rate inference with both high- and low-quality data. It scales well to large communities, but internal flux inference is less reliable with lower-quality data.

**Perspectives** : Large Community, missing metabolites, benchmark on real data.

## REFERENCES

1. Brunner et al., 2023, PLoS Comput. Biol. 19, e1011661

2. Junior, M.L. et al. (2020). PLoS Comput. Biol., 16, e1007099.

3. Gerken Starepravo et al., 2022, Digit. Chem. Eng. 2, 100011

4. Lecomte et al., 2023, Metab. Eng. doi.org/10.1016/j.ymben.2024.02.014

5. Mahadevan et al., 2002, Biophys. J. 83, 1331–1340

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