Introduction to Flux Balance Analysis and Metabolic Engineering

Introduction

Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cell’s production of a desired small molecule or macromolecule product, e.g., proteins of interest, and is a critical component of Biotechnology. There are two categories of biotechnology: industrial and medical. Metabolic engineering plays a vital role in both sectors. Industrial biotechnology is typically consumer-focused, e.g., components of consumer products such as detergents, food products, and small-molecule chemical feedstocks. On the other hand, medical biotechnology develops molecules for human (and animal) health applications, e.g., antibodies, therapeutic proteins, vaccines, etc.

The overall objective of a metabolic engineer is to maximize the performance of a metabolic network, e.g., develop a system that produces a desired product with the highest possible yield or at the maximum possible rate, etc. Toward this objective, metabolic engineers manipulate the biochemical networks that cells use to convert raw materials into molecules necessary for the cell’s survival. Metabolic engineering specifically seeks to:

1. Mathematically model biochemical networks, calculate the yield (product divided by substrate) of valuable products, and identify parts of the network that constrain the production of these products of interest.

2. Use genetic engineering techniques to modify the biochemical network to relieve constraints limiting production. Metabolic engineers can then model the modified network to calculate the new product yield and identify new constraints (back to 1).

Thus, a key component of metabolic engineering is the mathematical analysis of (bio)chemical reaction networks. Toward this challenge, we’ll introduce a widely used tool to model and analyze reaction networks: flux balance analysis. In particular, this lecture will:

• Introduce the Theory of Flux Balance Analysis, the Traditional FBA problem structure and an Alternative approach that uses mole balances and the open extent of reaction.

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Theory of Flux Balance Analysis

Flux balance analysis (FBA) is a mathematical modeling and analysis approach that estimates the intracellular or cell-free reaction rates (which are called fluxes) throughout a steady state reaction network (units: \(\star\)mol/volume-time). However, while we are introducing flux balance analysis in the context of metabolic network analysis, FBA is a general tool that can be used to estimate flows through many different types of networks and graphs, e.g., social graphs, communication networks, or other types of problems that can be represented as a network of graphs.

Flux balance analysis is arguably the most widely used computational tool in metabolic engineering. However, there are alternatives to FBA, such as metabolic flux analysis (MFA), but these alternatives vary more in the solution approach than the structure of the estimation problem.

Traditional FBA problem structure

Suppose we have a system, which can be either open or closed, with a species set \(\mathcal{M}\) and a reaction set \(\mathcal{R}\). Further, suppose that the system (or at least part of it) is at or near a steady state. Then, the objective of the flux balance analysis is to estimate the value of the reaction rates operating in the system using linear programming.

Remark 8 (What is linear programming?)

Linear programming (LP) is a method to estimate the best outcome (such as maximum profit, lowest cost, or the best possible rate of production) using a mathematical model whose requirements are represented by linear relationships subject to linear constraints.

The development of the simplex algorithm, an efficient approach for solving linear programs, has led to LPs being used to solve problems in many engineering applications and other diverse industries such as banking, education, forestry, energy, and logistics.

Canonical linear program

Linear programs maximize (or minimize) a linear objective function \(\mathcal{O}\) subject to linear constraints and bounds on the variables we are searching over.

Suppose we need to estimate some \(M\)-dimensional vector \(\mathbf{x}\), e.g., the best possible allocation of some scarce resource such as cars to people in a ride-sharing application. Further, our problem can be represented as a system of linear equations. Then, we can estimate the optimal value for the variables \(\mathbf{x}\) by solving the linear program (canonical form):

\[\begin{split} \begin{eqnarray} \text{maximize/minimize}~\mathcal{O} &=& \sum_{i=1}^{M} c_{i}x_{i}\\ \text{subject to}~\mathbf{Ax} &\leq&\mathbf{b}\\ \text{and}~x_{i}&\geq&{0}\qquad{i=1,2,\dots,M} \end{eqnarray} \end{split}\]

The components of \(\mathbf{x}\) are the variables to be determined, \(c_{i}\) are constant coefficients in the linear objective function \(\mathcal{O}\), \(\mathbf{A}\) is a \(N\times{M}\) matrix and \(\mathbf{b}\) is a \(N\times{1}\) (constant) vector.

