An objective function exploiting suboptimal solutions in metabolic networks

Background: Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results: We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion: Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network

Improved Network Performance via Antagonism: From Synthetic Rescues to Multi-drug Combinations

Recent research shows that a faulty or sub-optimally operating metabolic network can often be rescued by the targeted removal of enzyme-coding genes--the exact opposite of what traditional gene therapy would suggest. Predictions go as far as to assert that certain gene knockouts can restore the growth of otherwise nonviable gene-deficient cells. Many questions follow from this discovery: What are the underlying mechanisms? How generalizable is this effect? What are the potential applications? Here, I will approach these questions from the perspective of compensatory perturbations on networks. Relations will be drawn between such synthetic rescues and naturally occurring cascades of reaction inactivation, as well as their analogues in physical and other biological networks. I will specially discuss how rescue interactions can lead to the rational design of antagonistic drug combinations that select against resistance and how they can illuminate medical research on cancer, antibiotics, and metabolic diseases.Comment: Online Open "Problems and Paradigms" articl

A control-theoretic approach to dynamic optimization of metabolic networks

The characterization of general control principles that underpin metabolic dynamics is an important part of systems analysis in biology. It has been long argued that many biological regulatory mechanisms have evolved so as to optimize cellular adaptation in response to external stimuli. In this thesis we use an optimal control framework to solve dynamic optimization problems associated with metabolic dynamics. The analysis is based on a nonlinear control-ane model of a metabolic network with the enzyme concentrations as control inputs. We consider the optimization of time-dependent enzyme concentrations to activate an unbranched network and reach a prescribed metabolic ux. The solution accounts for time-resource optimality under constraints in the total enzymatic abundance. We identify a temporal pattern in the solution that is consistent with previous experimental and numerical observations. Our analysis suggests that this behaviour may appear in a broader class of networks than previously considered. In addition, we address the optimization of time-dependent enzyme expression rates for a metabolic network coupled with a model of enzyme dynamics. The formulation accounts for the transition between two metabolic steady states in networks with arbitrary stoichiometries and enzyme kinetics. We consider a nite horizon quadratic cost function that weighs the deviations of metabolites, enzymes and their expression rates from their target values, together with the time-derivative of the expression rates. The problem is recast as an iterative sequence of Linear Quadratic Tracking problems, and we derive conditions under which the iterations converge to a suboptimal solution of the original problem. Additionally, if constant metabolite concentrations are enforced, the nonlinear system can be written as a linear Dierential-Algebraic system. In the innite horizon case the problem can be recast as a standard Linear Quadratic Regulator problem for a lower-dimensional system, the solution of which is readily available

A control-theoretic approach to dynamic optimization of metabolic networks

The characterization of general control principles that underpin metabolic dynamics is an important part of systems analysis in biology. It has been long argued that many biological regulatory mechanisms have evolved so as to optimize cellular adaptation in response to external stimuli. In this thesis we use an optimal control framework to solve dynamic optimization problems associated with metabolic dynamics. The analysis is based on a nonlinear control-ane model of a metabolic network with the enzyme concentrations as control inputs. We consider the optimization of time-dependent enzyme concentrations to activate an unbranched network and reach a prescribed metabolic ux. The solution accounts for time-resource optimality under constraints in the total enzymatic abundance. We identify a temporal pattern in the solution that is consistent with previous experimental and numerical observations. Our analysis suggests that this behaviour may appear in a broader class of networks than previously considered. In addition, we address the optimization of time-dependent enzyme expression rates for a metabolic network coupled with a model of enzyme dynamics. The formulation accounts for the transition between two metabolic steady states in networks with arbitrary stoichiometries and enzyme kinetics. We consider a nite horizon quadratic cost function that weighs the deviations of metabolites, enzymes and their expression rates from their target values, together with the time-derivative of the expression rates. The problem is recast as an iterative sequence of Linear Quadratic Tracking problems, and we derive conditions under which the iterations converge to a suboptimal solution of the original problem. Additionally, if constant metabolite concentrations are enforced, the nonlinear system can be written as a linear Dierential-Algebraic system. In the innite horizon case the problem can be recast as a standard Linear Quadratic Regulator problem for a lower-dimensional system, the solution of which is readily available

Multicriteria global optimization for biocircuit design

One of the challenges in Synthetic Biology is to design circuits with increasing levels of complexity. While circuits in Biology are complex and subject to natural tradeoffs, most synthetic circuits are simple in terms of the number of regulatory regions, and have been designed to meet a single design criterion. In this contribution we introduce a multiobjective formulation for the design of biocircuits. We set up the basis for an advanced optimization tool for the modular and systematic design of biocircuits capable of handling high levels of complexity and multiple design criteria. Our methodology combines the efficiency of global Mixed Integer Nonlinear Programming solvers with multiobjective optimization techniques. Through a number of examples we show the capability of the method to generate non intuitive designs with a desired functionality setting up a priori the desired level of complexity. The presence of more than one competing objective provides a realistic design setting where every design solution represents a trade-off between different criteria. The tool can be useful to explore and identify different design principles for synthetic gene circuits

Global optimization in systems biology: stochastic methods and their applications

Mathematical optimization is at the core of many problems in systems biology: (1) as the underlying hypothesis for model development, (2) in model identification, or (3) in the computation of optimal stimulation procedures to synthetically achieve a desired biological behavior. These problems are usually formulated as nonlinear programing problems (NLPs) with dynamic and algebraic constraints. However the nonlinear and highly constrained nature of systems biology models, together with the usually large number of decision variables, can make their solution a daunting task, therefore calling for efficient and robust optimization techniques. Here, we present novel global optimization methods and software tools such as cooperative enhanced scatter search (eSS), AMIGO, or DOTcvpSB, and illustrate their possibilities in the context of modeling including model identification and stimulation design in systems biology.This work was supported by the Spanish MICINN project ”MultiSysBio” (ref. DPI2008-06880-C03-02), and by CSIC intramural project ”BioREDES” (ref. PIE-201170E018).Peer reviewe

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful