Understanding and controlling dynamics are at the core of major challenges in biology and ecology central to human and environmental health. With the increasing availability of data time-series in these fields and better comprehension of the fundamental biological mechanisms, building models is required to fully grasp these dynamics. The objective of Macbes is to apply and develop methodologies of control theory and computational biology to specific applications in biology and ecology: the ecologically friendly protection and management of ecosystems, such as agroecosystems, and the characterization and deciphering of mammalian cell responses to their environment, in particular the effect of network interactions and develop ments in synthetic biology. Macbes has privileged access to biological data generated by the partners within the Common Project Team which allows for the development of the most relevant models related to its applications.
Control theory provides answers to questions related to the need to measure the system’s variables, identify parameters, reconstruct non measured quantities of interest, regulate and control the system towards a desired state and optimize the yield of a given product. In computational biology, the tools of theoretical ecology and evolutionary biology provide answers on what a system will become.
The development of dynamical models representing mechanisms and interactions within our systems of interest is a first step in our approach. We develop models built in continuous ordinary differential equations, impulsive models, discrete models, or hybrid models, to better represent the variety of biological processes. In their diversity, these models are often built on representations of simplified biological processes, which yield systems that have particular structures that can be exploited: their variables are positive, some interactions can be modelled as mass transfers, they can be monotonic,…
Such models allow for analytical and numerical developments that help explain the dynamics and the functioning of biological processes. These models constitute the foundations on which we can apply the full toolbox of control theory.
The link of our models to data depends on the context.
On the one hand, we are at a turning point where the availability of “omics” and cell level data exceeds our capacity of interpretation, while on the other hand it may still be difficult to obtain reliable and useful data time series to understand ecosystem dynamics, though that could soon change too. Therefore, apprehending the complexity of these processes and interactions through this abundance of data or despite data scarcity, requires the construction of specific mathematical models with specific calibration approaches, that face the large uncertainties and variability that are intrinsic to biological systems.
In addition, to limit the impact of uncertainties and callibration errors on our results, we also develop models and control theoretic approaches relying on qualitatively described functions, through which generic answers can be sought that are valid over a wide range of situations and parameter values.
The research program is organized around four axes that make use of common tools from control theory and computational biology, with models built in continuous ordinary differential equations, impulsive models, discrete models, or hybrid models. Control theory provides answers to questions related to the need to measure the system’s variables, identify parameters, reconstruct non measured quantities of interest, regulate and control the system towards a desired state and optimize the yield of a given product. In computational biology, we use the tools of theoretical ecology and evolutionary biology to provide answers on what a system will become. The fours research axes of MACBES are:
- Network interactions for cell function and growth. Cells have evolved highly sophisticated intracellular communication pathways to enable their development and growth, under multiple environmental stresses and stimuli (growth factors, hormones, different types of drugs, temperature or light changes, etc.). In a modular view of a biological organism, each task is executed by a specific network, or module. These modules often interact with each other, one task triggering the next in a chain of events or cyclic phenomena: cascades of signaling networks, genetic-metabolic interactions, oscillatory behavior. One of the greatest challenges at the interface between biology and mathematics is to decipher and reproduce the complex behavior arising from the interconnection of two or more modules. The ability to reproduce the complexity of cellular responses will lead to a better capacity for regulation and balancing of factors towards healthy behaviors.
- Dynamics and control for synthetic biology. Synthetic biology aims at joining elements from both biology and engineering to construct cellular circuits that perform a desired function or induce a particular type of response. It is also a complementary approach to (traditional) molecular biology: newly creating and assembling synthetic cellular circuits from basic biological components (such as DNA, proteins, or metabolites) to form a “whole organism”, serves as a proof of principle towards understanding the mechanisms of biological networks. A fundamental question in synthetic biology is how to integrate the new circuit into the cell’s machinery, without upsetting the cellular resource allocation balance. To tackle this problem, understanding resource allocation in the cell and the interconnection of cellular oscillators is crucial.
- Modelling agro-ecological interactions. Plants experiment a wide range of biotic interactions. Some are beneficial to plant health, as in the case of pollinators or symbiotic organisms, whereas others are detrimental, as in the case of pathogens or herbivores. The dynamics and outcome of these interactions depend on the ecological conditions, including the phenotypes of the interacting species, their physiology and the abiotic environment in which the interactions take place. Our aim is to develop models relevant to several biotic interactions involving plants and other organisms, from the ecophysiological scale and the intimate interaction between plants and their partners, to the ecological interactions between populations and communities inhabiting crop fields.
- Design and control of managed ecosystems. In several contexts, such as bioreactors in industry or cropping systems in agriculture, it might be desirable to create an ecosystem that does not exist as is in nature. Putting together species that have mutualistic behaviors, whose synergy allows for the production of some desired output, or that protect one another, can enhance the functioning of the resulting ecosystem. Without going as far as designing an ecosystem de novo, it might also be necessary to take control actions to improve the functioning of an existing ecosystem or to restore a degraded ecosystem to a previous, desirable, state. The exploitation of natural or synthetic microbial communities for the accomplishment of processes of interest is being pursued in a vast range of scenarios, from established applications in the biotechnology and pharmaceutical industries, to innovative applications in medicine and environmental sciences. Larger scale managed ecosystems can simply be natural ecosystems into which one wants to re-introduce or maintain endangered species, but they can also be exploited ecosystems such as forests, agricultural fields, fish farms,… A special focus is put in MACBES on the development of pest/pathogen control methods in agroecosystems.