Flux balance analysis as a linear program

The problem we are trying to solve when performing flux balance analysis is to estimate the vector of unknown reaction rates in a metabolic network such that some objective is maximized, e.g., the maximum rate of production subject to material balance and reaction bounds constraints.

The flux balance analysis problem is typically encoded as a linear programming (LP) problem:

Definition 38 (Flux balance analysis problem)

Let a system consist of the species \(\mathcal{M}\) and reactions \(\mathcal{R}\). Further, let \(v_{i}\in\mathcal{R}\) denote the value of reaction rate (flux) \(i\) (units: mol/volume-time). Finally, we are interested in the steady-state value for the unknown reaction rates.

Then, the flux balance analysis problem, whose solution provides estimates for the values of the unknown fluxes inside the steady-state system, is a linear programming problem that maximizes (minimizes) the objective function:

\[\text{maximize/minimze}~\mathcal{O}=\sum_{i\in\mathcal{R}}c_{i}v_{i}\]

subject to the linear constraints:

\[\begin{split}\begin{eqnarray} \mathbf{S}\mathbf{v} &=& \mathbf{0}\\ \mathcal{L}\leq&\mathbf{v}&\leq\mathcal{U}\\ \dots &=& \dots \end{eqnarray}\end{split}\]

The matrix \(\mathbf{S}\) is the \(\mathcal{M}\times\mathcal{R}\) stoichiometric matrix, \(c_{i}\) denote the objective coefficients, \(\mathbf{v}\) denotes the unknown flux vector (the unknown that we are trying to estimate), and \(\mathcal{L}\) (\(\mathcal{U}\)) denote the permissible lower (upper) bounds on the unknown fluxes.

The first set of constraints in Definition 38 are material constraints, while the second imparts thermodynamic and kinetic information into the calculation. Finally, there are potentially other types of constraints (both linear and nonlinear) not shown in Definition 38 that may be necessary for specific applications encountered in practice.

Checkout out the following reference to understand better the different components of a flux balance analysis problem:

• Orth, J., Thiele, I. & Palsson, B. What is flux balance analysis?. Nat Biotechnol 28, 245–248 (2010). https://doi.org/10.1038/nbt.1614

Alternative approach

The standard flux balance analysis problem is written in concentration units, e.g., the metabolic reaction flux has units of \(\star\)mol/volume-time. However, there is nothing that says we have to do that. For example, it may be more convenient to work in mole units instead. In this case, we estimate the unknown open reaction extent instead of calculating the unknown reaction rate.

Mole-based problem formulation

We know from our earlier discussion of material balances that the open species mole balance around component \(i\) in a steady-state system with species \(\mathcal{M}\), streams \(\mathcal{S}\) and reactions \(\mathcal{R}\) is given by:

\[\sum_{s\in\mathcal{S}}v_{s}\dot{n}_{si} + \sum_{j\in\mathcal{R}}\sigma_{ij}\dot{\epsilon}_{j} = 0\qquad\forall{i}\in\mathcal{M}\]

If we don’t have kinetic rate laws, or we are not approaching this problem from the equilibrium or energy perspective, then we need some other way to estimate the extent of reaction \(\dot{\epsilon}_{j}\).

Observation 29 (Mole based problem formulation)

Suppose we have an open steady-state system with species \(\mathcal{M}\), streams \(\mathcal{S}\) and reactions \(\mathcal{R}\), but we have only a single stream entering (s = 1) and exiting (s = 2) the system. Further, suppose we want to maximize/minimize an objective of the form:

\[\text{maximize/minimze}~\mathcal{O}=\sum_{i\in\mathcal{R}}c_{i}\dot{\epsilon}_{i}\]

In other words, we want to estimate the open extent such that some technological objective is met, e.g., we maximize the production of a valuable product. For the case of a single input and output, the open mole balance becomes:

\[\dot{n}_{2,i} = \dot{n}_{1,i} + \sum_{j\in\mathcal{R}}\sigma_{ij}\dot{\epsilon}_{j}\qquad\forall{i}\in\mathcal{M}\]

Of course, when searching for the optimal set of reaction extents \(\dot{\epsilon}_{j}\) we have to select values that give physically realistic answers, e.g., we can’t have a negative mole flow rates: \(\dot{n}_{i,j}\geq{0}\) for all \(i\) and \(j\). Thus, the species mole balances (which are linear) are constraints that make sure we select physically realistic values for the optimal open extent:

\[\dot{n}_{1,i} + \sum_{j\in\mathcal{R}}\sigma_{ij}\dot{\epsilon}_{j}\geq{0}\qquad\forall{i}\in\mathcal{M}\]

Next, the \(\dot{\epsilon}_{j}\) terms, like concentration-based flux, must be bounded, i.e., we can’t have infinite values for the open reaction extent, nor can irreversible reactions be chosen to run backward. Thus, the extents are bounded from above and below:

\[\mathcal{L}_{j}\leq\dot{\epsilon}_{j}\leq\mathcal{U}_{j}\qquad{j=1,2\dots,\mathcal{R}}\]

The \(\mathcal{L}_{j}\) and \(\mathcal{U}_{j}\) denote the lower and upper bounds that \(\dot{\epsilon}_{j}\) can take. Open extents \(\dot{\epsilon}_{j}\) are just reaction rates times the volume; the lower and upper bounds describe the permissible range we expect the rate could take.

Putting these ideas together for the general case of multiple input and output streams gives a different problem formulation to compute the flux through a reaction network using mole based units:

Definition 39 (Mole based flux problem)

Suppose we have an open system with species \(\mathcal{M}\), streams \(\mathcal{S}\) and reactions \(\mathcal{R}\). Further, suppose we partition the stream set \(\mathcal{S}\) into streams entering the system \(\mathcal{S}^{+}\), and streams leaving the system \(\mathcal{S}^{-}\).

Then, the steady-state species mole balances are given by:

\[\sum_{s\in\mathcal{S}^{+}}\dot{n}_{si} - \sum_{k\in\mathcal{S}^{-}}\dot{n}_{ki} + \sum_{j\in\mathcal{R}}\sigma_{ij}\dot{\epsilon}_{j} = 0\qquad\forall{i}\in\mathcal{M}\]

Finally, we know that \(\dot{n}_{i,j}\geq{0}\) for every \(i\) and \(j\); species mole flows must be non-negative. Then, the (unknown) open extents \(\dot{\epsilon}_{j}\) are the solution of a linear programming problem in which the linear objective \(\mathcal{O}\):

\[\text{maximize/minimize}~\mathcal{O} = \sum_{j\in\mathcal{R}}c_{j}\dot{\epsilon}_{j}\]

is minimized (or maximized) subject to a collection of linear constraints:

\[\begin{split}\begin{eqnarray} \sum_{j\in\mathcal{R}}\sigma_{ij}\dot{\epsilon}_{j}&\geq&{-\sum_{s\in\mathcal{S}^{+}}\dot{n}_{si}}\qquad\forall{i}\in\mathcal{M}\\ \sum_{k\in\mathcal{S}^{-}}\dot{n}_{ki}&\geq&{0}\qquad\forall{i}\in\mathcal{M}\\ \mathcal{L}_{j}&\leq\dot{\epsilon}_{j}\leq&\mathcal{U}_{j}\qquad\forall{j}\in\mathcal{R} \end{eqnarray}\end{split}\]

Convex pathway analysis (advanced topic)

The stoichiometric matrix \(\mathbf{S}\) is a \(\mathcal{M}\times\mathcal{R}\) array holding the coefficients \(\sigma_{ij}\); each row of \(\mathbf{S}\) describes a metabolite, while each column represents a possible chemical reaction. Thus, the stoichiometric matrix is a digital representation of the biochemical capabilities of a metabolic network.

When formulating the stoichiometric matrix, what should be included to describe known or unknown desired phenomena? For example, suppose we are interested in producing product \(P\) or making sense of a metabolic data set. Should every possible reaction or just some subset of all possible reactions be included in the stoichiometric matrix? The practical significance of this question is two-fold:

• Fewer material balances are better when describing the behavior of a metabolic network, and

• Fewer chemical reactions (fluxes) result in less uncertainty when estimating the kinetic rates \(\hat{r}_{j}\) (bounds) or other constraints.

Convex pathway analysis is an approach to catalog all possible steady-state pathways through a reaction network. In particular, convex decomposition approaches posit that any potential steady-state flux can be represented as the convex combination of a set of convex independent basis vectors called extreme pathways \(\mathbf{P}_{i}\):

\[\mathbf{v} = \sum_{i=1}^{\mathcal{P}}\alpha_{i}\mathbf{P}_{i}\]

where \(\mathcal{P}\) denotes the number of basis pathways, and \(\mathbf{P}_{i}\) denotes the \(\mathcal{R}\times{1}\) extreme pathway basis vector. The term(s) \(\alpha_{i}\geq{0}\) denotes the weight of extreme pathway \(i\).

Extreme pathways are very difficult to compute but can give super exciting insights into the operation and capabilities of a metabolic network.

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Summary

This lecture introduced metabolic engineering and tools such as flux balance analysis to estimate the reaction rate through a (bio)chemical reaction network.

Flux balance analysis, written in either concentration or mole-based forms, is a linear programming problem in which the values for the unknown reaction rates or open reaction extents (which we collectively called fluxes) are estimated by minimizing (maximizing) the sum of the unknown fluxes subject to physical conservation constraints and constraints on the permissible values that the flux can take. While the material we introduced was in the context of estimating unknown reaction rates, the ideas that underly flux balance analysis are more general; these same principles are used in various applications in many disciplines.

In this lecture, we:

• Introduced the Theory of Flux Balance Analysis, the Traditional FBA problem structure and an Alternative approach that used mole balances.

References

Background resources for metabolic engineering:

• Bailey JE. Toward a science of metabolic engineering. Science. 1991 Jun 21;252(5013):1668-75. doi: 10.1126/science.2047876. PMID: 2047876.

• Stephanopoulos G, Vallino JJ. Network rigidity and metabolic engineering in metabolite overproduction. Science. 1991 Jun 21;252(5013):1675-81. doi: 10.1126/science.1904627. PMID: 1904627.

Background resources for biochemical network information:

• Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 Jan 1;28(1):27-30. doi: 10.1093/nar/28.1.27. PMID: 10592173; PMCID: PMC102409.

• Karp, Peter D et al. “The BioCyc collection of microbial genomes and metabolic pathways.” Briefings in bioinformatics vol. 20,4 (2019): 1085-1093. doi:10.1093/bib/bbx085

• Gama-Castro, Socorro et al. “RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond.” Nucleic acids research vol. 44,D1 (2016): D133-43. doi:10.1093/nar/gkv1156

Background resources convex pathway analysis:

• Bell SL, Palsson BØ. Expa: a program for calculating extreme pathways in biochemical reaction networks. Bioinformatics. 2005 Apr 15;21(8):1739-40. doi: 10.1093/bioinformatics/bti228. Epub 2004 Dec 21. PMID: 15613397.

• Trinh CT, Wlaschin A, Srienc F. Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl Microbiol Biotechnol. 2009;81(5):813-826. doi:10.1007/s00253-008-1770-1

• Bordbar A, Nagarajan H, Lewis NE, et al. Minimal metabolic pathway structure is consistent with associated biomolecular interactions. Mol Syst Biol. 2014;10(7):737. Published 2014 Jul 1. doi:10.15252/msb.20145243

• Song HS, Goldberg N, Mahajan A, Ramkrishna D. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming. Bioinformatics. 2017 Aug 1;33(15):2345-2353. doi: 10.1093/bioinformatics/btx171. PMID: 28369193.

Computational tools for working with flux balance analysis models:

• Heirendt, Laurent et al. “Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.” Nature protocols vol. 14,3 (2019): 639-702. doi:10.1038/s41596-018-0098-2